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0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" 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0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </div> </a></li> <li> <a 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width="24" data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" 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1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded 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8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2">Search syntax tips</a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 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Currently trying to develop more robust and flexible algorithms taking into account some brain dynamics.\\n\",\"date\":-62135596800,\"expirydate\":-62135596800,\"kind\":\"section\",\"lang\":\"en\",\"lastmod\":1674042515,\"objectID\":\"c7878e6e9da7120d418cfcc9dd9b85f4\",\"permalink\":\"https://LofNaDI.github.io/authors/benjamin/\",\"publishdate\":\"0001-01-01T00:00:00Z\",\"relpermalink\":\"/authors/benjamin/\",\"section\":\"authors\",\"summary\":\"I am a physicist interested in the intersection between artificial intelligence (AI) and neuroscience, specially in cognitive neuroscience. Currently trying to develop more robust and flexible algorithms taking into account some brain dynamics.\",\"tags\":null,\"title\":\"Benjamín Pascual Estrugo\",\"type\":\"authors\"},{\"authors\":[\"David Carrasco Peris\"],\"categories\":null,\"content\":\"I\\u0026rsquo;m technical engineer specialized in sensorization, home automation and artificial intelligence. My research interest includes the applications of Artificial Intelligence to improve learning algorithms.I\\u0026rsquo;m PhD candidate at the Lab of Natural and Designed Intelligence.\\n\",\"date\":-62135596800,\"expirydate\":-62135596800,\"kind\":\"section\",\"lang\":\"en\",\"lastmod\":1674045868,\"objectID\":\"5ba39124ed60e4a9ec212e2b8a7e4ca4\",\"permalink\":\"https://LofNaDI.github.io/authors/david/\",\"publishdate\":\"0001-01-01T00:00:00Z\",\"relpermalink\":\"/authors/david/\",\"section\":\"authors\",\"summary\":\"I\\u0026rsquo;m technical engineer specialized in sensorization, home automation and artificial intelligence. My research interest includes the applications of Artificial Intelligence to improve learning algorithms.I\\u0026rsquo;m PhD candidate at the Lab of Natural and Designed Intelligence.\",\"tags\":null,\"title\":\"David Carrasco Peris\",\"type\":\"authors\"},{\"authors\":[\"Ester Dionís Martí\"],\"categories\":null,\"content\":\"I\\u0026rsquo;m currently research assistant at the Lab of Natural and Designed Intelligence. My role inside the Lab is focus on clustering analysis and how to apply algorithms at chemical reactions in order to predict which catalyzer is the best for each case.\\nExperience Electrochemical Characterization Of Ceramic Electrodes And Membranes And Application To Photoelectrooxidation And Electrofiltration Processes (UPV). Technician Specialized at the Centre For Omic Sciences. Technological Centre of Nutrition and Health Technician at the Proteomics Unit. Centre for Genomic Regulation / Universitat Pompeu Fabra. Technician at the Proteomics Core Facility. Centro de Investigación Príncipe Felipe. Technician at the Department of Physical Chemistry. Universität Friedrich-Alexander Erlangen-Nürnberg. Technician at the Department of Analitical Chemistry. Universität Friedrich-Alexander-SIEMENS Erlangen-Nürnberg. \",\"date\":-62135596800,\"expirydate\":-62135596800,\"kind\":\"section\",\"lang\":\"en\",\"lastmod\":1634300404,\"objectID\":\"7e4a189ac6d33e1bd7283cf2c99c13ee\",\"permalink\":\"https://LofNaDI.github.io/authors/ester/\",\"publishdate\":\"0001-01-01T00:00:00Z\",\"relpermalink\":\"/authors/ester/\",\"section\":\"authors\",\"summary\":\"I\\u0026rsquo;m currently research assistant at the Lab of Natural and Designed Intelligence. My role inside the Lab is focus on clustering analysis and how to apply algorithms at chemical reactions in order to predict which catalyzer is the best for each case.\",\"tags\":null,\"title\":\"Ester Dionís Martí\",\"type\":\"authors\"},{\"authors\":[\"Juan García Méndez\"],\"categories\":null,\"content\":\"I\\u0026rsquo;m physicist. My interests include theoretical physics on one hand, especially quantum field theory and quantum materials, and artificial intelligence on the other.I\\u0026rsquo;m currently developing a deep neural network to predict neutrino trajectories in single-line events of the ANTARES telescope.\\n\",\"date\":-62135596800,\"expirydate\":-62135596800,\"kind\":\"section\",\"lang\":\"en\",\"lastmod\":1674044801,\"objectID\":\"7195bffbc9efc2358393b2638532603f\",\"permalink\":\"https://LofNaDI.github.io/authors/juan/\",\"publishdate\":\"0001-01-01T00:00:00Z\",\"relpermalink\":\"/authors/juan/\",\"section\":\"authors\",\"summary\":\"I\\u0026rsquo;m physicist. My interests include theoretical physics on one hand, especially quantum field theory and quantum materials, and artificial intelligence on the other.I\\u0026rsquo;m currently developing a deep neural network to predict neutrino trajectories in single-line events of the ANTARES telescope.\",\"tags\":null,\"title\":\"Juan García Méndez\",\"type\":\"authors\"},{\"authors\":[\"admin\"],\"categories\":null,\"content\":\"Our group is interested in identifying physiological mechanisms of brain function and dysfunction, in particular, the neural substrates of learning and intelligence. For this, we build neural network and reinforcement learning models.\\nWhile keeping a strong interest in computational neuroscience, new endeavors seek to adapt computationally efficient mechanisms of the brain into AI architectures (such as deep neural networks) aiming for more powerful learning algorithms.\\nBefore leading the Lab of Natural and Designed Intelligence at UPV, I was an Associate Research Scientist at Yale, where I was part of the Decision Lab (led by my current collaborator Ifat Levy). As a Postdoctoral Research Scientist, I collaborated with Nancy Kopell (Boston University), Thilo Womelsdorf (Vanderbilt University), and Xiao-Jing Wang (New York University). I earned my PhD in Computational Neuroscience at the Neuroscience Institute (UMH-CSIC), under the guidance of Albert Compte (IDIBAPS – Hospital Clinic).\\n\",\"date\":-62135596800,\"expirydate\":-62135596800,\"kind\":\"section\",\"lang\":\"en\",\"lastmod\":1634300589,\"objectID\":\"2525497d367e79493fd32b198b28f040\",\"permalink\":\"https://LofNaDI.github.io/authors/admin/\",\"publishdate\":\"0001-01-01T00:00:00Z\",\"relpermalink\":\"/authors/admin/\",\"section\":\"authors\",\"summary\":\"Our group is interested in identifying physiological mechanisms of brain function and dysfunction, in particular, the neural substrates of learning and intelligence. For this, we build neural network and reinforcement learning models.\",\"tags\":null,\"title\":\"Salva Ardid, PhD\",\"type\":\"authors\"},{\"authors\":[\"Santiago Galella Toledo\"],\"categories\":null,\"content\":\"I\\u0026rsquo;m currently a research assistant at the Lab of Natural and Designed intelligence, where I study how to incorporate concepts from biological neural networks into deep learning models. My research interests lie in the intersection between neuroscience and artificial intelligence. I\\u0026rsquo;m particularly interested in recurrent neural networks and learning algorithms. Currently, I\\u0026rsquo;m doing research in activation functions, population dimensionality and stopping criteria in deep learning\\n\",\"date\":-62135596800,\"expirydate\":-62135596800,\"kind\":\"section\",\"lang\":\"en\",\"lastmod\":1634300404,\"objectID\":\"d1c18aa97c9af60067a2e2147da359f1\",\"permalink\":\"https://LofNaDI.github.io/authors/santiago/\",\"publishdate\":\"0001-01-01T00:00:00Z\",\"relpermalink\":\"/authors/santiago/\",\"section\":\"authors\",\"summary\":\"I\\u0026rsquo;m currently a research assistant at the Lab of Natural and Designed intelligence, where I study how to incorporate concepts from biological neural networks into deep learning models. My research interests lie in the intersection between neuroscience and artificial intelligence.\",\"tags\":null,\"title\":\"Santiago Galella\",\"type\":\"authors\"},{\"authors\":[\"Silvia\"],\"categories\":null,\"content\":\"She is a specialized biologist in evaluating and monitoring marine and coastal ecosystems. Her research interests include the applications of Artificial Intelligence over marine ecosystem conservation, monitoring, and assessment.\\nShe is a Ph.D. candidate at the Lab of Natural and Designed Intelligence. Her thesis is focused on developing neural circuits that link the prefrontal cortex with the striatum. These two brain areas seem implicated in the decision-making process and learning development, so their joining is essential in understanding how these processes work. As a starting point, they have taken task-switching related studies that link experimental data with animal behavior to create a neural circuit capable of representing the triggered neural dynamics as projections of different decisions.\\nIn this framework, they aim to test three different scenarios. One will be considered the healthy condition, and the other two scenarios will represent illness conditions promoted by different dopamine concentrations (high and low levels). Previous works on this topic suggest that high dopamine levels are related to Parkinson\\u0026rsquo;s and low dopamine levels with schizophrenia.\\nSo, in the last year of her thesis work, she will focus on modifying those models to check how these different dopamine concentrations affect the \\u0026ldquo;normal\\u0026rdquo; circuit dynamics\\n\",\"date\":-62135596800,\"expirydate\":-62135596800,\"kind\":\"section\",\"lang\":\"en\",\"lastmod\":1674735623,\"objectID\":\"0f12d11c56413a6f9905f00ad2ba69b0\",\"permalink\":\"https://LofNaDI.github.io/authors/silvia/\",\"publishdate\":\"0001-01-01T00:00:00Z\",\"relpermalink\":\"/authors/silvia/\",\"section\":\"authors\",\"summary\":\"She is a specialized biologist in evaluating and monitoring marine and coastal ecosystems. Her research interests include the applications of Artificial Intelligence over marine ecosystem conservation, monitoring, and assessment.\\nShe is a Ph.\",\"tags\":null,\"title\":\"Silvia Vilariño León\",\"type\":\"authors\"},{\"authors\":[\"J.H Lee\",\"Y. Choe\",\"S. Ardid\",\"R. Abbasi-Asl\",\"M. McCarthy\",\"B. Hu\"],\"categories\":null,\"content\":\"\",\"date\":1674518400,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1675423927,\"objectID\":\"412ae213c66969fd560e9531788bf4fb\",\"permalink\":\"https://LofNaDI.github.io/publication/lee-2023-functional/\",\"publishdate\":\"2023-01-24T00:00:00Z\",\"relpermalink\":\"/publication/lee-2023-functional/\",\"section\":\"publication\",\"summary\":\"Fundamental principles underlying higher-order cognitive functions remain elusive, but recent breakthroughs in neurophysiology and deep learning offer new perspectives. First, experimental studies have uncovered neural circuit motifs consisting of various neuron types; see Brain Initiative Cell Census Network (https://www.nature.com/collections/cicghheddj). For example, inhibitory neuron types expressing exclusive genes have specific targets and distinct functions (Pfeffer et al., 2013). Furthermore, diverse neuron types in cortex and their connectomes were identified in cortical columns (Jiang et al., 2015); see also Barth et al. (2016) for a debate on neuron types. Second, artificial neural networks were originally inspired by structures of the brain (McCulloch and Pitts, 1943) and could be trained to perform complex functions similar to human perception/cognition by deep learning (DL) (Lecun et al., 2015).\",\"tags\":null,\"title\":\"Functional microcircuits in the brain and in artificial intelligent systems\",\"type\":\"publication\"},{\"authors\":[\"A. Albert\",\"S. Alves Garre\",\"M. André\",\"M. Ardid\",\"S. Ardid\"],\"categories\":null,\"content\":\"\",\"date\":1673568000,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674562075,\"objectID\":\"0043adebf6e907d4e3a9c2d6932f0463\",\"permalink\":\"https://LofNaDI.github.io/publication/albert-2023-limits/\",\"publishdate\":\"2023-01-13T00:00:00Z\",\"relpermalink\":\"/publication/albert-2023-limits/\",\"section\":\"publication\",\"summary\":\"In this work, a search for nuclearites of strange quark matter by using nine years of ANTARES data taken in the period 2009–2017 is presented. The passage through matter of these particles is simulated taking into account a detailed description of the detector response to nuclearites and of the data acquisition conditions. A down-going flux of cosmic nuclearites with Galactic velocities (β = 10^-3) was considered for this study. The mass threshold for detecting these particles at the detector level is 4 × 10^13 GeV/c^2. Upper limits on the nuclearite flux for masses up to 1017 GeV/c^2 at the level of ∼ 5 × 10^17 cm^-2 s^-1 sr^-1 are obtained. These are the first upper limits on nuclearites established with a neutrino telescope and the most stringent ever set for Galactic velocities.\",\"tags\":null,\"title\":\"Limits on the nuclearite flux using the ANTARES neutrino telescope\",\"type\":\"publication\"},{\"authors\":[\"J. Hass\",\"S. Ardid\",\"J. Sherfey\",\"N. Kopell\"],\"categories\":null,\"content\":\"\",\"date\":1651795200,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674562075,\"objectID\":\"2f3f5efda9c5c523c31af71f984606d1\",\"permalink\":\"https://LofNaDI.github.io/publication/hass-2022-constraints/\",\"publishdate\":\"2022-05-06T00:00:00Z\",\"relpermalink\":\"/publication/hass-2022-constraints/\",\"section\":\"publication\",\"summary\":\"Persistent activity, the maintenance of neural activation over short periods of time in cortical networks, is widely thought to underlie the cognitive function of working memory. A large body of modeling studies has reproduced this kind of activity using cell assemblies with strengthened synaptic connections. However, almost all of these studies have considered persistent activity within networks with homogeneous neurons and synapses, making it difficult to judge the validity of such model results for cortical dynamics, which is based on highly heterogeneous neurons. Here, we consider persistent activity in a detailed, strongly data-driven network model of the prefrontal cortex with heterogeneous neuron and synapse parameters. Surprisingly, persistent activity could not be reproduced in this model without incorporating further constraints. We identified three factors that prevent successful persistent activity: heterogeneity in the cell parameters of interneurons, heterogeneity in the parameters of short-term synaptic plasticity and heterogeneity in the synaptic weights. We also discovered a general dynamic mechanism that prevents persistent activity in the presence of heterogeneities, namely a gradual drop-out of cell assembly neurons out of a bistable regime as input variability increases. Based on this mechanism, we found that persistent activity is recovered if heterogeneity is compensated, e.g., by a homeostatic plasticity mechanism. Cell assemblies shaped in this way may be potentially targeted by distinct inputs or become more responsive to specific tuning or spectral properties. Finally, we show that persistent activity in the model is robust against external noise, but the compensation of heterogeneities may prevent the dynamic generation of intrinsic in vivo-like irregular activity. These results may help informing the ongoing debate about the neural basis of working memory.\",\"tags\":null,\"title\":\"Constraints on persistent activity in a biologically detailed network model of the prefrontal cortex with heterogeneities\",\"type\":\"publication\"},{\"authors\":[\"S. Aeillo\",\"A. Albert\",\"M. Alshamsi\",\"S. Alves Garre\",\"Z. Aly\",\"A. Ambronose\"],\"categories\":null,\"content\":\"\",\"date\":1646784000,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674562075,\"objectID\":\"687e04119c36bb0e311f855ec3f886e7\",\"permalink\":\"https://LofNaDI.github.io/publication/aiello-2022-combined/\",\"publishdate\":\"2022-03-09T00:00:00Z\",\"relpermalink\":\"/publication/aiello-2022-combined/\",\"section\":\"publication\",\"summary\":\"This article presents the potential of a combined analysis of the JUNO and KM3NeT/ORCA experiments to determine the neutrino mass ordering. This combination is particularly interesting as it significantly boosts the potential of either detector, beyond simply adding their neutrino mass ordering sensitivities, by removing a degeneracy in the determination of ∆𝑚231 between the two experiments when assuming the wrong ordering. The study is based on the latest projected performances for JUNO, and on simulation tools using a full Monte Carlo approach to the KM3NeT/ORCA response with a careful assessment of its energy systematics. From this analysis, a 5σ determination of the neutrino mass ordering is expected after 6 years of joint data taking for any value of the oscillation parameters. This sensitivity would be achieved after only 2 years of joint data taking assuming the current global best-fit values for those parameters for normal ordering.\",\"tags\":null,\"title\":\"Combined sensitivity of JUNO and KM3NeT/ORCA to the neutrino mass ordering\",\"type\":\"publication\"},{\"authors\":[\"S. Aeillo\",\"A. Albert\",\"S. Alves Garre\",\"Z. Aly\",\"A. Ambronose\"],\"categories\":null,\"content\":\"\",\"date\":1641859200,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674562075,\"objectID\":\"a82741bf677392f93ff7fe3a4fe7bb79\",\"permalink\":\"https://LofNaDI.github.io/publication/aiello-2022/\",\"publishdate\":\"2022-01-11T00:00:00Z\",\"relpermalink\":\"/publication/aiello-2022/\",\"section\":\"publication\",\"summary\":\"The next generation of water Cherenkov neutrino telescopes in the Mediterranean Sea are under construction offshore France (KM3NeT/ORCA) and Sicily (KM3NeT/ARCA). The KM3NeT/ORCA detector features an energy detection threshold which allows to collect atmospheric neutrinos to study flavour oscillation. This paper reports the KM3NeT/ORCA sensitivity to this phenomenon. The event reconstruction, selection and classification are described. The sensitivity to determine the neutrino mass ordering was evaluated and found to be 4.4σ if the true ordering is normal and 2.3σ if inverted, after 3 years of data taking. The precision to measure Δ𝑚232 and 𝜃23 were also estimated and found to be 85.10−6 eV2 and (+1.9−3.1)∘ for normal neutrino mass ordering and, 75.10−6 eV2 and (+2.0−7.0)∘ for inverted ordering. Finally, a unitarity test of the leptonic mixing matrix by measuring the rate of tau neutrinos is described. Three years of data taking were found to be sufficient to exclude event rate variations larger than 20% at 3σ level.\",\"tags\":null,\"title\":\"Determining the neutrino mass ordering and oscillation parameters with KM3NeT/ORCA\",\"type\":\"publication\"},{\"authors\":[\"J. García-Méndez\",\"N. Geißelbrecht\",\"T. Eberl\",\"M. Ardid\",\"S. Ardid\"],\"categories\":null,\"content\":\"\",\"date\":1631836800,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1675423927,\"objectID\":\"b4afebc8bdcac9a58a8cf1fb18f35cb0\",\"permalink\":\"https://LofNaDI.github.io/publication/garciam-2021-deep/\",\"publishdate\":\"2021-09-17T00:00:00Z\",\"relpermalink\":\"/publication/garciam-2021-deep/\",\"section\":\"publication\",\"summary\":\"ANTARES is currently the largest undersea neutrino telescope, located in the Mediterranean Sea and taking data since 2007. It consists of a 3D array of photo sensors, instrumenting about 10Mt of seawater to detect Cherenkov light induced by secondary particles from neutrino interactions. The event reconstruction and background discrimination is challenging and machine-learning techniques are explored to improve the performance. In this contribution, two case studies using deep convolutional neural networks are presented. In the first one, this approach is used to improve the direction reconstruction of low-energy single-line events, for which the reconstruction of the azimuth angle of the incoming neutrino is particularly difficult. We observe a promising improvement in resolution over classical reconstruction techniques and expect to at least double our sensitivity in the low-energy range, important for dark matter searches. The second study employs deep learning to reconstruct the visible energy of neutrino interactions of all flavors and for the multi-line setup of the full detector.\",\"tags\":null,\"title\":\"Deep learning reconstruction in ANTARES\",\"type\":\"publication\"},{\"authors\":[\"A. Albert\",\"S. Alves Garre\",\"M. André\",\"M. Anghinolfi\",\"G. Anton\",\"M. Ardid\",\"S. Ardid\"],\"categories\":null,\"content\":\"\",\"date\":1622505600,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"4944f22cf1028b3b50271b5e6e883bb6\",\"permalink\":\"https://LofNaDI.github.io/publication/albert-2022-search/\",\"publishdate\":\"2021-06-01T00:00:00Z\",\"relpermalink\":\"/publication/albert-2022-search/\",\"section\":\"publication\",\"summary\":\"This work presents a new search for magnetic monopoles using data taken with the ANTARES neutrino telescope over a period of 10 years (January 2008 to December 2017). Compared to previous ANTARES searches, this analysis uses a run-by-run simulation strategy, with a larger exposure as well as a new simulation of magnetic monopoles taking into account the Kasama, Yang and Goldhaber model for their interaction cross-section with matter. No signal compatible with the passage of relativistic magnetic monopoles is observed, and upper limits on the flux of magnetic monopoles with β=v/c≥0.55, are presented. For ultra-relativistic magnetic monopoles the flux limit is ∼7× 10^−18 cm^−2 s^−1 sr− 1.\",\"tags\":null,\"title\":\"Search for Magnetic Monopoles with ten years of the ANTARES neutrino telescope\",\"type\":\"publication\"},{\"authors\":[\"S. Aeillo\",\"A. Albert\",\"S. Alves Garre\",\"Z. Aly\",\"A. Ambronose\"],\"categories\":null,\"content\":\"\",\"date\":1621814400,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674562075,\"objectID\":\"c3cd77b60d5dab3e74dff2c1c46de79d\",\"permalink\":\"https://LofNaDI.github.io/publication/aiello-2021/\",\"publishdate\":\"2021-05-24T00:00:00Z\",\"relpermalink\":\"/publication/aiello-2021/\",\"section\":\"publication\",\"summary\":\"The KM3NeT research infrastructure is under construction in the Mediterranean Sea. It consists of two water Cherenkov neutrino detectors, ARCA and ORCA, aimed at neutrino astrophysics and oscillation research, respectively. Instrumenting a large volume of sea water with ∼6200 optical modules comprising a total of ∼200,000 photomultiplier tubes, KM3NeT will achieve sensitivity to ∼10 MeV neutrinos from Galactic and near-Galactic core-collapse supernovae through the observation of coincident hits in photomultipliers above the background. In this paper, the sensitivity of KM3NeT to a supernova explosion is estimated from detailed analyses of background data from the first KM3NeT detection units and simulations of the neutrino signal. The KM3NeT observational horizon (for a 5σ discovery) covers essentially the Milky-Way and for the most optimistic model, extends to the Small Magellanic Cloud (∼60 kpc). Detailed studies of the time profile of the neutrino signal allow assessment of the KM3NeT capability to determine the arrival time of the neutrino burst with a few milliseconds precision for sources up to 5–8 kpc away, and detecting the peculiar signature of the standing accretion shock instability if the core-collapse supernova explosion happens closer than 3–5 kpc, depending on the progenitor mass. KM3NeT’s capability to measure the neutrino flux spectral parameters is also presented.\",\"tags\":null,\"title\":\"The KM3NeT potential for the next core-collapse supernova observation with neutrinos\",\"type\":\"publication\"},{\"authors\":[\"S. Aeillo\",\"A. Albert\",\"M. Alshamsi\",\"S. Alves Garre\",\"Z. Aly\",\"A. Ambronose\"],\"categories\":null,\"content\":\"\",\"date\":1618099200,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674562075,\"objectID\":\"42a875c6938170be0f6e581169036ee9\",\"permalink\":\"https://LofNaDI.github.io/publication/aiello-2022-implementation/\",\"publishdate\":\"2021-04-11T00:00:00Z\",\"relpermalink\":\"/publication/aiello-2022-implementation/\",\"section\":\"publication\",\"summary\":\"The KM3NeT research infrastructure is unconstruction in the Mediterranean Sea. KM3NeT will study atmospheric and astrophysical neutrinos with two multi-purpose neutrino detectors, ARCA and ORCA, primarily aimed at GeV–PeV neutrinos. Thanks to the multi-photomultiplier tube design of the digital optical modules, KM3NeT is capable of detecting the neutrino burst from a Galactic or near-Galactic core-collapse supernova. This potential is already exploitable with the first detection units deployed in the sea. This paper describes the real-time implementation of the supernova neutrino search, operating on the two KM3NeT detectors since the first months of 2019. A quasi-online astronomy analysis is introduced to study the time profile of the detected neutrinos for especially significant events. The mechanism of generation and distribution of alerts, as well as the integration into the SNEWS and SNEWS 2.0 global alert systems, are described. The approach for the follow-up of external alerts with a search for a neutrino excess in the archival data is defined. Finally, an overview of the current detector capabilities and a report after the first two years of operation are given\",\"tags\":null,\"title\":\"Implementation and first results of the KM3NeT real-time core-collapse supernova neutrino search\",\"type\":\"publication\"},{\"authors\":[\"S. Aiello\",\"A. Albert\",\"M. Alshamsi\",\"S. Alve Garre\",\"Z. Aly\"],\"categories\":null,\"content\":\"\",\"date\":1603238400,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"2e7eaa9cb20f1c5d215f1720ad00bc0c\",\"permalink\":\"https://LofNaDI.github.io/publication/km3net-2021/\",\"publishdate\":\"2020-10-21T00:00:00Z\",\"relpermalink\":\"/publication/km3net-2021/\",\"section\":\"publication\",\"summary\":\"KM3NeT/ORCA is a next-generation neutrino telescope optimised for atmospheric neutrino oscillations studies. In this paper, the sensitivity of ORCA to the presence of a light sterile neutrino in a 3+1 model is presented. After three years of data taking, ORCA will be able to probe the active-sterile mixing angles θ 14, θ 24, θ 34 and the effective angle θ μe, over a broad range of mass squared difference Δ𝑚^2-41 ∼ [10^−5, 10] eV2, allowing to test the eV-mass sterile neutrino hypothesis as the origin of short baseline anomalies, as well as probing the hypothesis of a very light sterile neutrino, not yet constrained by cosmology. ORCA will be able to explore a relevant fraction of the parameter space not yet reached by present measurements.\",\"tags\":null,\"title\":\"Sensitivity to light sterile neutrino mixing parameters with KM3NeT/ORCA\",\"type\":\"publication\"},{\"authors\":[\"J. S. Sherfey\",\"S. Ardid\",\"E. K. Miller\",\"M. E. Hasselmo\",\"N. J. Kopell\"],\"categories\":null,\"content\":\"\",\"date\":1598918400,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"3217c7ed4a57f95ad4d749f61fcf3a34\",\"permalink\":\"https://LofNaDI.github.io/publication/sherfey-2020/\",\"publishdate\":\"2020-02-13T16:52:11.360094Z\",\"relpermalink\":\"/publication/sherfey-2020/\",\"section\":\"publication\",\"summary\":\"Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma-frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.\",\"tags\":null,\"title\":\"Prefrontal oscillations modulate the propagation of neuronal activity required for working memory\",\"type\":\"publication\"},{\"authors\":[\"S. Ardid\",\"J. S. Sherfey\",\"M. M. McCarthy\",\"J. Hass\",\"B. R. Pittman-Polletta\",\"N. Kopell\"],\"categories\":null,\"content\":\"\",\"date\":1554076800,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"15a28272a6ab51f64df4ad946bd3fd67\",\"permalink\":\"https://LofNaDI.github.io/publication/ardid-2019/\",\"publishdate\":\"2020-02-13T16:52:11.360094Z\",\"relpermalink\":\"/publication/ardid-2019/\",\"section\":\"publication\",\"summary\":\"Classical accounts of biased competition require an input bias to resolve the competition between neuronal ensembles driving downstream processing. However, flexible and reliable selection of behaviorally relevant ensembles can occur with unbiased stimulation: striatal D1 and D2 spiny projection neurons (SPNs) receive balanced cortical input, yet their activity determines the choice between GO and NO-GO pathways in the basal ganglia. We here present a corticostriatal model identifying three mechanisms that rely on physiological asymmetries to effect rate- and time-coded biased competition in the presence of balanced inputs. First, tonic input strength determines which one of the two SPN phenotypes exhibits a higher mean firing rate. Second, low-strength oscillatory inputs induce higher firing rate in D2 SPNs but higher coherence between D1 SPNs. Third, high-strength inputs oscillating at distinct frequencies can preferentially activate D1 or D2 SPN populations. Of these mechanisms, only the latter accommodates observed rhythmic activity supporting rule-based decision making in prefrontal cortex. [[Code]](https://github.com/LofNaDI/unbiasedCompetition)\",\"tags\":null,\"title\":\"Biased competition in the absence of input bias revealed through corticostriatal computation\",\"type\":\"publication\"},{\"authors\":[\"M. Oemisch\",\"S. Westendorff\",\"M. Azimi\",\"S. A. Hassani\",\"S. Ardid\",\"P. Tiesinga\",\"T. Womelsdorf\"],\"categories\":null,\"content\":\"\",\"date\":1546300800,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"802d9a1d6d8be192c0000606b55a3685\",\"permalink\":\"https://LofNaDI.github.io/publication/oemisch-2019/\",\"publishdate\":\"2020-02-13T16:52:11.360541Z\",\"relpermalink\":\"/publication/oemisch-2019/\",\"section\":\"publication\",\"summary\":\"To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Here, we report a potential solution: neurons in four recorded areas of the anterior fronto-striatal networks encode prediction errors that are specific to feature values of different dimensions of attended multidimensional stimuli. The most ubiquitous prediction error occurred for the reward-relevant dimension. Feature-specific prediction error signals a) emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anterior cingulate cortex and later in dorsolateral prefrontal cortex, caudate and ventral striatum, and c) contribute to feature-based stimulus selection after learning. Thus, a widely-distributed feature-specific eligibility trace may be used to update synaptic weights for improved feature-based attention.\",\"tags\":null,\"title\":\"Feature-specific prediction errors and surprise across macaque fronto-striatal circuits\",\"type\":\"publication\"},{\"authors\":[\"J. S. Sherfey\",\"S. Ardid\",\"J. Hass\",\"M. E. Hasselmo\",\"N. J. Kopell\"],\"categories\":null,\"content\":\"\",\"date\":1533081600,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"39da3a5505b9385f28c786d299738e1d\",\"permalink\":\"https://LofNaDI.github.io/publication/sherfey-2018-a/\",\"publishdate\":\"2020-02-13T16:52:11.360827Z\",\"relpermalink\":\"/publication/sherfey-2018-a/\",\"section\":\"publication\",\"summary\":\"Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.\",\"tags\":null,\"title\":\"Flexible resonance in prefrontal networks with strong feedback inhibition\",\"type\":\"publication\"},{\"authors\":[\"J. S. Sherfey\",\"A. E. Soplata\",\"S. Ardid\",\"E. A. Roberts\",\"D. A. Stanley\",\"B. R. Pittman-Polletta\",\"N. J. Kopell\"],\"categories\":null,\"content\":\"\",\"date\":1514764800,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"2873b3de8cc6e67b3e8af67b198b857f\",\"permalink\":\"https://LofNaDI.github.io/publication/sherfey-2018-b/\",\"publishdate\":\"2020-02-13T16:52:11.361052Z\",\"relpermalink\":\"/publication/sherfey-2018-b/\",\"section\":\"publication\",\"summary\":\"DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. DynaSim is freely available at http://dynasimtoolbox.org. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.\",\"tags\":null,\"title\":\"DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation\",\"type\":\"publication\"},{\"authors\":[\"S. A. Hassani\",\"M. Oemisch\",\"M. Balcarras\",\"S. Westendorff\",\"S. Ardid\",\"M. A. van der Meer\",\"P. Tiesinga\",\"T. Womelsdorf\"],\"categories\":null,\"content\":\"\",\"date\":1483228800,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"8074a97c5a0b4c5d5a5bb9b2790aa80f\",\"permalink\":\"https://LofNaDI.github.io/publication/hassani-2017/\",\"publishdate\":\"2020-02-13T16:52:11.361269Z\",\"relpermalink\":\"/publication/hassani-2017/\",\"section\":\"publication\",\"summary\":\"Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.\",\"tags\":null,\"title\":\"A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque\",\"type\":\"publication\"},{\"authors\":[\"M. Balcarras\",\"S. Ardid\",\"D. Kaping\",\"S. Everling\",\"T. Womelsdorf\"],\"categories\":null,\"content\":\"\",\"date\":1454284800,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"4e2f135763ebd90fe4ff02e399833e65\",\"permalink\":\"https://LofNaDI.github.io/publication/balcarras-2016/\",\"publishdate\":\"2020-02-13T16:52:11.361503Z\",\"relpermalink\":\"/publication/balcarras-2016/\",\"section\":\"publication\",\"summary\":\"Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.\",\"tags\":null,\"title\":\"Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness\",\"type\":\"publication\"},{\"authors\":[\"D. Gomez-Cabrero\",\"S. Ardid\",\"M. Cano-Colino\",\"J. Tegnér\",\"A. Compte\"],\"categories\":null,\"content\":\"\",\"date\":1451606400,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674734214,\"objectID\":\"81d87c60ac3dcfaf1fc4484f68e7dde1\",\"permalink\":\"https://LofNaDI.github.io/publication/gomez-cabrero-2016/\",\"publishdate\":\"2020-02-13T16:52:11.361757Z\",\"relpermalink\":\"/publication/gomez-cabrero-2016/\",\"section\":\"publication\",\"summary\":\"\",\"tags\":null,\"title\":\"Neuroswarm: a methodology to explore the constraints that function imposes on simulation parameters in large-scale networks of biological neurons\",\"type\":\"publication\"},{\"authors\":[\"C. Shen\",\"S. Ardid\",\"D. Kaping\",\"S. Westendorff\",\"S. Everling\",\"T. Womelsdorf\"],\"categories\":null,\"content\":\"\",\"date\":1438387200,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"9080c22c3a0897201e0583e7cb364788\",\"permalink\":\"https://LofNaDI.github.io/publication/shen-2015/\",\"publishdate\":\"2020-02-13T16:52:11.362344Z\",\"relpermalink\":\"/publication/shen-2015/\",\"section\":\"publication\",\"summary\":\"Errors indicate the need to adjust attention for improved future performance. Detecting errors is thus a fundamental step to adjust and control attention. These functions have been associated with the dorsal anterior cingulate cortex (dACC), predicting that dACC cells should track the specific processing states giving rise to errors in order to identify which processing aspects need readjustment. Here, we tested this prediction by recording cells in the dACC and lateral prefrontal cortex (latPFC) of macaques performing an attention task that dissociated 3 processing stages. We found that, across prefrontal subareas, the dACC contained the largest cell populations encoding errors indicating (1) failures of inhibitory control of the attentional focus, (2) failures to prevent bottom-up distraction, and (3) lapses when implementing a choice. Error-locked firing in the dACC showed the earliest latencies across the PFC, emerged earlier than reward omission signals, and involved a significant proportion of putative inhibitory interneurons. Moreover, early onset error-locked response enhancement in the dACC was followed by transient prefrontal-cingulate inhibition, possibly reflecting active disengagement from task processing. These results suggest a functional specialization of the dACC to track and identify the actual processes that give rise to erroneous task outcomes, emphasizing its role to control attentional performance.\",\"tags\":null,\"title\":\"Anterior Cingulate Cortex Cells Identify Process-Specific Errors of Attentional Control Prior to Transient Prefrontal-Cingulate Inhibition\",\"type\":\"publication\"},{\"authors\":[\"S. Ardid\",\"M. Vinck\",\"D. Kaping\",\"S. Marquez\",\"S. Everling\",\"T. Womelsdorf\"],\"categories\":null,\"content\":\"\",\"date\":1422748800,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"e6fc9270dcc966424396973881743a86\",\"permalink\":\"https://LofNaDI.github.io/publication/ardid-2015/\",\"publishdate\":\"2020-02-13T16:52:11.362004Z\",\"relpermalink\":\"/publication/ardid-2015/\",\"section\":\"publication\",\"summary\":\"Microcircuits are composed of multiple cell classes that likely serve unique circuit operations. But how cell classes map onto circuit functions is largely unknown, particularly for primate prefrontal cortex during actual goal-directed behavior. One difficulty in this quest is to reliably distinguish cell classes in extracellular recordings of action potentials. Here we surmount this issue and report that spike shape and neural firing variability provide reliable markers to segregate seven functional classes of prefrontal cells in macaques engaged in an attention task. We delineate an unbiased clustering protocol that identifies four broad spiking (BS) putative pyramidal cell classes and three narrow spiking (NS) putative inhibitory cell classes dissociated by how sparse, bursty, or regular they fire. We speculate that these functional classes map onto canonical circuit functions. First, two BS classes show sparse, bursty firing, and phase synchronize their spiking to 3-7 Hz (theta) and 12-20 Hz (beta) frequency bands of the local field potential (LFP). These properties make cells flexibly responsive to network activation at varying frequencies. Second, one NS and two BS cell classes show regular firing and higher rate with only marginal synchronization preference. These properties are akin to setting tonically the excitation and inhibition balance. Finally, two NS classes fired irregularly and synchronized to either theta or beta LFP fluctuations, tuning them potentially to frequency-specific subnetworks. These results suggest that a limited set of functional cell classes emerges in macaque prefrontal cortex (PFC) during attentional engagement to not only represent information, but to subserve basic circuit operations. [[Code: Clustering Analysis]](https://github.com/LofNaDI/clusteringAnalysis.git) and [[Code: Waveform Analysis]](https://github.com/LofNaDI/waveformAnalysis.git)\",\"tags\":null,\"title\":\"Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex\",\"type\":\"publication\"},{\"authors\":[\"T. Womelsdorf\",\"S. Ardid\",\"S. Everling\",\"T. A. Valiante\"],\"categories\":null,\"content\":\"\",\"date\":1414800000,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"2b274135bc4b3e8fa639588f2ce72225\",\"permalink\":\"https://LofNaDI.github.io/publication/womelsdorf-2014/\",\"publishdate\":\"2020-02-13T16:52:11.362603Z\",\"relpermalink\":\"/publication/womelsdorf-2014/\",\"section\":\"publication\",\"summary\":\"It is widely held that single cells in anterior cingulate and lateral prefrontal cortex (ACC/PFC) coordinate their activity during attentional processes, although cellular activity that may underlie such coordination across ACC/PFC has not been identified. We thus recorded cells in five ACC/PFC subfields of macaques engaged in a selective attention task, characterized those spiking events that indexed attention, and identified how spiking of distinct cell populations synchronized between brain areas. We found that cells in ACC/PFC increased the firing of brief 200 Hz spike bursts when subjects shifted attention and engaged in selective visual processing. In contrast to nonburst spikes, burst spikes synchronized over large distances to local field potentials at narrow beta (12-20 Hz) and at gamma (50-75 Hz) frequencies. Long-range burst synchronization was anatomically specific, functionally connecting those subfields in area 24 (ACC) and area 46 (PFC) that are key players of attentional control. By splitting cells into putative excitatory (pE) and inhibitory (pI) cells by their broad and narrow spikes, we identified that bursts of pI cells preceded and that bursts of pE cells followed in time periods of maximal beta coherent network activity. In contrast, gamma bursts were transient impulses with equal timing across cell classes. These findings suggest that processes underlying burst firing and burst synchronization are candidate mechanisms to coordinate attention information across brain areas. We speculate that distinct burst-firing motifs realize beta and gamma synchrony to trigger versus maintain functional network states during goal-directed behavior.\",\"tags\":null,\"title\":\"Burst firing synchronizes prefrontal and anterior cingulate cortex during attentional control\",\"type\":\"publication\"},{\"authors\":[\"S. Ardid\",\"X.-J. Wang\"],\"categories\":null,\"content\":\"\",\"date\":1388534400,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"102e0c2e3d2bc489df38f73e774aedef\",\"permalink\":\"https://LofNaDI.github.io/publication/ardid-2014-b/\",\"publishdate\":\"2020-02-13T16:52:11.3631Z\",\"relpermalink\":\"/publication/ardid-2014-b/\",\"section\":\"publication\",\"summary\":\"\",\"tags\":null,\"title\":\"The “tweaking principle” for task switching\",\"type\":\"publication\"},{\"authors\":[\"S. Ardid\",\"M. Balcarras\",\"T. Womelsdorf\"],\"categories\":null,\"content\":\"\",\"date\":1388534400,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"38609b75c563c5181c81eaec7725bddf\",\"permalink\":\"https://LofNaDI.github.io/publication/ardid-2014-a/\",\"publishdate\":\"2020-02-13T16:52:11.36285Z\",\"relpermalink\":\"/publication/ardid-2014-a/\",\"section\":\"publication\",\"summary\":\"\",\"tags\":null,\"title\":\"“Adaptive learning” as a mechanistic candidate for reaching optimal task-set representations flexibly\\\"\",\"type\":\"publication\"},{\"authors\":[\"S. Ardid\",\"X.J. Wang\"],\"categories\":null,\"content\":\"\",\"date\":1385856000,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674733977,\"objectID\":\"6bae673be11d7e61666d8f4660cf0337\",\"permalink\":\"https://LofNaDI.github.io/publication/ardid-2013/\",\"publishdate\":\"2020-02-13T16:52:11.363302Z\",\"relpermalink\":\"/publication/ardid-2013/\",\"section\":\"publication\",\"summary\":\"A hallmark of executive control is the brain's agility to shift between different tasks depending on the behavioral rule currently in play. In this work, we propose a \\\"tweaking hypothesis\\\" for task switching: a weak rule signal provides a small bias that is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categorization and action selection, leading to an all-or-none reconfiguration of sensory-motor mapping. Based on this principle, we developed a biologically realistic model with multiple modules for task switching. We found that the model quantitatively accounts for complex task switching behavior: switch cost, congruency effect, and task-response interaction; as well as monkey's single-neuron activity associated with task switching. The model yields several testable predictions, in particular, that category-selective neurons play a key role in resolving sensory-motor conflict. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability.\",\"tags\":null,\"title\":\"A tweaking principle for executive control: neuronal circuit mechanism for rule-based task switching and conflict resolution\",\"type\":\"publication\"},{\"authors\":[\"T. Womelsdorf\",\"S. Westendorff\",\"S. Ardid\"],\"categories\":null,\"content\":\"\",\"date\":1356998400,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"2692526511cabc56d7fe1c62238eceb9\",\"permalink\":\"https://LofNaDI.github.io/publication/womelsdorf-2013/\",\"publishdate\":\"2020-02-13T16:52:11.363592Z\",\"relpermalink\":\"/publication/womelsdorf-2013/\",\"section\":\"publication\",\"summary\":\"\",\"tags\":null,\"title\":\"Subnetwork selection in deep cortical layers is mediated by beta-oscillation dependent firing\",\"type\":\"publication\"},{\"authors\":[\"J. D. Murray\",\"S. Ardid\"],\"categories\":null,\"content\":\"\",\"date\":1293840000,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"e61fb55f911cca523abec6e172f99165\",\"permalink\":\"https://LofNaDI.github.io/publication/murray-2011/\",\"publishdate\":\"2020-02-13T16:52:11.363794Z\",\"relpermalink\":\"/publication/murray-2011/\",\"section\":\"publication\",\"summary\":\"\",\"tags\":null,\"title\":\"What Can Tracking Fluctuations in Dozens of Sensory Neurons Tell about Selective Attention?\",\"type\":\"publication\"},{\"authors\":[\"S. Ardid\",\"X. J. Wang\",\"D. Gomez-Cabrero\",\"A. Compte\"],\"categories\":null,\"content\":\"\",\"date\":1266969600,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674733977,\"objectID\":\"3c41b5e9a2e79ed7b34b1c837083ed66\",\"permalink\":\"https://LofNaDI.github.io/publication/ardid-2010/\",\"publishdate\":\"2010-02-24T00:00:00Z\",\"relpermalink\":\"/publication/ardid-2010/\",\"section\":\"publication\",\"summary\":\"In this computational work, we investigated gamma-band synchronization across cortical circuits associated with selective attention. The model explicitly instantiates a reciprocally connected loop of spiking neurons between a sensory-type (area MT) and an executive-type (prefrontal/parietal) cortical circuit (the source area for top-down attentional signaling). Moreover, unlike models in which neurons behave as clock-like oscillators, in our model single-cell firing is highly irregular (close to Poisson), while local field potential exhibits a population rhythm. In this \\\"sparsely synchronized oscillation\\\" regime, the model reproduces and clarifies multiple observations from behaving animals. Top-down attentional inputs have a profound effect on network oscillatory dynamics while only modestly affecting single-neuron spiking statistics. In addition, attentional synchrony modulations are highly selective: interareal neuronal coherence occurs only when there is a close match between the preferred feature of neurons, the attended feature, and the presented stimulus, a prediction that is experimentally testable. When interareal coherence was abolished, attention-induced gain modulations of sensory neurons were slightly reduced. Therefore, our model reconciles the rate and synchronization effects, and suggests that interareal coherence contributes to large-scale neuronal computation in the brain through modest enhancement of rate modulations as well as a pronounced attention-specific enhancement of neural synchrony.\",\"tags\":null,\"title\":\"Reconciling coherent oscillation with modulation of irregular spiking activity in selective attention: gamma-range synchronization between sensory and executive cortical areas\",\"type\":\"publication\"},{\"authors\":[\"S. Ardid\",\"X. J. Wang\",\"A. Compte\"],\"categories\":null,\"content\":\"\",\"date\":1185926400,\"expirydate\":-62135596800,\"kind\":\"page\",\"lang\":\"en\",\"lastmod\":1674730273,\"objectID\":\"ba960d3c779044a0071af782dd08a67d\",\"permalink\":\"https://LofNaDI.github.io/publication/ardid-2007/\",\"publishdate\":\"2020-02-13T16:52:11.364275Z\",\"relpermalink\":\"/publication/ardid-2007/\",\"section\":\"publication\",\"summary\":\"Selective attention is a fundamental cognitive function that uses top-down signals to orient and prioritize information processing in the brain. Single-cell recordings from behaving monkeys have revealed a number of attention-induced effects on sensory neurons, and have given rise to contrasting viewpoints about the neural underpinning of attentive processing. Moreover, there is evidence that attentional signals originate from the prefrontoparietal working memory network, but precisely how a source area of attention interacts with a sensory system remains unclear. To address these questions, we investigated a biophysically based network model of spiking neurons composed of a reciprocally connected loop of two (sensory and working memory) networks. We found that a wide variety of physiological phenomena induced by selective attention arise naturally in such a system. In particular, our work demonstrates a neural circuit that instantiates the \\\"feature-similarity gain modulation principle,\\\" according to which the attentional gain effect on sensory neuronal responses is a graded function of the difference between the attended feature and the preferred feature of the neuron, independent of the stimulus. Furthermore, our model identifies key circuit mechanisms that underlie feature-similarity gain modulation, multiplicative scaling of tuning curve, and biased competition, and provide specific testable predictions. These results offer a synthetic account of the diverse attentional effects, suggesting a canonical neural circuit for feature-based attentional processing in the cortex.\",\"tags\":null,\"title\":\"An integrated microcircuit model of attentional processing in the 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Pascual Estrugo"],"categories":null,"content":"I am a physicist interested in the intersection between artificial intelligence (AI) and neuroscience, specially in cognitive neuroscience. Currently trying to develop more robust and flexible algorithms taking into account some brain dynamics.\n","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1674042515,"objectID":"c7878e6e9da7120d418cfcc9dd9b85f4","permalink":"https://LofNaDI.github.io/authors/benjamin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/benjamin/","section":"authors","summary":"I am a physicist interested in the intersection between artificial intelligence (AI) and neuroscience, specially in cognitive neuroscience. Currently trying to develop more robust and flexible algorithms taking into account some brain dynamics.","tags":null,"title":"Benjamín Pascual Estrugo","type":"authors"},{"authors":["David Carrasco Peris"],"categories":null,"content":"I\u0026rsquo;m technical engineer specialized in sensorization, home automation and artificial intelligence. My research interest includes the applications of Artificial Intelligence to improve learning algorithms.I\u0026rsquo;m PhD candidate at the Lab of Natural and Designed Intelligence.\n","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1674045868,"objectID":"5ba39124ed60e4a9ec212e2b8a7e4ca4","permalink":"https://LofNaDI.github.io/authors/david/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/david/","section":"authors","summary":"I\u0026rsquo;m technical engineer specialized in sensorization, home automation and artificial intelligence. My research interest includes the applications of Artificial Intelligence to improve learning algorithms.I\u0026rsquo;m PhD candidate at the Lab of Natural and Designed Intelligence.","tags":null,"title":"David Carrasco Peris","type":"authors"},{"authors":["Ester Dionís Martí"],"categories":null,"content":"I\u0026rsquo;m currently research assistant at the Lab of Natural and Designed Intelligence. My role inside the Lab is focus on clustering analysis and how to apply algorithms at chemical reactions in order to predict which catalyzer is the best for each case.\nExperience Electrochemical Characterization Of Ceramic Electrodes And Membranes And Application To Photoelectrooxidation And Electrofiltration Processes (UPV). Technician Specialized at the Centre For Omic Sciences. Technological Centre of Nutrition and Health Technician at the Proteomics Unit. Centre for Genomic Regulation / Universitat Pompeu Fabra. Technician at the Proteomics Core Facility. Centro de Investigación Príncipe Felipe. Technician at the Department of Physical Chemistry. Universität Friedrich-Alexander Erlangen-Nürnberg. Technician at the Department of Analitical Chemistry. Universität Friedrich-Alexander-SIEMENS Erlangen-Nürnberg. ","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1634300404,"objectID":"7e4a189ac6d33e1bd7283cf2c99c13ee","permalink":"https://LofNaDI.github.io/authors/ester/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/ester/","section":"authors","summary":"I\u0026rsquo;m currently research assistant at the Lab of Natural and Designed Intelligence. My role inside the Lab is focus on clustering analysis and how to apply algorithms at chemical reactions in order to predict which catalyzer is the best for each case.","tags":null,"title":"Ester Dionís Martí","type":"authors"},{"authors":["Juan García Méndez"],"categories":null,"content":"I\u0026rsquo;m physicist. My interests include theoretical physics on one hand, especially quantum field theory and quantum materials, and artificial intelligence on the other.I\u0026rsquo;m currently developing a deep neural network to predict neutrino trajectories in single-line events of the ANTARES telescope.\n","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1674044801,"objectID":"7195bffbc9efc2358393b2638532603f","permalink":"https://LofNaDI.github.io/authors/juan/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/juan/","section":"authors","summary":"I\u0026rsquo;m physicist. My interests include theoretical physics on one hand, especially quantum field theory and quantum materials, and artificial intelligence on the other.I\u0026rsquo;m currently developing a deep neural network to predict neutrino trajectories in single-line events of the ANTARES telescope.","tags":null,"title":"Juan García Méndez","type":"authors"},{"authors":["admin"],"categories":null,"content":"Our group is interested in identifying physiological mechanisms of brain function and dysfunction, in particular, the neural substrates of learning and intelligence. For this, we build neural network and reinforcement learning models.\nWhile keeping a strong interest in computational neuroscience, new endeavors seek to adapt computationally efficient mechanisms of the brain into AI architectures (such as deep neural networks) aiming for more powerful learning algorithms.\nBefore leading the Lab of Natural and Designed Intelligence at UPV, I was an Associate Research Scientist at Yale, where I was part of the Decision Lab (led by my current collaborator Ifat Levy). As a Postdoctoral Research Scientist, I collaborated with Nancy Kopell (Boston University), Thilo Womelsdorf (Vanderbilt University), and Xiao-Jing Wang (New York University). I earned my PhD in Computational Neuroscience at the Neuroscience Institute (UMH-CSIC), under the guidance of Albert Compte (IDIBAPS – Hospital Clinic).\n","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1634300589,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://LofNaDI.github.io/authors/admin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/admin/","section":"authors","summary":"Our group is interested in identifying physiological mechanisms of brain function and dysfunction, in particular, the neural substrates of learning and intelligence. For this, we build neural network and reinforcement learning models.","tags":null,"title":"Salva Ardid, PhD","type":"authors"},{"authors":["Santiago Galella Toledo"],"categories":null,"content":"I\u0026rsquo;m currently a research assistant at the Lab of Natural and Designed intelligence, where I study how to incorporate concepts from biological neural networks into deep learning models. My research interests lie in the intersection between neuroscience and artificial intelligence. I\u0026rsquo;m particularly interested in recurrent neural networks and learning algorithms. Currently, I\u0026rsquo;m doing research in activation functions, population dimensionality and stopping criteria in deep learning\n","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1634300404,"objectID":"d1c18aa97c9af60067a2e2147da359f1","permalink":"https://LofNaDI.github.io/authors/santiago/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/santiago/","section":"authors","summary":"I\u0026rsquo;m currently a research assistant at the Lab of Natural and Designed intelligence, where I study how to incorporate concepts from biological neural networks into deep learning models. My research interests lie in the intersection between neuroscience and artificial intelligence.","tags":null,"title":"Santiago Galella","type":"authors"},{"authors":["Silvia"],"categories":null,"content":"She is a specialized biologist in evaluating and monitoring marine and coastal ecosystems. Her research interests include the applications of Artificial Intelligence over marine ecosystem conservation, monitoring, and assessment.\nShe is a Ph.D. candidate at the Lab of Natural and Designed Intelligence. Her thesis is focused on developing neural circuits that link the prefrontal cortex with the striatum. These two brain areas seem implicated in the decision-making process and learning development, so their joining is essential in understanding how these processes work. As a starting point, they have taken task-switching related studies that link experimental data with animal behavior to create a neural circuit capable of representing the triggered neural dynamics as projections of different decisions.\nIn this framework, they aim to test three different scenarios. One will be considered the healthy condition, and the other two scenarios will represent illness conditions promoted by different dopamine concentrations (high and low levels). Previous works on this topic suggest that high dopamine levels are related to Parkinson\u0026rsquo;s and low dopamine levels with schizophrenia.\nSo, in the last year of her thesis work, she will focus on modifying those models to check how these different dopamine concentrations affect the \u0026ldquo;normal\u0026rdquo; circuit dynamics\n","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1674735623,"objectID":"0f12d11c56413a6f9905f00ad2ba69b0","permalink":"https://LofNaDI.github.io/authors/silvia/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/silvia/","section":"authors","summary":"She is a specialized biologist in evaluating and monitoring marine and coastal ecosystems. Her research interests include the applications of Artificial Intelligence over marine ecosystem conservation, monitoring, and assessment.\nShe is a Ph.","tags":null,"title":"Silvia Vilariño León","type":"authors"},{"authors":["J.H Lee","Y. Choe","S. Ardid","R. Abbasi-Asl","M. McCarthy","B. Hu"],"categories":null,"content":"","date":1674518400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1675423927,"objectID":"412ae213c66969fd560e9531788bf4fb","permalink":"https://LofNaDI.github.io/publication/lee-2023-functional/","publishdate":"2023-01-24T00:00:00Z","relpermalink":"/publication/lee-2023-functional/","section":"publication","summary":"Fundamental principles underlying higher-order cognitive functions remain elusive, but recent breakthroughs in neurophysiology and deep learning offer new perspectives. First, experimental studies have uncovered neural circuit motifs consisting of various neuron types; see Brain Initiative Cell Census Network (https://www.nature.com/collections/cicghheddj). For example, inhibitory neuron types expressing exclusive genes have specific targets and distinct functions (Pfeffer et al., 2013). Furthermore, diverse neuron types in cortex and their connectomes were identified in cortical columns (Jiang et al., 2015); see also Barth et al. (2016) for a debate on neuron types. Second, artificial neural networks were originally inspired by structures of the brain (McCulloch and Pitts, 1943) and could be trained to perform complex functions similar to human perception/cognition by deep learning (DL) (Lecun et al., 2015).","tags":null,"title":"Functional microcircuits in the brain and in artificial intelligent systems","type":"publication"},{"authors":["A. Albert","S. Alves Garre","M. André","M. Ardid","S. Ardid"],"categories":null,"content":"","date":1673568000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674562075,"objectID":"0043adebf6e907d4e3a9c2d6932f0463","permalink":"https://LofNaDI.github.io/publication/albert-2023-limits/","publishdate":"2023-01-13T00:00:00Z","relpermalink":"/publication/albert-2023-limits/","section":"publication","summary":"In this work, a search for nuclearites of strange quark matter by using nine years of ANTARES data taken in the period 2009–2017 is presented. The passage through matter of these particles is simulated taking into account a detailed description of the detector response to nuclearites and of the data acquisition conditions. A down-going flux of cosmic nuclearites with Galactic velocities (β = 10^-3) was considered for this study. The mass threshold for detecting these particles at the detector level is 4 × 10^13 GeV/c^2. Upper limits on the nuclearite flux for masses up to 1017 GeV/c^2 at the level of ∼ 5 × 10^17 cm^-2 s^-1 sr^-1 are obtained. These are the first upper limits on nuclearites established with a neutrino telescope and the most stringent ever set for Galactic velocities.","tags":null,"title":"Limits on the nuclearite flux using the ANTARES neutrino telescope","type":"publication"},{"authors":["J. Hass","S. Ardid","J. Sherfey","N. Kopell"],"categories":null,"content":"","date":1651795200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674562075,"objectID":"2f3f5efda9c5c523c31af71f984606d1","permalink":"https://LofNaDI.github.io/publication/hass-2022-constraints/","publishdate":"2022-05-06T00:00:00Z","relpermalink":"/publication/hass-2022-constraints/","section":"publication","summary":"Persistent activity, the maintenance of neural activation over short periods of time in cortical networks, is widely thought to underlie the cognitive function of working memory. A large body of modeling studies has reproduced this kind of activity using cell assemblies with strengthened synaptic connections. However, almost all of these studies have considered persistent activity within networks with homogeneous neurons and synapses, making it difficult to judge the validity of such model results for cortical dynamics, which is based on highly heterogeneous neurons. Here, we consider persistent activity in a detailed, strongly data-driven network model of the prefrontal cortex with heterogeneous neuron and synapse parameters. Surprisingly, persistent activity could not be reproduced in this model without incorporating further constraints. We identified three factors that prevent successful persistent activity: heterogeneity in the cell parameters of interneurons, heterogeneity in the parameters of short-term synaptic plasticity and heterogeneity in the synaptic weights. We also discovered a general dynamic mechanism that prevents persistent activity in the presence of heterogeneities, namely a gradual drop-out of cell assembly neurons out of a bistable regime as input variability increases. Based on this mechanism, we found that persistent activity is recovered if heterogeneity is compensated, e.g., by a homeostatic plasticity mechanism. Cell assemblies shaped in this way may be potentially targeted by distinct inputs or become more responsive to specific tuning or spectral properties. Finally, we show that persistent activity in the model is robust against external noise, but the compensation of heterogeneities may prevent the dynamic generation of intrinsic in vivo-like irregular activity. These results may help informing the ongoing debate about the neural basis of working memory.","tags":null,"title":"Constraints on persistent activity in a biologically detailed network model of the prefrontal cortex with heterogeneities","type":"publication"},{"authors":["S. Aeillo","A. Albert","M. Alshamsi","S. Alves Garre","Z. Aly","A. Ambronose"],"categories":null,"content":"","date":1646784000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674562075,"objectID":"687e04119c36bb0e311f855ec3f886e7","permalink":"https://LofNaDI.github.io/publication/aiello-2022-combined/","publishdate":"2022-03-09T00:00:00Z","relpermalink":"/publication/aiello-2022-combined/","section":"publication","summary":"This article presents the potential of a combined analysis of the JUNO and KM3NeT/ORCA experiments to determine the neutrino mass ordering. This combination is particularly interesting as it significantly boosts the potential of either detector, beyond simply adding their neutrino mass ordering sensitivities, by removing a degeneracy in the determination of ∆𝑚231 between the two experiments when assuming the wrong ordering. The study is based on the latest projected performances for JUNO, and on simulation tools using a full Monte Carlo approach to the KM3NeT/ORCA response with a careful assessment of its energy systematics. From this analysis, a 5σ determination of the neutrino mass ordering is expected after 6 years of joint data taking for any value of the oscillation parameters. This sensitivity would be achieved after only 2 years of joint data taking assuming the current global best-fit values for those parameters for normal ordering.","tags":null,"title":"Combined sensitivity of JUNO and KM3NeT/ORCA to the neutrino mass ordering","type":"publication"},{"authors":["S. Aeillo","A. Albert","S. Alves Garre","Z. Aly","A. Ambronose"],"categories":null,"content":"","date":1641859200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674562075,"objectID":"a82741bf677392f93ff7fe3a4fe7bb79","permalink":"https://LofNaDI.github.io/publication/aiello-2022/","publishdate":"2022-01-11T00:00:00Z","relpermalink":"/publication/aiello-2022/","section":"publication","summary":"The next generation of water Cherenkov neutrino telescopes in the Mediterranean Sea are under construction offshore France (KM3NeT/ORCA) and Sicily (KM3NeT/ARCA). The KM3NeT/ORCA detector features an energy detection threshold which allows to collect atmospheric neutrinos to study flavour oscillation. This paper reports the KM3NeT/ORCA sensitivity to this phenomenon. The event reconstruction, selection and classification are described. The sensitivity to determine the neutrino mass ordering was evaluated and found to be 4.4σ if the true ordering is normal and 2.3σ if inverted, after 3 years of data taking. The precision to measure Δ𝑚232 and 𝜃23 were also estimated and found to be 85.10−6 eV2 and (+1.9−3.1)∘ for normal neutrino mass ordering and, 75.10−6 eV2 and (+2.0−7.0)∘ for inverted ordering. Finally, a unitarity test of the leptonic mixing matrix by measuring the rate of tau neutrinos is described. Three years of data taking were found to be sufficient to exclude event rate variations larger than 20% at 3σ level.","tags":null,"title":"Determining the neutrino mass ordering and oscillation parameters with KM3NeT/ORCA","type":"publication"},{"authors":["J. García-Méndez","N. Geißelbrecht","T. Eberl","M. Ardid","S. Ardid"],"categories":null,"content":"","date":1631836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1675423927,"objectID":"b4afebc8bdcac9a58a8cf1fb18f35cb0","permalink":"https://LofNaDI.github.io/publication/garciam-2021-deep/","publishdate":"2021-09-17T00:00:00Z","relpermalink":"/publication/garciam-2021-deep/","section":"publication","summary":"ANTARES is currently the largest undersea neutrino telescope, located in the Mediterranean Sea and taking data since 2007. It consists of a 3D array of photo sensors, instrumenting about 10Mt of seawater to detect Cherenkov light induced by secondary particles from neutrino interactions. The event reconstruction and background discrimination is challenging and machine-learning techniques are explored to improve the performance. In this contribution, two case studies using deep convolutional neural networks are presented. In the first one, this approach is used to improve the direction reconstruction of low-energy single-line events, for which the reconstruction of the azimuth angle of the incoming neutrino is particularly difficult. We observe a promising improvement in resolution over classical reconstruction techniques and expect to at least double our sensitivity in the low-energy range, important for dark matter searches. The second study employs deep learning to reconstruct the visible energy of neutrino interactions of all flavors and for the multi-line setup of the full detector.","tags":null,"title":"Deep learning reconstruction in ANTARES","type":"publication"},{"authors":["A. Albert","S. Alves Garre","M. André","M. Anghinolfi","G. Anton","M. Ardid","S. Ardid"],"categories":null,"content":"","date":1622505600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"4944f22cf1028b3b50271b5e6e883bb6","permalink":"https://LofNaDI.github.io/publication/albert-2022-search/","publishdate":"2021-06-01T00:00:00Z","relpermalink":"/publication/albert-2022-search/","section":"publication","summary":"This work presents a new search for magnetic monopoles using data taken with the ANTARES neutrino telescope over a period of 10 years (January 2008 to December 2017). Compared to previous ANTARES searches, this analysis uses a run-by-run simulation strategy, with a larger exposure as well as a new simulation of magnetic monopoles taking into account the Kasama, Yang and Goldhaber model for their interaction cross-section with matter. No signal compatible with the passage of relativistic magnetic monopoles is observed, and upper limits on the flux of magnetic monopoles with β=v/c≥0.55, are presented. For ultra-relativistic magnetic monopoles the flux limit is ∼7× 10^−18 cm^−2 s^−1 sr− 1.","tags":null,"title":"Search for Magnetic Monopoles with ten years of the ANTARES neutrino telescope","type":"publication"},{"authors":["S. Aeillo","A. Albert","S. Alves Garre","Z. Aly","A. Ambronose"],"categories":null,"content":"","date":1621814400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674562075,"objectID":"c3cd77b60d5dab3e74dff2c1c46de79d","permalink":"https://LofNaDI.github.io/publication/aiello-2021/","publishdate":"2021-05-24T00:00:00Z","relpermalink":"/publication/aiello-2021/","section":"publication","summary":"The KM3NeT research infrastructure is under construction in the Mediterranean Sea. It consists of two water Cherenkov neutrino detectors, ARCA and ORCA, aimed at neutrino astrophysics and oscillation research, respectively. Instrumenting a large volume of sea water with ∼6200 optical modules comprising a total of ∼200,000 photomultiplier tubes, KM3NeT will achieve sensitivity to ∼10 MeV neutrinos from Galactic and near-Galactic core-collapse supernovae through the observation of coincident hits in photomultipliers above the background. In this paper, the sensitivity of KM3NeT to a supernova explosion is estimated from detailed analyses of background data from the first KM3NeT detection units and simulations of the neutrino signal. The KM3NeT observational horizon (for a 5σ discovery) covers essentially the Milky-Way and for the most optimistic model, extends to the Small Magellanic Cloud (∼60 kpc). Detailed studies of the time profile of the neutrino signal allow assessment of the KM3NeT capability to determine the arrival time of the neutrino burst with a few milliseconds precision for sources up to 5–8 kpc away, and detecting the peculiar signature of the standing accretion shock instability if the core-collapse supernova explosion happens closer than 3–5 kpc, depending on the progenitor mass. KM3NeT’s capability to measure the neutrino flux spectral parameters is also presented.","tags":null,"title":"The KM3NeT potential for the next core-collapse supernova observation with neutrinos","type":"publication"},{"authors":["S. Aeillo","A. Albert","M. Alshamsi","S. Alves Garre","Z. Aly","A. Ambronose"],"categories":null,"content":"","date":1618099200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674562075,"objectID":"42a875c6938170be0f6e581169036ee9","permalink":"https://LofNaDI.github.io/publication/aiello-2022-implementation/","publishdate":"2021-04-11T00:00:00Z","relpermalink":"/publication/aiello-2022-implementation/","section":"publication","summary":"The KM3NeT research infrastructure is unconstruction in the Mediterranean Sea. KM3NeT will study atmospheric and astrophysical neutrinos with two multi-purpose neutrino detectors, ARCA and ORCA, primarily aimed at GeV–PeV neutrinos. Thanks to the multi-photomultiplier tube design of the digital optical modules, KM3NeT is capable of detecting the neutrino burst from a Galactic or near-Galactic core-collapse supernova. This potential is already exploitable with the first detection units deployed in the sea. This paper describes the real-time implementation of the supernova neutrino search, operating on the two KM3NeT detectors since the first months of 2019. A quasi-online astronomy analysis is introduced to study the time profile of the detected neutrinos for especially significant events. The mechanism of generation and distribution of alerts, as well as the integration into the SNEWS and SNEWS 2.0 global alert systems, are described. The approach for the follow-up of external alerts with a search for a neutrino excess in the archival data is defined. Finally, an overview of the current detector capabilities and a report after the first two years of operation are given","tags":null,"title":"Implementation and first results of the KM3NeT real-time core-collapse supernova neutrino search","type":"publication"},{"authors":["S. Aiello","A. Albert","M. Alshamsi","S. Alve Garre","Z. Aly"],"categories":null,"content":"","date":1603238400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"2e7eaa9cb20f1c5d215f1720ad00bc0c","permalink":"https://LofNaDI.github.io/publication/km3net-2021/","publishdate":"2020-10-21T00:00:00Z","relpermalink":"/publication/km3net-2021/","section":"publication","summary":"KM3NeT/ORCA is a next-generation neutrino telescope optimised for atmospheric neutrino oscillations studies. In this paper, the sensitivity of ORCA to the presence of a light sterile neutrino in a 3+1 model is presented. After three years of data taking, ORCA will be able to probe the active-sterile mixing angles θ 14, θ 24, θ 34 and the effective angle θ μe, over a broad range of mass squared difference Δ𝑚^2-41 ∼ [10^−5, 10] eV2, allowing to test the eV-mass sterile neutrino hypothesis as the origin of short baseline anomalies, as well as probing the hypothesis of a very light sterile neutrino, not yet constrained by cosmology. ORCA will be able to explore a relevant fraction of the parameter space not yet reached by present measurements.","tags":null,"title":"Sensitivity to light sterile neutrino mixing parameters with KM3NeT/ORCA","type":"publication"},{"authors":["J. S. Sherfey","S. Ardid","E. K. Miller","M. E. Hasselmo","N. J. Kopell"],"categories":null,"content":"","date":1598918400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"3217c7ed4a57f95ad4d749f61fcf3a34","permalink":"https://LofNaDI.github.io/publication/sherfey-2020/","publishdate":"2020-02-13T16:52:11.360094Z","relpermalink":"/publication/sherfey-2020/","section":"publication","summary":"Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma-frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.","tags":null,"title":"Prefrontal oscillations modulate the propagation of neuronal activity required for working memory","type":"publication"},{"authors":["S. Ardid","J. S. Sherfey","M. M. McCarthy","J. Hass","B. R. Pittman-Polletta","N. Kopell"],"categories":null,"content":"","date":1554076800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"15a28272a6ab51f64df4ad946bd3fd67","permalink":"https://LofNaDI.github.io/publication/ardid-2019/","publishdate":"2020-02-13T16:52:11.360094Z","relpermalink":"/publication/ardid-2019/","section":"publication","summary":"Classical accounts of biased competition require an input bias to resolve the competition between neuronal ensembles driving downstream processing. However, flexible and reliable selection of behaviorally relevant ensembles can occur with unbiased stimulation: striatal D1 and D2 spiny projection neurons (SPNs) receive balanced cortical input, yet their activity determines the choice between GO and NO-GO pathways in the basal ganglia. We here present a corticostriatal model identifying three mechanisms that rely on physiological asymmetries to effect rate- and time-coded biased competition in the presence of balanced inputs. First, tonic input strength determines which one of the two SPN phenotypes exhibits a higher mean firing rate. Second, low-strength oscillatory inputs induce higher firing rate in D2 SPNs but higher coherence between D1 SPNs. Third, high-strength inputs oscillating at distinct frequencies can preferentially activate D1 or D2 SPN populations. Of these mechanisms, only the latter accommodates observed rhythmic activity supporting rule-based decision making in prefrontal cortex. [[Code]](https://github.com/LofNaDI/unbiasedCompetition)","tags":null,"title":"Biased competition in the absence of input bias revealed through corticostriatal computation","type":"publication"},{"authors":["M. Oemisch","S. Westendorff","M. Azimi","S. A. Hassani","S. Ardid","P. Tiesinga","T. Womelsdorf"],"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"802d9a1d6d8be192c0000606b55a3685","permalink":"https://LofNaDI.github.io/publication/oemisch-2019/","publishdate":"2020-02-13T16:52:11.360541Z","relpermalink":"/publication/oemisch-2019/","section":"publication","summary":"To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Here, we report a potential solution: neurons in four recorded areas of the anterior fronto-striatal networks encode prediction errors that are specific to feature values of different dimensions of attended multidimensional stimuli. The most ubiquitous prediction error occurred for the reward-relevant dimension. Feature-specific prediction error signals a) emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anterior cingulate cortex and later in dorsolateral prefrontal cortex, caudate and ventral striatum, and c) contribute to feature-based stimulus selection after learning. Thus, a widely-distributed feature-specific eligibility trace may be used to update synaptic weights for improved feature-based attention.","tags":null,"title":"Feature-specific prediction errors and surprise across macaque fronto-striatal circuits","type":"publication"},{"authors":["J. S. Sherfey","S. Ardid","J. Hass","M. E. Hasselmo","N. J. Kopell"],"categories":null,"content":"","date":1533081600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"39da3a5505b9385f28c786d299738e1d","permalink":"https://LofNaDI.github.io/publication/sherfey-2018-a/","publishdate":"2020-02-13T16:52:11.360827Z","relpermalink":"/publication/sherfey-2018-a/","section":"publication","summary":"Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.","tags":null,"title":"Flexible resonance in prefrontal networks with strong feedback inhibition","type":"publication"},{"authors":["J. S. Sherfey","A. E. Soplata","S. Ardid","E. A. Roberts","D. A. Stanley","B. R. Pittman-Polletta","N. J. Kopell"],"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"2873b3de8cc6e67b3e8af67b198b857f","permalink":"https://LofNaDI.github.io/publication/sherfey-2018-b/","publishdate":"2020-02-13T16:52:11.361052Z","relpermalink":"/publication/sherfey-2018-b/","section":"publication","summary":"DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. DynaSim is freely available at http://dynasimtoolbox.org. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.","tags":null,"title":"DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation","type":"publication"},{"authors":["S. A. Hassani","M. Oemisch","M. Balcarras","S. Westendorff","S. Ardid","M. A. van der Meer","P. Tiesinga","T. Womelsdorf"],"categories":null,"content":"","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"8074a97c5a0b4c5d5a5bb9b2790aa80f","permalink":"https://LofNaDI.github.io/publication/hassani-2017/","publishdate":"2020-02-13T16:52:11.361269Z","relpermalink":"/publication/hassani-2017/","section":"publication","summary":"Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.","tags":null,"title":"A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque","type":"publication"},{"authors":["M. Balcarras","S. Ardid","D. Kaping","S. Everling","T. Womelsdorf"],"categories":null,"content":"","date":1454284800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"4e2f135763ebd90fe4ff02e399833e65","permalink":"https://LofNaDI.github.io/publication/balcarras-2016/","publishdate":"2020-02-13T16:52:11.361503Z","relpermalink":"/publication/balcarras-2016/","section":"publication","summary":"Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.","tags":null,"title":"Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness","type":"publication"},{"authors":["D. Gomez-Cabrero","S. Ardid","M. Cano-Colino","J. Tegnér","A. Compte"],"categories":null,"content":"","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674734214,"objectID":"81d87c60ac3dcfaf1fc4484f68e7dde1","permalink":"https://LofNaDI.github.io/publication/gomez-cabrero-2016/","publishdate":"2020-02-13T16:52:11.361757Z","relpermalink":"/publication/gomez-cabrero-2016/","section":"publication","summary":"","tags":null,"title":"Neuroswarm: a methodology to explore the constraints that function imposes on simulation parameters in large-scale networks of biological neurons","type":"publication"},{"authors":["C. Shen","S. Ardid","D. Kaping","S. Westendorff","S. Everling","T. Womelsdorf"],"categories":null,"content":"","date":1438387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"9080c22c3a0897201e0583e7cb364788","permalink":"https://LofNaDI.github.io/publication/shen-2015/","publishdate":"2020-02-13T16:52:11.362344Z","relpermalink":"/publication/shen-2015/","section":"publication","summary":"Errors indicate the need to adjust attention for improved future performance. Detecting errors is thus a fundamental step to adjust and control attention. These functions have been associated with the dorsal anterior cingulate cortex (dACC), predicting that dACC cells should track the specific processing states giving rise to errors in order to identify which processing aspects need readjustment. Here, we tested this prediction by recording cells in the dACC and lateral prefrontal cortex (latPFC) of macaques performing an attention task that dissociated 3 processing stages. We found that, across prefrontal subareas, the dACC contained the largest cell populations encoding errors indicating (1) failures of inhibitory control of the attentional focus, (2) failures to prevent bottom-up distraction, and (3) lapses when implementing a choice. Error-locked firing in the dACC showed the earliest latencies across the PFC, emerged earlier than reward omission signals, and involved a significant proportion of putative inhibitory interneurons. Moreover, early onset error-locked response enhancement in the dACC was followed by transient prefrontal-cingulate inhibition, possibly reflecting active disengagement from task processing. These results suggest a functional specialization of the dACC to track and identify the actual processes that give rise to erroneous task outcomes, emphasizing its role to control attentional performance.","tags":null,"title":"Anterior Cingulate Cortex Cells Identify Process-Specific Errors of Attentional Control Prior to Transient Prefrontal-Cingulate Inhibition","type":"publication"},{"authors":["S. Ardid","M. Vinck","D. Kaping","S. Marquez","S. Everling","T. Womelsdorf"],"categories":null,"content":"","date":1422748800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"e6fc9270dcc966424396973881743a86","permalink":"https://LofNaDI.github.io/publication/ardid-2015/","publishdate":"2020-02-13T16:52:11.362004Z","relpermalink":"/publication/ardid-2015/","section":"publication","summary":"Microcircuits are composed of multiple cell classes that likely serve unique circuit operations. But how cell classes map onto circuit functions is largely unknown, particularly for primate prefrontal cortex during actual goal-directed behavior. One difficulty in this quest is to reliably distinguish cell classes in extracellular recordings of action potentials. Here we surmount this issue and report that spike shape and neural firing variability provide reliable markers to segregate seven functional classes of prefrontal cells in macaques engaged in an attention task. We delineate an unbiased clustering protocol that identifies four broad spiking (BS) putative pyramidal cell classes and three narrow spiking (NS) putative inhibitory cell classes dissociated by how sparse, bursty, or regular they fire. We speculate that these functional classes map onto canonical circuit functions. First, two BS classes show sparse, bursty firing, and phase synchronize their spiking to 3-7 Hz (theta) and 12-20 Hz (beta) frequency bands of the local field potential (LFP). These properties make cells flexibly responsive to network activation at varying frequencies. Second, one NS and two BS cell classes show regular firing and higher rate with only marginal synchronization preference. These properties are akin to setting tonically the excitation and inhibition balance. Finally, two NS classes fired irregularly and synchronized to either theta or beta LFP fluctuations, tuning them potentially to frequency-specific subnetworks. These results suggest that a limited set of functional cell classes emerges in macaque prefrontal cortex (PFC) during attentional engagement to not only represent information, but to subserve basic circuit operations. [[Code: Clustering Analysis]](https://github.com/LofNaDI/clusteringAnalysis.git) and [[Code: Waveform Analysis]](https://github.com/LofNaDI/waveformAnalysis.git)","tags":null,"title":"Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex","type":"publication"},{"authors":["T. Womelsdorf","S. Ardid","S. Everling","T. A. Valiante"],"categories":null,"content":"","date":1414800000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"2b274135bc4b3e8fa639588f2ce72225","permalink":"https://LofNaDI.github.io/publication/womelsdorf-2014/","publishdate":"2020-02-13T16:52:11.362603Z","relpermalink":"/publication/womelsdorf-2014/","section":"publication","summary":"It is widely held that single cells in anterior cingulate and lateral prefrontal cortex (ACC/PFC) coordinate their activity during attentional processes, although cellular activity that may underlie such coordination across ACC/PFC has not been identified. We thus recorded cells in five ACC/PFC subfields of macaques engaged in a selective attention task, characterized those spiking events that indexed attention, and identified how spiking of distinct cell populations synchronized between brain areas. We found that cells in ACC/PFC increased the firing of brief 200 Hz spike bursts when subjects shifted attention and engaged in selective visual processing. In contrast to nonburst spikes, burst spikes synchronized over large distances to local field potentials at narrow beta (12-20 Hz) and at gamma (50-75 Hz) frequencies. Long-range burst synchronization was anatomically specific, functionally connecting those subfields in area 24 (ACC) and area 46 (PFC) that are key players of attentional control. By splitting cells into putative excitatory (pE) and inhibitory (pI) cells by their broad and narrow spikes, we identified that bursts of pI cells preceded and that bursts of pE cells followed in time periods of maximal beta coherent network activity. In contrast, gamma bursts were transient impulses with equal timing across cell classes. These findings suggest that processes underlying burst firing and burst synchronization are candidate mechanisms to coordinate attention information across brain areas. We speculate that distinct burst-firing motifs realize beta and gamma synchrony to trigger versus maintain functional network states during goal-directed behavior.","tags":null,"title":"Burst firing synchronizes prefrontal and anterior cingulate cortex during attentional control","type":"publication"},{"authors":["S. Ardid","X.-J. Wang"],"categories":null,"content":"","date":1388534400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"102e0c2e3d2bc489df38f73e774aedef","permalink":"https://LofNaDI.github.io/publication/ardid-2014-b/","publishdate":"2020-02-13T16:52:11.3631Z","relpermalink":"/publication/ardid-2014-b/","section":"publication","summary":"","tags":null,"title":"The “tweaking principle” for task switching","type":"publication"},{"authors":["S. Ardid","M. Balcarras","T. Womelsdorf"],"categories":null,"content":"","date":1388534400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"38609b75c563c5181c81eaec7725bddf","permalink":"https://LofNaDI.github.io/publication/ardid-2014-a/","publishdate":"2020-02-13T16:52:11.36285Z","relpermalink":"/publication/ardid-2014-a/","section":"publication","summary":"","tags":null,"title":"“Adaptive learning” as a mechanistic candidate for reaching optimal task-set representations flexibly\"","type":"publication"},{"authors":["S. Ardid","X.J. Wang"],"categories":null,"content":"","date":1385856000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674733977,"objectID":"6bae673be11d7e61666d8f4660cf0337","permalink":"https://LofNaDI.github.io/publication/ardid-2013/","publishdate":"2020-02-13T16:52:11.363302Z","relpermalink":"/publication/ardid-2013/","section":"publication","summary":"A hallmark of executive control is the brain's agility to shift between different tasks depending on the behavioral rule currently in play. In this work, we propose a \"tweaking hypothesis\" for task switching: a weak rule signal provides a small bias that is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categorization and action selection, leading to an all-or-none reconfiguration of sensory-motor mapping. Based on this principle, we developed a biologically realistic model with multiple modules for task switching. We found that the model quantitatively accounts for complex task switching behavior: switch cost, congruency effect, and task-response interaction; as well as monkey's single-neuron activity associated with task switching. The model yields several testable predictions, in particular, that category-selective neurons play a key role in resolving sensory-motor conflict. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability.","tags":null,"title":"A tweaking principle for executive control: neuronal circuit mechanism for rule-based task switching and conflict resolution","type":"publication"},{"authors":["T. Womelsdorf","S. Westendorff","S. Ardid"],"categories":null,"content":"","date":1356998400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"2692526511cabc56d7fe1c62238eceb9","permalink":"https://LofNaDI.github.io/publication/womelsdorf-2013/","publishdate":"2020-02-13T16:52:11.363592Z","relpermalink":"/publication/womelsdorf-2013/","section":"publication","summary":"","tags":null,"title":"Subnetwork selection in deep cortical layers is mediated by beta-oscillation dependent firing","type":"publication"},{"authors":["J. D. Murray","S. Ardid"],"categories":null,"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"e61fb55f911cca523abec6e172f99165","permalink":"https://LofNaDI.github.io/publication/murray-2011/","publishdate":"2020-02-13T16:52:11.363794Z","relpermalink":"/publication/murray-2011/","section":"publication","summary":"","tags":null,"title":"What Can Tracking Fluctuations in Dozens of Sensory Neurons Tell about Selective Attention?","type":"publication"},{"authors":["S. Ardid","X. J. Wang","D. Gomez-Cabrero","A. Compte"],"categories":null,"content":"","date":1266969600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674733977,"objectID":"3c41b5e9a2e79ed7b34b1c837083ed66","permalink":"https://LofNaDI.github.io/publication/ardid-2010/","publishdate":"2010-02-24T00:00:00Z","relpermalink":"/publication/ardid-2010/","section":"publication","summary":"In this computational work, we investigated gamma-band synchronization across cortical circuits associated with selective attention. The model explicitly instantiates a reciprocally connected loop of spiking neurons between a sensory-type (area MT) and an executive-type (prefrontal/parietal) cortical circuit (the source area for top-down attentional signaling). Moreover, unlike models in which neurons behave as clock-like oscillators, in our model single-cell firing is highly irregular (close to Poisson), while local field potential exhibits a population rhythm. In this \"sparsely synchronized oscillation\" regime, the model reproduces and clarifies multiple observations from behaving animals. Top-down attentional inputs have a profound effect on network oscillatory dynamics while only modestly affecting single-neuron spiking statistics. In addition, attentional synchrony modulations are highly selective: interareal neuronal coherence occurs only when there is a close match between the preferred feature of neurons, the attended feature, and the presented stimulus, a prediction that is experimentally testable. When interareal coherence was abolished, attention-induced gain modulations of sensory neurons were slightly reduced. Therefore, our model reconciles the rate and synchronization effects, and suggests that interareal coherence contributes to large-scale neuronal computation in the brain through modest enhancement of rate modulations as well as a pronounced attention-specific enhancement of neural synchrony.","tags":null,"title":"Reconciling coherent oscillation with modulation of irregular spiking activity in selective attention: gamma-range synchronization between sensory and executive cortical areas","type":"publication"},{"authors":["S. Ardid","X. J. Wang","A. Compte"],"categories":null,"content":"","date":1185926400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674730273,"objectID":"ba960d3c779044a0071af782dd08a67d","permalink":"https://LofNaDI.github.io/publication/ardid-2007/","publishdate":"2020-02-13T16:52:11.364275Z","relpermalink":"/publication/ardid-2007/","section":"publication","summary":"Selective attention is a fundamental cognitive function that uses top-down signals to orient and prioritize information processing in the brain. Single-cell recordings from behaving monkeys have revealed a number of attention-induced effects on sensory neurons, and have given rise to contrasting viewpoints about the neural underpinning of attentive processing. Moreover, there is evidence that attentional signals originate from the prefrontoparietal working memory network, but precisely how a source area of attention interacts with a sensory system remains unclear. To address these questions, we investigated a biophysically based network model of spiking neurons composed of a reciprocally connected loop of two (sensory and working memory) networks. We found that a wide variety of physiological phenomena induced by selective attention arise naturally in such a system. In particular, our work demonstrates a neural circuit that instantiates the \"feature-similarity gain modulation principle,\" according to which the attentional gain effect on sensory neuronal responses is a graded function of the difference between the attended feature and the preferred feature of the neuron, independent of the stimulus. Furthermore, our model identifies key circuit mechanisms that underlie feature-similarity gain modulation, multiplicative scaling of tuning curve, and biased competition, and provide specific testable predictions. These results offer a synthetic account of the diverse attentional effects, suggesting a canonical neural circuit for feature-based attentional processing in the cortex.","tags":null,"title":"An integrated microcircuit model of attentional processing in the neocortex","type":"publication"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1634300404,"objectID":"c1d17ff2b20dca0ad6653a3161942b64","permalink":"https://LofNaDI.github.io/people/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/people/","section":"","summary":"","tags":null,"title":"","type":"widget_page"}]</div></div></div></div></div></div><div id="copilot-button-container"></div></div><div id="highlighted-line-menu-container"></div></div></div><button hidden="" data-testid="hotkey-button" data-hotkey-scope="read-only-cursor-text-area"></button><button hidden=""></button></section></div></div></div> <!-- --> <!-- --> </div></div></div><div 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