index.json

[{"authors":["laurent-u-perrinet"],"categories":null,"content":"Laurent Perrinet is a computational neuroscientist specialized in large scale neural network models of low-level vision, perception and action, currently at the “Institut de Neurosciences de la Timone” (France), a joint research unit (CNRS / Aix-Marseille Université, UMR7289). He co-authored more than 40 articles in computational neuroscience and computer vision. He graduated from the aeronautics engineering school SUPAERO, in Toulouse (France) with a signal processing and applied mathematics degree. He received a PhD in Cognitive Science in 2003 on the mathematical analysis of temporal spike coding of images by using a multi-scale and adaptive representation of natural scenes. His research program is focusing in bridging the complex dynamics of realistic, large-scale models of spiking neurons with functional models of low-level vision. In particular, as part of the FACETS and BrainScaleS consortia, he has developed experimental protocols in collaboration with neurophysiologists to characterize the response of population of neurons. Recently, he extended models of visual processing in the framework of predictive processing in collaboration with the team of Karl Friston at the University College of London. This method aims at characterizing the processing of dynamical flow of information as an active inference process. His current challenge within the NeOpTo team is to translate, or compile in computer terminology, this mathematical formalism with the event-based nature of neural information with the aim of pushing forward the frontiers of Artificial Intelligence systems.\n","date":1711724400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1711724400,"objectID":"34290dc2ee08b914b9858e658a955aa2","permalink":"https://conect-int.github.io/authors/laurent-u-perrinet/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/laurent-u-perrinet/","section":"authors","summary":"Laurent Perrinet is a computational neuroscientist specialized in large scale neural network models of low-level vision, perception and action, currently at the “Institut de Neurosciences de la Timone” (France), a joint research unit (CNRS / Aix-Marseille Université, UMR7289).","tags":null,"title":"Laurent U Perrinet","type":"authors"},{"authors":["matthieu-gilson"],"categories":null,"content":"How does the brain integrate information about the subject’s environment with internal processes like motivation to produce behavior? What are the neuronal mechanisms and circuits that implement those functions? To address those questions, I develop models of neuronal networks to study their dynamics and emerging functions, at the microscopic and macroscopic levels. My continuing interest is on distributed representations and computations in neuronal networks. I use network models to interpret neuroimaging and electrophysiological data (spike trains, fMRI, EEG, MEG), in terms of brain communication at the macroscopic level and in terms of neuronal computations at the microscopic level. See Projects and Publications (top of page) for details.\n","date":1710320400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1710320400,"objectID":"1d7da2d49b38c493b9cb72d7372b026d","permalink":"https://conect-int.github.io/authors/matthieu-gilson/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/matthieu-gilson/","section":"authors","summary":"How does the brain integrate information about the subject’s environment with internal processes like motivation to produce behavior? What are the neuronal mechanisms and circuits that implement those functions? To address those questions, I develop models of neuronal networks to study their dynamics and emerging functions, at the microscopic and macroscopic levels.","tags":null,"title":"Matthieu Gilson","type":"authors"},{"authors":["hugo-ladret"],"categories":null,"content":"PhD Student (2019-09 / 2024-02): A multiscale cortical model to account for orientation selectivity in natural-like stimulations Aix-Marseille Université, Institut des Neurosciences de la Timone Université de Montréal, Laboratoire des Neurosciences de la Vision Hugo Ladret focuses on predictive coding, an influential brain theory that promises to account for the many seemingly disparate results neuroscientists have gathered over decades of experiments. Using neurobiology with a theory-driven approach, his experimental work deals about vision, and to find theoretical insights for neural network modelling.\n","date":1695306600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1695306600,"objectID":"93c3936b7d939ed96ca258bc12454ed2","permalink":"https://conect-int.github.io/authors/hugo-ladret/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/hugo-ladret/","section":"authors","summary":"PhD Student (2019-09 / 2024-02): A multiscale cortical model to account for orientation selectivity in natural-like stimulations Aix-Marseille Université, Institut des Neurosciences de la Timone Université de Montréal, Laboratoire des Neurosciences de la Vision Hugo Ladret focuses on predictive coding, an influential brain theory that promises to account for the many seemingly disparate results neuroscientists have gathered over decades of experiments.","tags":null,"title":"Hugo Ladret","type":"authors"},{"authors":["nicolas-meirhaeghe"],"categories":null,"content":"","date":1687269600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1687269600,"objectID":"32cecbcee646b1047e4b863be838f20f","permalink":"https://conect-int.github.io/authors/nicolas-meirhaeghe/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/nicolas-meirhaeghe/","section":"authors","summary":"","tags":null,"title":"Nicolas Meirhaeghe","type":"authors"},{"authors":["emmanuel-dauce"],"categories":null,"content":"Emmanuel Daucé is associate professor at the Ecole Centrale de Marseille, doing his research in Computational Neuroscience at the Institut de Neurosciences de la Timone (France), a joint research unit (CNRS / Aix-Marseille Université). His research lies at the crossroad of machine learning, artificial intelligence and neuroscience, seeking to develop innovative computational models and methods though remaining consistent with the principles of biological systems.\n","date":1686560400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1686560400,"objectID":"fc438c70df365b40938335cb7e325000","permalink":"https://conect-int.github.io/authors/emmanuel-dauce/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/emmanuel-dauce/","section":"authors","summary":"Emmanuel Daucé is associate professor at the Ecole Centrale de Marseille, doing his research in Computational Neuroscience at the Institut de Neurosciences de la Timone (France), a joint research unit (CNRS / Aix-Marseille Université).","tags":null,"title":"Emmanuel Daucé","type":"authors"},{"authors":["simon-nougaret"],"categories":null,"content":"","date":1686560400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1686560400,"objectID":"d0ff691b3242f1ee3ae216c775de4134","permalink":"https://conect-int.github.io/authors/simon-nougaret/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/simon-nougaret/","section":"authors","summary":"","tags":null,"title":"Simon Nougaret","type":"authors"},{"authors":["frederic-y-chavane"],"categories":null,"content":"","date":1677853800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1677853800,"objectID":"79ef82413d16cf2e1a5766e01762cafa","permalink":"https://conect-int.github.io/authors/frederic-y-chavane/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/frederic-y-chavane/","section":"authors","summary":"","tags":null,"title":"Frédéric Chavane","type":"authors"},{"authors":["antoine-grimaldi"],"categories":null,"content":"“Ultra-fast vision using Spiking Neural Networks” (PhD position, 2020-09 / 2023-09) Venue: Aix-Marseille Université with the APROVIS3D grant (ANR-19-CHR3-0008-03)\nKeywords: Vision, Spiking Neural Networks, Bio-Inspired Computer Vision\nThesis director: Dr. Laurent PERRINET, Research unit: Institut de Neurosciences de la Timone (INT)\nmore at https://laurentperrinet.github.io/author/antoine-grimaldi/\n","date":1639134000,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1639134000,"objectID":"c2f0a6a942593f19f40fafaac178a1ee","permalink":"https://conect-int.github.io/authors/antoine-grimaldi/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/antoine-grimaldi/","section":"authors","summary":"“Ultra-fast vision using Spiking Neural Networks” (PhD position, 2020-09 / 2023-09) Venue: Aix-Marseille Université with the APROVIS3D grant (ANR-19-CHR3-0008-03)\nKeywords: Vision, Spiking Neural Networks, Bio-Inspired Computer Vision\nThesis director: Dr. Laurent PERRINET, Research unit: Institut de Neurosciences de la Timone (INT)","tags":null,"title":"Antoine Grimaldi","type":"authors"},{"authors":["david-hansel"],"categories":null,"content":"","date":1618963200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1618963200,"objectID":"cca39b7dabd5880271fac7809eab8503","permalink":"https://conect-int.github.io/authors/david-hansel/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/david-hansel/","section":"authors","summary":"","tags":null,"title":"David Hansel","type":"authors"},{"authors":["anna-montagnini"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"45d66edaf2e796c6cc66249755f036b9","permalink":"https://conect-int.github.io/authors/anna-montagnini/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/anna-montagnini/","section":"authors","summary":"","tags":null,"title":"Anna Montagnini","type":"authors"},{"authors":["David Hansel","Laurent U Perrinet"],"categories":null,"content":"Neuroscience is in revolution: Over the past decade, tremendous technological advances across several disciplines have dramatically expanded the frontiers of experimentally accessible neuroscientific facts.\nBridging across different spatial and temporal scales, combination of in vivo two photon imaging, large population recording-array technologies, optogenetic circuit control tools, transgenic manipulations as well as large volume circuit reconstructions are now used to examine the function, structure and dynamics of neural networks on an unprecedented level of detail and precision. Current applications of these novel techniques include sensory information processing, motor production, neural correlates of learning, memory and decision making as well as mechanisms of dysfunctions and disease. These experiments have begun to produce a huge amount of data, on a broad spectrum of temporal and spatial scales, providing finer and more quantitative descriptions of the biological reality than we would have been able to dream of only a decade ago. The daunting complexity of the biological reality revealed by these technologies highlights the importance of neurophysics to provide a conceptual bridge between abstract principles of brain function and their biological implementations within neural circuits. This revolution is accompanied by a parallel revolution in the domain of Artificial Intelligence. An exponential number of algorithms in sensory processing, such as image classification, or reinforcement learning have realized practical tools which have replaced the classical tools we were using on a daily basis by a novel range of intelligent tools of a new generation. This is the context in which we are creating CONECT.\nWe are convinced that the close collaboration between experimentalists and theoreticians in neuroscience is essential to develop mechanistic as well as quantitative understandings of how the brain performs its functions. This is in fact a primary motivating force in establishing this center. However, for such collaborations to be effective, experimentalists must be well aware of the approaches and challenges in modeling while theoreticians must be well acquainted with the experimental techniques, their power and the challenges they present. CONECT has also the ambition to contribute to the training of a new generation of neuroscientists who will have all these qualities.\nThis approach is therefore complementary but distinct in its purpose from neuroinformatics (creation of tools for analyzing neuroscientific data) or artificial intelligence (creation of algorithms inspired by the functioning of the brain). The field of computational neuroscience is still young but its community is now structured in an autonomous community with strong interaction with the other branches of neuroscience. It is this autonomy that we want to foster at INT.\n","date":1618963200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1618963200,"objectID":"2cc8f1fda97d726e8b395d9e9522c602","permalink":"https://conect-int.github.io/post/about-conect/","publishdate":"2021-04-21T00:00:00Z","relpermalink":"/post/about-conect/","section":"post","summary":"Neuroscience is in revolution: Over the past decade, tremendous technological advances across several disciplines have dramatically expanded the frontiers of experimentally accessible neuroscientific facts.\n","tags":null,"title":"Why CONECT?","type":"post"},{"authors":["Laurent U Perrinet","Matthieu Gilson"],"categories":null,"content":"Within the INT, many components of CONECT already exist, either carried by researchers in computational neurosciences or as themes strongly anchored in this field. A survey of the current situation reveals the existence of projects at different scales.\nContact us to be added! from the cellular to the network level deciphering the biophysical principles underlying robustness of neuronal activity using quantitative genotype-to-phenotype mapping strategies and realistic neuronal model databases (Jean-Marc Goaillard). dynamics and function of small and large-scale neural networks: Laurent Perrinet with Frédéric Chavane, Matthieu Gilson) from networks to mesoscopic levels : Bayesian inference and predictive process models (Anna Montagnini, Emmanuel Daucé and Laurent Perrinet), reinforcement learning, action selection, decision Andrea Brovelli and Emmanuel Daucé), link with attentional mechanisms (Guilhem Ibos) information theory and functional connectivity for the analysis of cognitive brain networks (Andrea Brovelli, Matthieu Gilson) and Bruno Giordano) deep learning for data processing (Bruno Giordano), deep learning + neuroimaging (in voice perception) (Charly Lamothe) computational neuroscience and data processing in neuroinformatics (Sylvain Takerkart, NIT platform) at brain level brain anatomy, particularly as applied to the formation of cortical folding (Julien Lefèvre with Guillaume Auzias, Sylvain Takerkart and Olivier Coulon), the development of prognostic models of the evolution of certain pathologies (Lionel Velly, Sylvain Takerkart), develop the collaboration of theoretical neurosciences with neuroinformatics, notably with the NIT (Sylvain Takerkart, Guillaume Auzias) A structuring of these different components through a center (independent of existing and future teams) would be a major asset to reach a new stage in the creation of INT³.\n","date":1624233600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1624233600,"objectID":"d1158720bba54942ab0824fb7155b3d4","permalink":"https://conect-int.github.io/post/actors-conect/","publishdate":"2021-06-21T00:00:00Z","relpermalink":"/post/actors-conect/","section":"post","summary":"Within the INT, many components of CONECT already exist, either carried by researchers in computational neurosciences or as themes strongly anchored in this field. A survey of the current situation reveals the existence of projects at different scales.\nContact us to be added! ","tags":null,"title":"Actors of CONECT","type":"post"},{"authors":["Matthieu Gilson","Laurent U Perrinet"],"categories":null,"content":"The Computational Neuroscience Center (CONECT) is an incubator within the INT to promote theoretical and computational neuroscience.\nIt ambitions to contribute to the training of a new generation of neuroscientists, following the revolution experienced by neuroscience over the past decades: tremendous technological advances across several disciplines have dramatically expanded the frontiers of experimentally accessible neuro-scientific facts. CONECT is thus concerned with new analysis tools and models that can account for large and complex datasets, in parallel with the NeuroTech Center that focuses on experimental devices.\nCONECT aims to build an inter-disciplinary community within the INT to foster interactions between computational neuroscientists and with experimentalists. The tools include scientific animation (journal clubs, seminars) and practical sessions to leanr and master new tools. This will participate in structuring the teaching and research environment around computational neuroscience around AMU beyond INT alone. The plan is to involve not only local research partners of INT like NeuroMarseille and the Laennec Institute, but also engineer schools (PolyTech, Centrale Marseille) and applied mathematics masters.\n","date":1632182400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1632182400,"objectID":"97612ea19ee0118357cb0a4f1a8bb4e6","permalink":"https://conect-int.github.io/post/objectives-conect/","publishdate":"2021-09-21T00:00:00Z","relpermalink":"/post/objectives-conect/","section":"post","summary":"The Computational Neuroscience Center (CONECT) is an incubator within the INT to promote theoretical and computational neuroscience.\n","tags":null,"title":"Objectives of CONECT","type":"post"},{"authors":["Laurent U Perrinet"],"categories":null,"content":" When: Friday, March 29th 14:30 to 15:30 Where: salle Gastaut (TBC) During this INT/CONECT seminar, Dr Joe MacInnes will present his modelling work on “Casting a wide (neural) net: models and simulations of eye movements and attention”\nAbstract : Eye movements are an excellent proxy for visual attention, and offer a rich source of behavioural data. Decades of neuroscience and experimental results have provided many interesting artefacts that hint at underlying attentional mechanisms. Models and simulations of attention allow us the opportunity to implement our best theories and test them against a wide variety of experimental and imaging results. Simulations of human eye movements, for example, can predict where we allocate attention, the temporal distributions of saccades and even the presence of attentional artifacts like Inhibition of Return, errors, anticipations and even virtual TMS lesions. This talk will cover a couple of recent models that simulate attention and eye movements using deep learning neural nets, spiking network layers and diffusion models to simulate aspects, quirks and mechanisms of human attention.\nJoe has an interdisciplinary PhD from Dalhousie University in Canada combining computer science (graphics and machine learning) with psychology (eye movements and attention). He has worked in psychology departments (University of Toronto, University of Aberdeen, and HSE University Moscow), computer science departments (Dalhousie University, Canada, St Mary’s University, Canada, and Swansea University Wales) and industry research (Data visualization and AI, Canada). He is currently a senior lecturer in AI at Swansea University department of computer science. ","date":1711724400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1711724400,"objectID":"f214628467b696cc2291e10f2083d654","permalink":"https://conect-int.github.io/talk/2024-03-29-int-conect-seminar-by-joe-macinnes/","publishdate":"2024-02-20T06:00:00Z","relpermalink":"/talk/2024-03-29-int-conect-seminar-by-joe-macinnes/","section":"event","summary":"INT-CONECT seminar by Joe MacInnes \"Casting a wide (neural) net: models and simulations of eye movements and attention\".","tags":["events"],"title":"2024-03-29 : INT-CONECT seminar by Joe MacInnes","type":"event"},{"authors":["Matthieu Gilson"],"categories":null,"content":" When: Thursday-Friday 9:00 to 19:00 Where: salle Henri Gastaut All details on https://conect-int.github.io/transint.github.io/\nSpeakers Schedule Venue\nThis workshop is supported by INT and NeuroMarseille. ","date":1710320400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1710320400,"objectID":"945fb2163ab8805414ff31360814c0f6","permalink":"https://conect-int.github.io/talk/2024-march-13-14-workshop-transint/","publishdate":"2024-02-21T09:00:00Z","relpermalink":"/talk/2024-march-13-14-workshop-transint/","section":"event","summary":"INT/CONECT/NeuroMarseille workshop\".","tags":["events"],"title":"2024 March 13-14: Workshop TransINT","type":"event"},{"authors":["Fanny Cazettes"],"categories":null,"content":" When: Monday, March 11th 14:30 to 16:00 Where: salle Laurent Vinay During this CONECT seminar, Danny Burnham will present his recent work on “Mice alternate between inference- and stimulus-bound strategies during probabilistic foraging”\nAbstract : Essential features of the world are often hidden and must be inferred by constructing internal models based on indirect evidence. During foraging, animals must continually choose between trying to exploit a depleting food source at their current location and leaving to explore a new source at the expense of costly travel epochs. In a deterministic environment, the optimal strategy is to leave the current site when the immediate rate of reward drops below the average rate - a stimulus-bound strategy, assigning each action a value that is updated based on its immediate outcome. This strategy, however, is not optimal in a realistic foraging scenario, where rewards are encountered probabilistically and the optimal strategy is inference-bound, requiring the animal to infer the hidden structure of the world. Motivated by recent studies showing that mice alternate between discrete strategies during perceptual decision-making, we test the hypothesis that mouse behavior during a probabilistic foraging task switches between inference- and stimulus-bound strategies within the same session. To this end, we developed a novel hidden Markov model with linear emissions (LM-HMM) to capture this switching dynamic. When applied to mice engaged in the task, the LM-HMM revealed that mice switch between distinct inference bound and stimulus bound strategies exhibiting varying impulsivities.\nDanny Burnham is a computational neuroscientist from the Institute of Neuroscience at the university of Oregon interested in Artificial Neural Network models of learning and memory. ","date":1710169200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1710169200,"objectID":"a14a867c3c76e106c3cc5fd8d2e3fcbc","permalink":"https://conect-int.github.io/talk/2024-03-11-conect-seminar-by-danny-burnham/","publishdate":"2024-01-29T09:00:00Z","relpermalink":"/talk/2024-03-11-conect-seminar-by-danny-burnham/","section":"event","summary":"Conect seminar by Danny Burnham \"Mice alternate between inference- and stimulus-bound strategies during probabilistic foraging\".","tags":["events"],"title":"2024-03-11 : CONECT seminar by Danny Burnham","type":"event"},{"authors":["Bruno L. Giordano"],"categories":null,"content":" When: Tuesday, March 5th 10:00 to 11:00 Where: salle Vinay (R+1) During this INT/CONECT seminar, Prof. Elia Formisano will present his recent work on the neuroscience and computational modelling of natural sounds “Auditory Cognition: Bridging Human and Machine Perspectives”\nAbstract : The ability to recognize and interpret sounds is crucial for both humans and, increasingly, machines. From the chirping of birds to the sirens of emergency vehicles, sound perception allows us to understand events and identify objects, even in challenging contexts like darkness or behind barriers where visual information lacks. Drawing on interdisciplinary research from cognitive psychology, neuroscience, and artificial intelligence (AI), I will discuss current models of how the human brain processes natural sounds, transforming complex acoustic waveforms into meaningful semantic representations. I will then explore potential directions for collaborative developments in AI and neuroscience, framed as a tool for deepening our understanding of the neural computations involved in the extraction of diverse semantic information from naturalistic soundscapes.\nElia Formisano received his MSc degree in Electronic Engineering in 1996 from the University of Naples (Italy) and his PhD from the national (Italian) program in Bioengineering in 2000. Thanks to an outgoing grant, in 1998-1999, he was a visiting research fellow at the Max Planck Institute for Brain Research in Frankfurt/Main. In January 2000, he was appointed Assistant Professor at Maastricht University (Faculty of Psychology and Neuroscience) where he is now Professor of Neuroimaging Methods: Neural Signal Analysis. In 2008-2013, he has been Head of the Department of Cognitive Neuroscience. He is the scientific director of the Maastricht Brain Imaging Center (MBIC), Principal Investigator of the Auditory Perception and Cognition group and founding member of the Maastricht Center for Systems Biology (MaCSBio). His research is supported by several national (e.g. NWO VIDI, VICI, Gravitation) and international funding sources. His research aims at discovering the neural basis of human auditory perception, cognition and plasticity He pioneered the use of ultra-high magnetic field (7 Tesla) functional MRI and multivariate modeling in neuroscience studies of audition. He is actively involved in methods development, focusing on algorithms for unsupervised and supervised learning. On these topics, he has published in high ranked journals, including Science, Neuron, PNAS, Current Biology. He has about 20 years of teaching experience, which includes the development of courses and curricula at bachelor, master and graduate school level on topics of cognitive neuroscience, neuroimaging and biomedical engineering (biomedical signal and image analysis). In 2008-2010, he has been Chair of the Educational Program for the Organization for Human Brain Mapping (OHBM) meetings. Google Scholar ","date":1709650800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1709650800,"objectID":"178fd0e97c84cd4fc188068313281401","permalink":"https://conect-int.github.io/talk/2024-03-29-conect-seminar-by-prof-elia-formisano/","publishdate":"2024-02-21T09:00:00Z","relpermalink":"/talk/2024-03-29-conect-seminar-by-prof-elia-formisano/","section":"event","summary":"CONECT seminar by Prof Elia Formisano \"Auditory Cognition: Bridging Human and Machine Perspectives\".","tags":["events"],"title":"2024-03-29 : CONECT seminar by Prof Elia Formisano","type":"event"},{"authors":["Andrea Brovelli"],"categories":null,"content":" When: Monday 15:00 to 16:00 Where: salle Laurent Vinay During this CONECT seminar, Lorenzo Fontolan will present his recent work on “Neural mechanisms of memory-guided motor learning”\nAbstract : TBA\nLorenzo Fontolan is a computational neuroscientist interested in how neural interactions give rise to cognitive phenomena, how brain circuits change during learning, and how mental disorders disrupt communication pathways in the brain. Currently he is a CENTURI Group Leader at Aix-Marseille University in France. ","date":1696258800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1696258800,"objectID":"d0694bd3149d8573e913832c5ac1be56","permalink":"https://conect-int.github.io/talk/2023-10-02-conect-seminar-by-lorenzo-fontolan/","publishdate":"2023-09-20T09:00:00Z","relpermalink":"/talk/2023-10-02-conect-seminar-by-lorenzo-fontolan/","section":"event","summary":"Conect INT seminar by Lorenzo Fontolan \"Neural mechanisms of memory-guided motor learning\".","tags":["events"],"title":"2023-10-02 : CONECT seminar by Lorenzo Fontolan","type":"event"},{"authors":["Matthieu Gilson"],"categories":null,"content":" When: On Fridays at 11.00am to 12.00am Where: salle Laurent Vinay (INT) This is the first occurrence of monthly sessions on advanced computational tools for neuroscience to occur during the coding club organized by the NIT.\nThis Friday 22th Sept, we will have a look into Optuna, which is a generic-purpose optimizer for hyperparameters. It combines a diversity of modern search algorithms to optimize models in a black-box fashion, namely defining hyperparameters to optimize and an objective/cost function. Check the tutorials for further information.\nCOME TO SHARE AND TEST RECENT COMPUTATIONAL TOOLS FOR NEUROMODELING, ANALYZING YOUR DATA AND MUCH MORE!\nThe general purpose of these sessions is in line with the Coding Club on promoting open science in our daily practice. The focus of the CONECT contributions is on advanced (e.g. python, R, etc.) computational packages of interest for the local neuroscientific community, both theoreticians and experimentalists. Methodological questions like how to model specific neuronal systems and fitting experimental data are also in the scope of these sessions, with again a focus on practical tools related to simulation and analysis.\nTo CONECT members: Propose topics at Coding Club x CONECT. And check the Mattermost channel for the schedule. ","date":1695380400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1695380400,"objectID":"d7333ed471b43f53b13f471bb2d7b278","permalink":"https://conect-int.github.io/talk/2023-09-22-coding-club-x-conect-on-optuna/","publishdate":"2023-09-19T06:00:00Z","relpermalink":"/talk/2023-09-22-coding-club-x-conect-on-optuna/","section":"event","summary":"Coding Club x CONECT this week: Optuna","tags":["events"],"title":"2023-09-22: Coding Club x CONECT on Optuna","type":"event"},{"authors":["Matthieu Gilson"],"categories":[],"content":" Monthly coding club by CONECT Advanced computational tools for neuroscience and open science When? on Friday per month, at 11.00am Where? salle Vinay at INT 2023-09-22 at 11.00am: Optuna Optuna is a generic-purpose optimizer for hyperparameters. It combines a diversity of modern search algorithms to optimize models in a black-box fashion, namely defining hyperparameters to optimize and an objective/cost function. Check the tutorials for further information.\n","date":1695380400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1695380400,"objectID":"5820ff3403ee4bcaa5a9538a3a2bcea0","permalink":"https://conect-int.github.io/slides/2023-09-22-coding-club-conect/","publishdate":"2023-09-19T09:00:00Z","relpermalink":"/slides/2023-09-22-coding-club-conect/","section":"slides","summary":"Advanced computational tools for neuroscience and open science","tags":[],"title":"Monthly coding club by CONECT","type":"slides"},{"authors":["Hugo Ladret","Laurent U Perrinet"],"categories":null,"content":" When: Thursday 2:30pm to 4pm Where: salle Laurent Vinay During this CONECT seminar, Jason Eshraghian will present his recent work on “Making spiking neural networks do useful things”\nThis presentation will dive into how spiking neural networks can be trained to accomplish practical engineering problems. We will provide an overview of the various learning rules that have emerged over the past several decades, along with a few large-scale applications we’ve achieved with spike-based computation. This involves our spike-based language model, SpikeGPT, and our open-source Python library that adopts gradient-based optimization into spike-based models, snnTorch.\nJason K. Eshraghian is an Assistant Professor with the Department of Electrical and Computer Engineering, University of California, Santa Cruz and the maintainer of snnTorch. In addition, we had a master class in the morning on snnTorch, get the notebook (upon request).\n","date":1695306600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1695306600,"objectID":"264661d1ebe7289be68192f1d2303206","permalink":"https://conect-int.github.io/talk/2023-09-21-conect-seminar-by-jason-eshraghian/","publishdate":"2023-09-14T09:00:00Z","relpermalink":"/talk/2023-09-21-conect-seminar-by-jason-eshraghian/","section":"event","summary":"INT seminar by Jason Eshraghian \"Making spiking neural networks do useful things\".","tags":["events"],"title":"2023-09-21 : CONECT seminar by Jason Eshraghian","type":"event"},{"authors":["Matthieu Gilson"],"categories":null,"content":" When: Monday 11th Sept 2023 2.30pm to 3.30pm Where: salle de cours 5, batiment principal Timone- (aile verte) During this CONECT seminar co-organized with Institut de Neurosciences des Systèmes (INS), Taro Toyoizumi will present his recent work on “Modeling the fluctuations and state-dependence of synaptic dynamics”\nAbstract: Adaptive behavior, crucial for thriving in complex environments, is believed to be enabled by activity-dependent synaptic plasticity within neural circuits. In the first part of this talk, I present how synaptic plasticity could be stabilized in the brain. Conventional models of Hebbian plasticity often facilitate connections between coincidentally active neurons and produce pathologically synchronous neural activity. I demonstrate that biologically observed intrinsic synaptic dynamics—activity-independent changes in synapses—can maintain a physiological distribution of synaptic strength and stabilize memory within neural networks. In the second part, I adopt a top-down approach to model synaptic plasticity. Viewing the brain as an efficient information-processing organ, I assume that synaptic weights are updated to transmit information between neurons efficiently. This theory provides insights into the distinct outcomes of synaptic plasticity observed during the up and down states of non-rapid eye movement sleep, thereby shedding light on how memory consolidation may be influenced by the states and spatial scale of slow waves.\nTaro Toyoizumi leads the Lab for Neural Computation and Adaptation at RIKEN Center for Brain Science. ","date":1694442600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1694442600,"objectID":"67efce93a8cdc14b5e3cb0f941785a12","permalink":"https://conect-int.github.io/talk/2023-09-11-conect-seminar-by-taro-toyoizumi/","publishdate":"2023-09-11T09:00:00Z","relpermalink":"/talk/2023-09-11-conect-seminar-by-taro-toyoizumi/","section":"event","summary":"INT seminar by Taro Toyoizumi \"Modeling the fluctuations and state-dependence of synaptic dynamics\".","tags":["events"],"title":"2023-09-11: CONECT seminar by Taro Toyoizumi","type":"event"},{"authors":["Laurent U Perrinet"],"categories":null,"content":"During this CONECT seminar, Adrien Fois did present his recent work on “Plasticity and Temporal Coding in Spiking Neural Networks Applied to Representation Learning”:\nThe brain is a highly efficient computational system, capable of delivering 600 petaFlops while consuming only 20 W of energy, comparable to that of a light bulb. Computation is based on neural impulses, involving information encoding in the form of spikes and learning based on these spikes. According to the dominant paradigm, information is encoded by the number of spikes. However, an alternative paradigm suggests that information is contained in the precise timing of the spikes, offering significant advantages in terms of energy efficiency and information transfer speed.\nMy work aims to extract representations from temporal codes using event-based learning rules that are both spatially and temporally local. In particular, I will present a learning model that learns representations not in synaptic weights, but in transmission delays, which inherently operate in the temporal domain. Learning delays prove to be particularly relevant for processing temporal codes and enable the activation of a key function of spiking neurons: the detection of temporal coincidences.\nAdrien Fois was a PhD Student at the Institute Lorrain de Recherche en Informatique et Ses Applications and now a Post-doc at INT. ","date":1688997600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1688997600,"objectID":"3ea661007ad80e1545a6963467ac0c64","permalink":"https://conect-int.github.io/talk/2023-07-10-conect-seminar-by-adrien-fois/","publishdate":"2023-07-04T09:00:00Z","relpermalink":"/talk/2023-07-10-conect-seminar-by-adrien-fois/","section":"event","summary":"INT seminar by Adrien Fois \"Plasticity and Temporal Coding in Spiking Neural Networks Applied to Representation Learning\".","tags":["events"],"title":"2023-07-10 : CONECT seminar by Adrien Fois","type":"event"},{"authors":["Nicolas Meirhaeghe","Laurent U Perrinet"],"categories":null,"content":"Title: Neural computation through population dynamics Program in construction - you can already check the program of the summer school (June 20 - July 01, 2023) or directly access the detailed program. Question Detecting precise spiking motifs in neurobiological data\nPreliminary program Monday, June 19 – Room 4 at CIELL (Hexagone building – 1st floor) 14 – 16h : introduction par Rosa and Pierre 16h-17h : group project presentation (in Hexagone auditorium) Tuesday, June 20 13h-14h15: group lunch at CROUS (booking in the small room) Tuesday, June 20 to Thursday, June 29 Afternoon 14:30 - 17:00: group projects in Room 4 at CIELL Room 4 at CIELL (Hexagone building- 1st floor) Wednesday, June 28 and Thursday, June 29 Room 4 at CIELL (Hexagone building- 1st floor) All day: Group projects in Institutes Friday, June 30 – HEXAGONE AUDITORIUM 09h30-12h: presentation of group projects 12h-13h30 : group lunch – buffet in the HEXAGONE Hall 13h30: end of the event Challenge At any given instant, hundreds of billions of cells in our brains are lighting up in a complicated yet highly coordinated manner to give rise to our thoughts, percepts, and movements. A single neuron may be connected to thousands of other cells, sending out and receiving information through electrical impulses called spikes. From an engineering perspective, these spikes form a signal that may be viewed as a series of ones and zeros rapidly unfolding in time. Altogether, these signals reflect the ongoing computations taking place inside the nervous system, and as such, constitute a window into the brain’s inner workings. Recent advances in recording techniques have allowed experimenters to collect data from hundreds to thousands of neurons simultaneously while animals perform simple tasks. Dealing with such high-dimensional data poses important technical challenges that require elaborate methods for data mining and analysis. In this project, students will deal with datasets of increasing complexity and develop a set of analyses to extract meaningful information from the data.\nType of data The folowing datasets will be shared by the teaching staff:\npublicly available recordings from a reaching task from Hatsopoulos, Joshi, and O’Leary (2004) doi:10.1152/jn.01245.2003\npublicly available recordings from the dorsomedial frontal cortex of NHPs performing a time-interval reproduction task Meirhaeghe, Sohn, and Jazayeri (2021) doi:10.1016/j.neuron.2021.08.025 - see (https://github.com/jazlab/Meirhaeghe2021).\nMethods Data visualisation, neural decoding, principal component analysis, kinematic and geometric analyses of neural trajectories in high-dimensional space, hypothesis-testing, null distributions and statistics\nResources https://github.com/CONECT-INT/2023_CENTURI-SummerSchool\nhttps://github.com/SpikeAI/2022-11_brainhack_DetecSpikMotifs\n","date":1687269600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1687269600,"objectID":"62928ae196518fb9f5151105fe8c20f5","permalink":"https://conect-int.github.io/talk/2023-06-20-conect-at-the-centuri-summer-school/","publishdate":"2023-02-24T09:00:00Z","relpermalink":"/talk/2023-06-20-conect-at-the-centuri-summer-school/","section":"event","summary":"Title: Neural computation through population dynamics Program in construction - you can already check the program of the summer school (June 20 - July 01, 2023) or directly access the detailed program.","tags":null,"title":"2023-06-20: CONECT at the CENTURI summer school","type":"event"},{"authors":["Nicolas Meirhaeghe","Laurent U Perrinet"],"categories":[],"content":" Neural computation through population dynamics Computational Neuroscience project CENTURI Summer school https://conect-int.github.io/talk/2023-06-20-conect-at-the-centuri-summer-school/\n1 MINUTE\nPress S key to view this project is part of the CENTURI summer school - and we would like to thank the organizers of the school… In this short presentation, we will present the challenges that we want to tackle and which we named… Who are we? NicolasMeirhaeghe LaurentPerrinet 2 MINUTE\nThis project is supervised by NM and myself. We are both at the INT, working at the interface between neurophysiology and computational modelling.\nChallenge: brain decoding 2 MINUTE\nour brains light up billions of cells, in majority carried by action potentials, or spikes, neural activity is structured in a way that allows agents to act on the world we wish to better understand this relationship by using machine learning. In this example, a monkey is seeing a display for which a reaching task is associated. at the same time neural activity (raster plot) is recorded in the premotor area. our goal is to be able to design a computational method to predict the actual behavior. achieving to do this allows to better understand computational principles of the brain\napplication to BCI “what I can build, I can understand” (to be more modest, as Feynman said “What I cannot build. I do not understand.” )\nObjectives Learn computational methods to interpret and interrogate neural data Learn to reduce the complexity of high-dimensional neural data Learn statistical approaches to perform hypothesis-testing on neural data Learn the principles of decoding analyses to relate neural data to behavioral data 2 MINUTES\nThe objectives in this project are: …\nDatasets Dataset 1: reaching task (Hatsopoulos et al., J. Neurophysiol., 2004) Dataset 2: time interval task (Meirhaeghe et al., Neuron, 2021) 1 MINUTE\nDuring the project we will focus on two datasets:\n… which is openly available the second … which will be provided during the course Dataset 1: reaching task Hatsopoulos, Joshi, and O’Leary (2004) doi:10.1152/jn.01245.2003\nGoal: decode intended arm movements from motor cortical activity 1 MINUTE\nThe first dataset is a classic reaching task. it consists of recordings in primary motor (MI) and dorsal premotor (PMd) cortices in behaving monkeys doing a reaching task, that is, instructed to move a cursor from the center to a target.\nDataset 2: time interval task Meirhaeghe, Sohn, and Jazayeri (2021) doi:10.1016/j.neuron.2021.08.025\nGoal: relating neural dynamics to animals’ behavioral performance 1 MINUTE\nthe second dataset is more challenging and involves :\nMonkeys measured time intervals drawn from various distributions Activity in the frontal cortex scaled in time with the mean interval Temporal scaling allowed time to be encoded predictively relative to the mean Dataset 2: time interval task Malvache, Reichinnek, Vilette, Haimerl \u0026amp; Cossart (2016) doi:10.1126/science.aaf3319\nGoal: use precise spike times to improve decoding 2 MINUTE\nour goal is to improve decoding\nInternal representation of hippocampal neuronal population spans a time-distance continuum.\nyet the domain is vast, and there s lot to do in SNNs\nQuestions? home page: https://conect-int.github.io/talk/2022-06-20-conect-at-the-centuri-summer-school/ Contact us @ nicolas.meirhaeghe@univ-amu.fr, laurent.perrinet@univ-amu.fr GitHub repository: https://github.com/CONECT-INT/2023_CENTURI-SummerSchool 1 MINUTE\nwe look forward to start working with you on this project ! Questions? ","date":1687183200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1687183200,"objectID":"151903f4677aed12fa06749da206b647","permalink":"https://conect-int.github.io/slides/2023-06-19-conect-centuri-summer-school/","publishdate":"2023-06-15T09:00:00Z","relpermalink":"/slides/2023-06-19-conect-centuri-summer-school/","section":"slides","summary":"CENTURI Summer school: Computational Neuroscience projet.","tags":[],"title":"Computational Neuroscience projet","type":"slides"},{"authors":["Simon Nougaret","Emmanuel Daucé","Laurent U Perrinet","Matthieu Gilson"],"categories":null,"content":"CONECT Workshop Active learning in brains and machines We organized a one-day workshop on Monday, June 12th, at the Institut de Neurosciences de la Timone (INT), in the Gastaut room (9h-17h), campus Timone. The aim of CONECT one-day workshops was to gather computational neuroscientists and experimentalists around an open question in the field, with plenty of room for interaction and discussion.\nWhen? 12th of June 2023\nWhere? Campus Timone (room Gastaut), Aix-Marseille Université, 27 Boulevard Jean Moulin, 13005 Marseille\nOrganizers: Simon Nougaret, Emmanuel Daucé, Laurent Perrinet (mobile: 0619478120), Matthieu Gilson\nTopics In biology, a major trait of neural systems is the capability to learn, that is, to adapt its behavior to the environment it interacts with. Recent advances in machine learning and deep learning have, in parallel, contributed to formulate learning in terms of optimizing performance under task-specific domains.\nWhile each field inspires the other, there is still a gap in our understanding of how learning in machines may compare or even relate to learning in biology. The goal of this workshop is to allow people from both computational and experimental sides to understand current research achievements and challenges about active learning in brains and machines.\nPROGRAM 9:00 : Welcome \u0026amp; Introduction\nSession 1 : Encoding of neuronal representations (chair: Emmanuel Daucé) 9:15 : Alexandre Pitti (ETIS, CY-U, Cergy Pontoise): “Neuro-inspired mechanisms for sensorimotor and syntactic learning in language”\n9:55 : Laurie Mifsud \u0026amp; Matthieu Gilson (INT, Marseille) “Statistical learning in bio-inspired neuronal network”\n10:15 : Antoine Grimaldi (INT, Marseille) “Learning in networks of spiking neurons with heterogeneous delays”\n10:35 : coffee break\nSession 2 : Learning action selection (chair: Matthieu Gilson) 11:00 : Jorge Ramirez Ruiz (Univ Pompeu Fabra, Barcelona) “Path occcupancy maximization principle”\n11:40 : Nicolas Meirhaeghe (INT, Marseille) : “Bayesian Computation through Cortical Latent Dynamics”\n12:00 : Emmanuel Daucé \u0026amp; Hamza Oueld (INT, Marseille) : “Principles of model-driven active sampling in the brain”\n12:30 : Meal\nSession 3 : Neuronal basis of vocal representation (chair: Laurent Perrinet) 14:00 : Thomas Schatz (LIS, Marseille): “Perceptual development, unsupervised representation learning and auditory neuroscience”\n14:40 : Charly Lamothe (LIS/INT, Marseille) \u0026amp; Etienne Thoret (PRISM/LIS/ILCB, Marseille): “Decoding voice identity from brain activity”\n15:00 : coffee break\nSession 4 : Distribution and integration of brain functions (chair: Simon Nougaret) 15:30 : Jean-Rémi King (Meta / CNRS): “Language in the brain and algorithms”\n16:10 : Etienne Combrisson \u0026amp; Andrea Brovelli (INT, Marseille) : “Cortico-cortical interactions for goal-directed causal learning”\n16:30 : Round table\nAbstracts Andrea Brovelli: “Cortico-cortical interactions for goal-directed learning” During my presentation, I will provide a concise overview of two recent studies that explore the significance of cortico-cortical interactions in goal-directed learning and the processing of outcome-related learning computations, specifically prediction errors. In the first study, we examined the interaction between human prefrontal and insular regions during reward and punishment learning. Using intracranial EEG recordings to measure high-gamma activity (HGA) and leveraging advancements in information theory, we discovered a functional distinction in inter-areal interactions between reward and punishment learning. A reward subsystem with redundant interactions was observed between the orbitofrontal and ventromedial prefrontal cortices, where the ventromedial prefrontal cortex played a Granger-causality driving role. Additionally, we identified a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with the insula acting as the primary driver. Furthermore, we found that the encoding of both reward and punishment prediction errors was mediated by synergistic interactions between these two subsystems. In the second study, we investigated the spatio-temporal characteristics of cortico-cortical interactions that support learning-related variables, such as reward-related signals (Bayesian surprise). Our results revealed the involvement of a distributed network comprising the visual, lateral prefrontal, and orbitofrontal cortex. Preliminary findings also indicated the presence of higher-order synergistic interactions that emerge from the combined activation of these networks.\nEmmanuel Daucé \u0026amp; Hamza Oueld : “Principles of model-driven active sampling in the brain” Understanding our environment requires not only passively observing sensory samples, but also acting to seek out useful relationships between our actions and their possible outcomes. Inspired by the concept of “visual salience”, we provide a way to interpret action selection as making an “ideal experiment”, in a behavioral task …","date":1686560400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1686560400,"objectID":"506e1b14b350be8e5af39e69eed3760e","permalink":"https://conect-int.github.io/talk/2023-06-12-conect-workshop-on-learning/","publishdate":"2023-05-23T14:00:00Z","relpermalink":"/talk/2023-06-12-conect-workshop-on-learning/","section":"event","summary":"CONECT Workshop Active learning in brains and machines We organized a one-day workshop on Monday, June 12th, at the Institut de Neurosciences de la Timone (INT), in the Gastaut room (9h-17h), campus Timone.","tags":null,"title":"2023-06-12: CONECT Workshop on Learning","type":"event"},{"authors":["Laurent U Perrinet"],"categories":null,"content":"This meet-up was focused on discussing recent developments in Spiking Neural Networks, with plenty of time for discussion.\nWe met at INT, Laurent Vinay meeting room. program 10:00\nLaurent Perrinet Title: A short intro on Precise Spiking Motifs in Neurobiological and Neuromorphic Data Slides: https://conect-int.github.io/slides/2023-03-28-conect-seminar-day-on-snns/ 10:30\nSander Bohte Title: Scaling Up Spiking Neural Networks with Online Learning in Gated Spiking Neurons 11:30\nAntoine Grimaldi, PhD student (INT) Title: Learning heterogeneous delays in a layer of spiking neurons for fast motion detection The response of a biological neuron depends on the precise timing of afferent spikes. This temporal aspect of the neuronal code is essential in understanding information processing in neurobiology and applies particularly well to the output of neuromorphic hardware such as event-based cameras. However, most artificial neuronal models do not take advantage of this minute temporal dimension. Inspired by this neuroscientific observation, we develop a model for the efficient detection of temporal spiking motifs based on a layer of spiking neurons with heterogeneous delays which we apply to the computer vision task of motion detection. Indeed, the variety of synaptic delays on the dendritic tree allows synchronizing synaptic inputs as they reach the basal dendritic tree. We show this can be formalized as a time-invariant logistic regression which can be trained using labeled data. We apply this model to solve the specific computer vision problem of motion detection, and demonstrate its application to synthetic naturalistic videos transformed into event streams similar to the output of event-based cameras. In particular, we quantify how the accuracy of the model can vary with the total computational load. This end-to-end event-driven computational brick could help improve the performance of future spiking neural network algorithms and their prospective use in neuromorphic chips. 12:00 Lunch time (at INT R+4, will be provided only for people registered below)\n14:00\nPr. Benoît Miramond (LEAT, Université Côte d’Azur) Title: Estimating Energy Efficiency of Spiking Neural Networks on neuromorphic hardware Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks, but few works have proven these claims to be true. In this work, we present a metric to estimate the energy consumption of SNNs independently of a specific hardware. We then apply this metric on SNNs processing three different data types (static, dynamic and event-based) representative of real-world applications. As a result, all of our SNNs are 6 to 8 times more efficient than their FNN counterparts. 15:00 break\n15:30\nDr. Andrea Castagnetti (LEAT, Université Côte d’Azur) Title: Trainable quantization for Speedy Spiking Neural Networks Spiking neural networks are considered as the third generation of Artificial Neural Networks. SNNs perform computation using neurons and synapses that communicate using binary and asynchronous signals known as spikes. They have attracted significant research interest over the last years since their computing paradigm allows theoretically sparse and low-power operations. This hypothetical gain, used from the beginning of the neuromorphic research, was however limited by three main factors: the absence of an efficient learning rule competing with the one of classical deep learning, the lack of mature learning framework, and an important data processing latency finally generating energy overhead. While the first two limitations have recently been addressed in the literature, the major problem of latency is not solved yet. Indeed, information is not exchanged instantaneously between spiking neurons but gradually builds up over time as spikes are generated and propagated through the network. This presentation focuses on quantization error, one of the main consequence of the SNN discrete representation of information. We propose an in-depth characterization of SNN quantization noise. We then propose a end-to-end direct learning approach based on a new trainable spiking neural model. 16:00\nYann Cherdo, PhD student (LEAT, Université Côte d’Azur - Renault) Title: HTM and SNN for a bio inspired time series forecasting In the recent years, Spiking Neural Networks have gain much attention from the research community. They can now be trained using the powerful gradient descent and have drifted from the neuroscience to the Machine Learning community. An abundant literature shows that they can perform well on classical Artificial Intelligence tasks such as image or signal classification while consuming less energy than state-of-the-art models like Convolutional Neural Networks. Yet, there is very little work about their performance on unsupervised anomaly detection and time-series prediction. Indeed, the processing of such …","date":1679997600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1679997600,"objectID":"89afbb8e87b5af47050fe0c901d2d5ba","permalink":"https://conect-int.github.io/talk/2023-03-28-conect-thematic-day-on-spiking-neural-networks/","publishdate":"2023-03-24T09:00:00Z","relpermalink":"/talk/2023-03-28-conect-thematic-day-on-spiking-neural-networks/","section":"event","summary":"This meet-up was focused on discussing recent developments in Spiking Neural Networks, with plenty of time for discussion.\nWe met at INT, Laurent Vinay meeting room. program 10:00\nLaurent Perrinet Title: A short intro on Precise Spiking Motifs in Neurobiological and Neuromorphic Data Slides: https://conect-int.","tags":null,"title":"2023-03-28: CONECT thematic day on Spiking Neural Networks","type":"event"},{"authors":["Laurent U Perrinet"],"categories":[],"content":" Spiking Neural Networks CONECT thematic day 1 MINUTE\nPress S key to view Hi, I am LP and in the name of CONECT, we look forward to discuss on SNNs as part of the CONECT… In this short presentation, we will present the challenges that we want to tackle and which we named… CONECT: Computational Neuroscience Center @ INT\n2 MINUTE\n-so, what is CONECT?\nCONECT is Computational Neuroscience Center @ INT, bringing together a core of theoretician\naims at making bridges in neuroscience\nand across the community\nChallenge: Visual latencies Thorpe \u0026amp; Fabre-Thorpe, 2001\n1 MINUTE\nIn particular in our group, we are interested in dynamics of neural processing\nThe visual system is very efficient in generating a decision from the retinal image to the different stages of the visual pathways, here for a macaque monkey, a reaction of finger muscles in about 300 milliseconds.\nthe process of categorizing an object takes 10 layers\nChallenge: Visual latencies Review on Precise Spiking Motifs\n1 MINUTE\nthe latencies are of similar in the human brain but merely scaled due to the brain size\nas a consequence, it is thought that this efficiency is achieved by spikes that is, brief all-or-none events which are passed in the very large network which forms the brain from assemblies of neurons to others.\nKey: Spiking Neural Networks Mainen Sejnowski, 1995\n2 MINUTE\nreproduucibility Key: Spiking Neural Networks Diesmann et al. 1999\n2 MINUTE\n“This hypothesis is reviewed with respect to our knowledge of the neurobiology, for instance in the hippocampus of rodents. We also review Hypothesis: Spiking motifs Review on Precise Spiking Motifs\n2 MINUTE\nThis hypothesis is reviewed with respect to our knowledge of the neurobiology, for instance in the hippocampus of rodents. We also review Hypothesis: Spiking motifs 2 MINUTE\nnumerous and extensive work on mechanisms which may allow the neural system to learn to actually use that precise spiking motifs by attuning the delay between pairs of neurons. Hypothesis: Spiking motifs Review on Precise Spiking Motifs\n2 MINUTE\nIzhikevich polychronization\nyet the domain is vast, and there s lot to do in SNNs\nToday’s program… SanderBohte AntoineGrimaldi BenoitMiramond AndreaCastagnetti YannCherdo Program \u0026amp; more\n2 MINUTE\n","date":1679997600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1679997600,"objectID":"2b8caa468b3d067a98bf3f5564e8b617","permalink":"https://conect-int.github.io/slides/2023-03-28-conect-seminar-day-on-snns/","publishdate":"2023-03-27T10:00:00Z","relpermalink":"/slides/2023-03-28-conect-seminar-day-on-snns/","section":"slides","summary":"CONECT thematic day on Spiking Neural Networks.","tags":[],"title":"CONECT thematic day on Spiking Neural Networks","type":"slides"},{"authors":["Frédéric Chavane"],"categories":null,"content":"During this INT seminar, Andrea Alamia will present his recent work on “Interpreting oscillations as travelling waves: the role of alpha-band oscillations in cognition”:\nIn this talk, I will present three studies that characterize oscillatory travelling waves in the framework of Predictive Coding. In the first study, I’ll introduce a simple model of the visual cortex based on predictive coding mechanisms, in which physiological communication delays between levels generate alpha-band rhythms. Interestingly, these oscillations propagate as traveling waves across levels, both forward (during visual stimulation) and backward (during rest). Remarkably, experimental EEG data matched the predictions of our model. The second study refines the results of the first one, demonstrating that the direction of propagation of alpha-band waves is task dependent. Specifically, forward waves (from occipital to frontal regions) prevail during visual processing, whereas backward waves (from frontal to occipital areas) occur predominantly without visual stimulation. The last study explores the effect of a powerful psychedelics drug, N,N, Dimethyltryptamine (DMT), on alpha-band oscillations, considering a model proposed in the literature based on Predictive Coding. Despite participants being in the eye-closed condition, DMT elicits a spatio-temporal pattern of cortical activation (i.e. travelling waves) similar to that produced by visual stimulation, in line with the predictions of the proposed model. Lastly, I’ll show some preliminary results about the role of oscillatory traveling waves in schizophrenic patients, interpreting the results in the light of Predictive Coding.\nAndrea Alamia is a CNRS researcher at the Brain and Cognition Research Center (CerCo) in Toulouse (France). ","date":1677853800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1677853800,"objectID":"6604a9fc89d3edef1ed8e46409aee04c","permalink":"https://conect-int.github.io/talk/2023-03-03-int-seminar-by-andrea-alamia/","publishdate":"2023-01-13T09:00:00Z","relpermalink":"/talk/2023-03-03-int-seminar-by-andrea-alamia/","section":"event","summary":"INT seminar by Andrea Alamia \"Interpreting oscillations as travelling waves: the role of alpha-band oscillations in cognition\".","tags":["events"],"title":"2023-03-03 : INT seminar by Andrea Alamia","type":"event"},{"authors":["Laurent U Perrinet"],"categories":null,"content":"During this CONECT seminar, Guillaume Dumas will present his recent work on “Multilevel Development of Cognitive Abilities in an Artificial Neural Network”:\nSeveral neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the physical and socio-cultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious manner. The third and cognitively highest level handles the information globally and consciously. It is based on the Global Neuronal Workspace (GNW) theory and is referred to as conscious level. We use trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory-inhibitory ratio increases.\nMore info: https://www.pnas.org/doi/10.1073/pnas.2201304119\nKeywords: computational biology, dynamical systems, medical machine learning (ML), neuroscience, AI ethics\nGuillaume Dumas is an Associate Professor of Computational Psychiatry in the Faculty of Medicine at the Université de Montréal, and the Principle Investigator of the Precision Psychiatry and Social Physiology laboratory at the CHU Sainte-Justine Research Center. He holds the IVADO professorship for “AI in Mental Health”, and the FRQS J1 in “AI and Digital Health”. ","date":1672934400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672934400,"objectID":"e0cdbb8d22d0675c5c449658c1c7ddb0","permalink":"https://conect-int.github.io/talk/2023-01-05-conect-seminar-by-guillaume-dumas/","publishdate":"2022-12-24T09:00:00Z","relpermalink":"/talk/2023-01-05-conect-seminar-by-guillaume-dumas/","section":"event","summary":"CONECT seminar by Guillaume Dumas \"Multilevel Development of Cognitive Abilities in an Artificial Neural Network\".","tags":["events"],"title":"2023-01-05 : CONECT seminar by Guillaume Dumas","type":"event"},{"authors":["Matthieu Gilson","Laurent U Perrinet"],"categories":null,"content":" TL;DR This project aims to develop a method for the automated detection of repeating spiking motifs, possibly noisy, in ongoing activity. Results are available on the shared repo: https://github.com/SpikeAI/2022-11_brainhack_DetecSpikMotifs\nMattermost channel Description Leaders Matthieu Gilson - https://github.com/MatthieuGilson Laurent Perrinet - https://github.com/LaurentPerrinet Collaborators Hugo Ladret George Abitbol Brainhack Global 2022 Event Brainhack Marseille supported by the Polychronies grant Project Description The study of spatio-temporal correlated activity patterns is very active in several fields related to neuroscience, like machine learning in vision (Muller Nat Rev Neurosci 2018) and neuronal representations and processing (Shahidi Nat Neurosci 2019). This project aims to develop a method for the automated detection of repeating spiking motifs, possibly noisy, in ongoing activity. A diversity of formalizations and detection methods have been proposed and we will focus on several example measures for event/spike trains, to be compared on both synthetic and real data.\nAn implementation could be based on autodifferentiable networks as implemented in Python libraries like pytorch. This framework allows for the tuning of parameters with specific architectures like convolutional layers that can capture various timescales in spike patterns (e.g. latencies) in an automated fashion. Another recent tool based on the estimation of firing probability for a range of latencies has been proposed (Grimaldi ICIP 2022). This will be compared with existing approaches like Elephant’s SPADE or decoding techniques based on computed statistics computed on smoothed spike trains (adapted from time series processing, see (Lawrie, biorxiv).\nOne part concerns the generation of realistic synthetic data producing spike trains which include spiking motifs with specific latencies or comodulation of firing rate. The goal is to test how these different structures, which rely on specific assumptions about e.g. stationarity or independent firing probability across time, can be captured by different detection methods.\n","date":1669626000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1669626000,"objectID":"1138cff36b9a221e820b3aa96cbad717","permalink":"https://conect-int.github.io/talk/2022-11-28-conect-at-the-int-brainhack-automatic-detection-of-spiking-motifs-in-neurobiological-data/","publishdate":"2022-11-28T09:00:00Z","relpermalink":"/talk/2022-11-28-conect-at-the-int-brainhack-automatic-detection-of-spiking-motifs-in-neurobiological-data/","section":"event","summary":"TL;DR This project aims to develop a method for the automated detection of repeating spiking motifs, possibly noisy, in ongoing activity. Results are available on the shared repo: https://github.com/SpikeAI/2022-11_brainhack_DetecSpikMotifs","tags":null,"title":"2022-11-28: CONECT at the INT brainhack: Automatic detection of spiking motifs in neurobiological data","type":"event"},{"authors":["Laurent U Perrinet"],"categories":null,"content":"During this CONECT seminar, Bruno Cessac did present his recent work on “Retinal processing: Insights from mathematical modelling”:\nThe retina is the entrance of the visual system. Although based on common biophysical principles, the dynamics of retinal neurons are quite different from their cortical counterparts, raising interesting problems for modellers. In this paper, I address some mathematically stated questions in this spirit, discussing, in particular: (1) How could lateral amacrine cell connectivity shape the spatio-temporal spike response of retinal ganglion cells? (2) How could spatio-temporal stimuli correlations and retinal network dynamics shape the spike train correlations at the output of the retina? These questions are addressed, first, introducing a mathematically tractable model of the layered retina, integrating amacrine cells’ lateral connectivity and piecewise linear rectification, allowing for computing the retinal ganglion cells receptive field together with the voltage and spike correlations of retinal ganglion cells resulting from the amacrine cells networks. Then, I review some recent results showing how the concept of spatio-temporal Gibbs distributions and linear response theory can be used to characterize the collective spike response to a spatio-temporal stimulus of a set of retinal ganglion cells, coupled via effective interactions corresponding to the amacrine cells network. On these bases, I briefly discuss several potential consequences of these results at the cortical level. Keywords: retinal network; visual system; spatio-temporal spike correlations; linear response; non stationarity\nMy research was initially modeling and analysis of large sized dynamical systems arising in various fields such as physics, biology, sociology, computers networks. I have worked on subjects such as self-organized criticality, linear response in chaotic systems, social networks, communications networks. My main interest concerns neuronal networks dynamics. I have developed methods combining dynamical systems theory, statistical physics and ergodic theory allowing to classify dynamics arising in canonical neuronal networks models like integrate and fire models or firing rate models. I have applied these methods for the study of synaptic and intrinsic plasticity, dynamical learning, spike coding, spike train statistics analysis, mean-field dynamics. I am now involved in developing models for the visual system, especially the retina, as well as numerical methods and software for neuroscientists. ","date":1669298400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1669298400,"objectID":"082e87d16ccbb0763d3ee08599f08e2d","permalink":"https://conect-int.github.io/talk/2022-11-24-conect-seminar-by-bruno-cessac/","publishdate":"2022-11-24T09:00:00Z","relpermalink":"/talk/2022-11-24-conect-seminar-by-bruno-cessac/","section":"event","summary":"CONECT seminar by Bruno Cessac \"Retinal processing: Insights from mathematical modelling\".","tags":["events"],"title":"2022-11-24 : CONECT seminar by Bruno Cessac","type":"event"},{"authors":["Andrea Brovelli"],"categories":null,"content":"We are pleased to invite you to a mini-workshop organized as a scientific activity of the Computational Neuroscience Center (CONECT) of the INT focused on “Higher-order interactions in brain networks: from theory to data analysis”.\nTwo speakers will present their work from two complementary theoretical perspectives, emerged from recent advances in network science (Giovanni Petri) and information theory (Daniele Marinazzo).\nIf you are interested in the topic and/or want to learn something new, please join us in the salle Laurent Vinay on Wednesday 5th October 2022 at 14:30.\nHere are the titles and abstracts of the two presentations:\nGiovanni Petri (Turin Univ) https://lordgrilo.github.io/\nhttps://scholar.google.co.uk/citations?user=jb__2PIAAAAJ\u0026amp;hl=en\nTitle: Between higher-order mechanisms and phenomena\nAbstract: Complex networks have become the main paradigm for modelling the dynamics of complex interacting systems. However, networks are intrinsically limited to describing pairwise interactions, whereas real-world systems are often characterized by higher-order interactions involving groups of three or more units. Higher-order structures, such as hypergraphs and simplicial complexes, are therefore a better tool to map the real organization of many social, biological and man-made systems. At the same time, higher-order observables, typically topological or information-theoretic in nature and often sharing the same simplicial language, have been gathering attention for their capacity to capture properties of complex systems that are invisible to standard statistical descriptions. This had led to a certain confusion between these two facets, mechanisms on one side, phenomena on the other. Here, using recent examples from both computational modeling and neuroimaging analysis, I highlight collective behaviours induced by higher-order interactions, their interface with recent advances in topological data analysis, and finally outline three key challenges for the physics of higher-order complex systems.\nDaniele Marinazzo (Ghent Univ) https://users.ugent.be/~dmarinaz/\nhttps://scholar.google.com/citations?user=OJbWSLoAAAAJ\u0026amp;hl=en\nTitle: Two is company, three is a party, and how not to get lost in big parties: practical considerations on higher-order data analysis.\nAbstract: I will make the case for looking for high-order interactions in (neural) data, and for trying to do so in a principled yet feasible way (who needs yet another axiom?). I will then loop back and show how we can find low-order descriptors of high-order interactions (we’re not good in figuring things in high dimensions).\n","date":1664980200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664980200,"objectID":"8186249370ae0a874089d9126a7862ec","permalink":"https://conect-int.github.io/talk/2022-10-05-conect-workshop-higher-order-interactions-in-brain-networks-from-theory-to-data-analysis/","publishdate":"2022-09-12T09:00:00Z","relpermalink":"/talk/2022-10-05-conect-workshop-higher-order-interactions-in-brain-networks-from-theory-to-data-analysis/","section":"event","summary":"CONECT workshop \"Higher-order interactions\" (Petri \u0026 Marinazzo)","tags":["events"],"title":"2022-10-05 : CONECT workshop \"Higher-order interactions in brain networks: from theory to data analysis\"","type":"event"},{"authors":["Laurent U Perrinet"],"categories":null,"content":"During this CONECT seminar, Charlie Sexton will present his recent work on “Spike-timing dependent plasticity among multiple layers of motion-sensitive neurons: a feedforward mechanism for motion extrapolation”:\nThe ability of the brain to represent the external world in real-time is impacted by the fact that neural processing takes time. Because neural delays accumulate as information progresses through the visual system, representations encoded at each hierarchical level are based upon input that is progressively outdated with respect to the external world. This is particularly relevant to the task of localizing a moving object – because the object’s location changes with time, neural representations of its location potentially lag behind its true location. It has therefore been proposed that the visual system utilizes the predictive nature of motion to extrapolate moving objects along their trajectory. Burkitt and Hogendoorn (2021, https://doi.org/10.1523/JNEUROSCI.2017-20.2021) showed how spike-timing dependent plasticity (STDP) can achieve motion extrapolation in a two-layer, feedforward network of velocity-tuned neurons, by shifting the receptive-fields of second-layer neurons in the opposite direction to a moving stimulus. The current study extends this work by implementing two important changes to the network to bring it more into line with biology: we expanded the network to multiple layers to reflect the depth of the visual hierarchy, and we implemented more realistic synaptic time-courses. We examine the degree to which STDP can facilitate compensation of neural delays across six layers, and show that the multi-layer network achieves cumulative compensation comparable in magnitude to the delays incurred in visual processing. We also explore the effect of additional delays imposed on the network by the integration time of the membrane potential.\nMore about Charlie: So far in my PhD I have looked at how spike-timing dependent plasticity (STDP) can shift feedforward connection strengths between levels of the visual hierarchy, such that higher levels encode moving objects further along their trajectory (as a mechanism for motion extrapolation). The project began with a paper by my supervisors, Hinze Hogendoorn and Tony Burkitt: https://www.jneurosci.org/content/41/20/4428.abstract. I have been working on extending this model network to have 6 layers to see the combined effect of plasticity at several visual areas. I will attach my abstract for my ECVP talk here. ","date":1662645600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1662645600,"objectID":"d9763f5852dd98f6d2d3f0c3455ca5d2","permalink":"https://conect-int.github.io/talk/2022-09-08-conect-seminar-by-charlie-sexton/","publishdate":"2022-09-02T09:00:00Z","relpermalink":"/talk/2022-09-08-conect-seminar-by-charlie-sexton/","section":"event","summary":"CONECT seminar by Charlie Sexton \"Spike-timing dependent plasticity among multiple layers of motion-sensitive neurons: a feedforward mechanism for motion extrapolation\".","tags":["events"],"title":"2022-09-08 : CONECT seminar by Charlie Sexton","type":"event"},{"authors":["Nicolas Meirhaeghe","Laurent U Perrinet"],"categories":null,"content":"Title: Neural computation through population dynamics The CENTURI #SummerSchool 2022 has been launched this morning ! Rosa Cossart has welcomed all the participants this morning.👾\nExcited to begin these two weeks of transfer of knowledge and deep thinking at @univamu\n!👏\nFor more info➡️https://t.co/K2oyuQWsmb pic.twitter.com/bAldJeyssj\n— CENTURI - Turing Centre for Living Systems (@centuri_ls) June 20, 2022 The summer school is now over! You can check the program of the summer school (June 20 - July 01, 2022)! Question How does neural population dynamics relate to behaviorally-relevant computations?\nChallenge At any given instant, hundreds of billions of cells in our brains are lighting up in a complicated yet highly coordinated manner to give rise to our thoughts, percepts, and movements. A single neuron may be connected to thousands of other cells, sending out and receiving information through electrical impulses called spikes. From an engineering perspective, these spikes form a signal that may be viewed as a series of ones and zeros rapidly unfolding in time. Altogether, these signals reflect the ongoing computations taking place inside the nervous system, and as such, constitute a window into the brain’s inner workings. Recent advances in recording techniques have allowed experimenters to collect data from hundreds to thousands of neurons simultaneously while animals perform simple tasks. Dealing with such high-dimensional data poses important technical challenges that require elaborate methods for data mining and analysis. In this project, students will deal with datasets of increasing complexity and develop a set of analyses to extract meaningful information from the data.\nType of data Data that will be shared by the teaching staff, under the BIDS standard data organization which is currently being extended to electrophysiology data by the members of the INT and the CONECT team. The data we will use consists of:\npublicly available recordings from the dorsomedial frontal cortex of NHPs performing a time-interval reproduction task (https://github.com/jazlab/Meirhaeghe2021)\npublicly available recordings from the motor cortex (M1/PMd) during an instructed reach-to-grasp task (https://www.nature.com/articles/sdata201855, available at the following URL in BIDS: https://gin.g-node.org/sprenger/multielectrode_grasp/src/bep_animalephys)\nhttps://www.biorxiv.org/content/10.1101/2021.03.30.437692v5 : V1, gratings-like, natural stimulations : extracellular electrophy recordings in cat V1\nMethods Data visualisation, neural decoding, principal component analysis, kinematic and geometric analyses of neural trajectories in high-dimensional space, hypothesis-testing, null distributions and statistics\nResources https://github.com/CONECT-INT/2022_CENTURI-SummerSchool_private https://github.com/CONECT-INT/2022_CENTURI-SummerSchool ","date":1655733600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1655733600,"objectID":"8de6e7db412c31966c615e0f08226652","permalink":"https://conect-int.github.io/talk/2022-06-20-conect-at-the-centuri-summer-school/","publishdate":"2022-02-24T09:00:00Z","relpermalink":"/talk/2022-06-20-conect-at-the-centuri-summer-school/","section":"event","summary":"Title: Neural computation through population dynamics The CENTURI #SummerSchool 2022 has been launched this morning ! Rosa Cossart has welcomed all the participants this morning.👾\nExcited to begin these two weeks of transfer of knowledge and deep thinking at @univamu","tags":null,"title":"2022-06-20: CONECT at the CENTURI summer school","type":"event"},{"authors":["Nicolas Meirhaeghe","Laurent U Perrinet"],"categories":[],"content":" Computational Neuroscience projet CENTURI Summer school https://conect-int.github.io/talk/2022-06-20-conect-at-the-centuri-summer-school/\n1 MINUTE\nPress S key to view Hi, we are LP and NM and we look forward to start working with you on this project as part of the CENTURI summer school - and we would like to thank the organizers of the school… In this short presentation, we will present the challenges that we want to tackle and which we named… Who are we? NicolasMeirhaeghe LaurentPerrinet 2 MINUTE\nblah blas blah\nChallenge: brain decoding 2 MINUTE\nour brains light up billions of cells in a structured way, neural activity is in majority carried by action potentials, or spikes, we wish to better understand this structure by using machine learning. Objectives Learn computational methods to interpret and interrogate neural data Learn to reduce the complexity of high-dimensional neural data Learn statistical approaches to perform hypothesis-testing on neural data Learn the principles of decoding analyses to relate neural data to behavioral data 2 MINUTES\nblah blas blah\nDatasets Dataset 1: reaching task (Hatsopoulos et al., J. Neurophysiol., 2004) Dataset 2: grasping task (Brochier et al., Sci. Data, 2018) Dataset 3: time interval task (Meirhaeghe et al., Neuron, 2021) 1 MINUTE\nblah blas blah\nDataset 1: reaching task Goal: decode intended arm movements from motor cortical activity Hatsopoulos, Joshi, and O’Leary (2004) doi:10.1152/jn.01245.2003\n1 MINUTE\nblah blas blah\nDataset 2: grasping task Goal: predicting animals’ reaction times from neural preparatory activity Brochier, Zehl, Hao, Duret, Sprenger, Denker, Grün, \u0026amp; Riehle (2018) Scientific Data 5 : 180055. doi:10.1038/sdata.2018.55\n1 MINUTE\nblah blas blah\nDataset 3: time interval task Goal: relating neural dynamics to animals’ behavioral performance Meirhaeghe, Sohn, and Jazayeri (2021) doi:10.1016/j.neuron.2021.08.025 1 MINUTE\nblah blas blah\nQuestions? home page: https://conect-int.github.io/talk/2022-06-20-conect-at-the-centuri-summer-school/ Contact us @ nicolas.meirhaeghe@univ-amu.fr, laurent.perrinet@univ-amu.fr GitHub repository: https://github.com/CONECT-INT/2022_CENTURI-SummerSchool ","date":1655733600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1655733600,"objectID":"7393b3528a91265bfe668067eebfb28e","permalink":"https://conect-int.github.io/slides/2022-06-20-conect-centuri-summer-school/","publishdate":"2022-06-15T09:00:00Z","relpermalink":"/slides/2022-06-20-conect-centuri-summer-school/","section":"slides","summary":"CENTURI Summer school: Computational Neuroscience projet.","tags":[],"title":"Computational Neuroscience projet","type":"slides"},{"authors":["Laurent U Perrinet"],"categories":null,"content":"During this CONECT seminar, David Dahmen did present his recent work on “Global organization of neuronal activity only requires unstructured local connectivity”:\nDahmen D, Layer M, Deutz L, Dąbrowska PA, Voges N, von Papen M, Brochier T, Riehle A, Diesmann M, Grün S, Helias M. Elife. 2022 Jan 20;11:e68422\nDr. David Dahmen is a PostDoc at the Research Centre Jülich (Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience \u0026amp; Institute for Advanced Simulation (IAS-6), Theoretical Neuroscience \u0026amp; JARA-Institut Brain structure-function relationships (INM-10)). ","date":1652364000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1652364000,"objectID":"e82739200a2f955dc9f4fbf490f9a891","permalink":"https://conect-int.github.io/talk/2022-05-12-a-conect-seminar-global-organization-of-neuronal-activity-only-requires-unstructured-local-connectivity-david-dahmen/","publishdate":"2022-02-24T09:00:00Z","relpermalink":"/talk/2022-05-12-a-conect-seminar-global-organization-of-neuronal-activity-only-requires-unstructured-local-connectivity-david-dahmen/","section":"event","summary":"A CONECT seminar by David Dahmen.","tags":["events"],"title":"2022-05-12 : A CONECT seminar \"Global organization of neuronal activity only requires unstructured local connectivity\" (David Dahmen)","type":"event"},{"authors":["Laurent U Perrinet"],"categories":null,"content":"During a seminar at the Institute of Neurosciences Timone in Marseille, Jonathan Vacher will present his recent work on “Unifying Different Psychometric Methods : Theory and Experiment”:\nThe two-alternative forced choice (2AFC) paradigm is one of the main methods used to measure perceptual thresholds and biases. Measurements from a 2AFC experiment can be modelled using signal detection theory (SDT) from which the psychometric function can be derived theoretically. Recent efforts to combine SDT with Bayesian probabilities has linked thresholds and biases to hypothesized prior knowledge and optimal encoding/decoding [1]. From another perspective, the maximum likelihood difference scaling (MLDS) paradigm is a more recent method that allows the experimenter to estimate a perceptual scale that links a physical property to a psychological dimension [2]. Such a perceptual scale is obtained from the comparison of relative differences between pairs of stimuli. Here again, the underlying model can be understood in terms of SDT and Bayesian probabilities. However, no comparison between MLDS and 2AFC measurements has been performed yet. Here, we introduce the theory that unifies those measurements and we present some preliminary experimental results. In this context, we further explore how MLDS measurements could help to understand the perception of more complex textures generated from the statistic of deep neural network features [3].\n[1] Wei, X. X., \u0026amp; Stocker, A. A. (2017). Lawful relation between perceptual bias and discriminability. Proceedings of the National Academy of Sciences, 114(38), 10244-10249.\n[2] Maloney, L. T., \u0026amp; Yang, J. N. (2003). Maximum likelihood difference scaling. Journal of Vision, 3(8):5, 573-585.\n[3] Vacher, J. \u0026amp; Davila, A., Kohn, A. \u0026amp; Coen-Cagli, R. (2021). Texture Interpolation for Probing Visual Perception. Advances in Neural Information Processing Systems, 33.\nDr. Jonathan Vacher was a student at École Normale Supérieure de Cachan (now Saclay) where he pursued a degree in mathematics and applied mathematics. He completed his bachelor’s and master’s degree with a specialty in computational imaging and machine learning (Master MVA). He started a multidisciplinary PhD in mathematics (Paris Dauphine University, CEREMADE) and neuroscience (CNRS, UNIC) under the supervision of Gabriel Peyré and Cyril Monier. Then, Jonathan was a postdoc at Albert Einstein College of Medicine in New-York between 2017 and 2020 while initiating a collaboration with Pascal Mamassian from the Laboratoire des Systèmes Perceptifs ()École Normale Supérieure de Paris) where he is currently a postdoc. ","date":1646404200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1646404200,"objectID":"0bcf6ac719db26e6d5f9ac952df8f30e","permalink":"https://conect-int.github.io/talk/2022-03-04-int-seminar-unifying-different-psychometric-methods-theory-and-experiment-jonathan-vacher/","publishdate":"2021-11-08T13:00:00Z","relpermalink":"/talk/2022-03-04-int-seminar-unifying-different-psychometric-methods-theory-and-experiment-jonathan-vacher/","section":"event","summary":"A seminar by Jonathan Vacher at the Institute of Neurosciences Timone in Marseille (with the CONECT group).","tags":["events"],"title":"2022-03-04 : INT Seminar - \"Unifying Different Psychometric Methods : Theory and Experiment\" (Jonathan Vacher)","type":"event"},{"authors":["Laurent U Perrinet"],"categories":null,"content":"Etienne Thoret (ILCB/PRISM/LIS/AMU) kindly accepted to present his research project during our novel series of CONECT-core © seminars (=seminars open to all but focused on the core theoretical scientific questions of the CONECT members):\nExplainable AI for computational auditory neurosciences\nMachine learning and deep neural networks have been raised as compelling models to simulate a broad range of tasks on signals: from classification of sound events to the prediction of human physiological state from electrophysiological data. But what do we really understand about these models and how do they process the information they have been trained to process? As users, we often use them as tools without precisely understanding their mechanistic and representational underpinnings. In this talk, I’ll present recent works on how we can take part of these computational systems to answer fundamental research mysteries on auditory perception, speech production and cerebral processing. Beyond acoustics and sound perception, these techniques can find applications for the modeling of a variety of systems, including computational vision in robotics, haptics and clinical applications.\n","date":1645711200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1645711200,"objectID":"428f086e882c7b45965fe7237e4b3345","permalink":"https://conect-int.github.io/talk/2022-02-24-conect-seminar-explainable-ai-for-computational-auditory-neurosciences-etienne-thoret/","publishdate":"2022-02-24T13:00:00Z","relpermalink":"/talk/2022-02-24-conect-seminar-explainable-ai-for-computational-auditory-neurosciences-etienne-thoret/","section":"event","summary":"A seminar by Etienne Thoret for the CONECT group: \"Explainable AI for computational auditory neurosciences\".","tags":["events"],"title":"2022-02-24 : CONECT Seminar - \"Explainable AI for computational auditory neurosciences\" (Etienne Thoret)","type":"event"},{"authors":["Antoine Grimaldi","Laurent U Perrinet"],"categories":null,"content":"During a seminar at the Institute of Neurosciences Timone in Marseille, Matteo Saponati, will present his recent work showing that “Sequence anticipation and STDP emerge from a voltage-based predictive learning rule”:\nIntelligent behavior depends on the brain’s ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on predictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local information. Consequently, neurons learn sequences over long timescales and shift their spikes towards the first inputs in a sequence. We show that this mechanism can explain the development of anticipatory motion signaling and recall in the visual system. Furthermore, we demonstrate that the learning rule gives rise to several experimentally observed STDP (spike-timing-dependent plasticity) mechanisms. These findings suggest prediction as a guiding principle to orchestrate learning and synaptic plasticity in single neurons. https://www.biorxiv.org/content/10.1101/2021.10.31.466667v1\nwhen: Friday 10th of December at 11am where: Salle Laurent Vinay at the Institute of Neurosciences Timone. Matteo Saponati is a PhD candidate at Ernst Strüngmann Institute (ESI) for Neuroscience - IMPRS for Neural Circuits. ","date":1639134000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1639134000,"objectID":"03e4fe9de8457e5639d5dfa39e6f76ab","permalink":"https://conect-int.github.io/talk/2021-12-10-conect-seminar-sequence-anticipation-and-stdp-emerge-from-a-voltage-based-predictive-learning-rule-matteo-saponati/","publishdate":"2021-11-30T08:00:00Z","relpermalink":"/talk/2021-12-10-conect-seminar-sequence-anticipation-and-stdp-emerge-from-a-voltage-based-predictive-learning-rule-matteo-saponati/","section":"event","summary":"A seminar by Matteo Saponati at the Institute of Neurosciences Timone in Marseille.","tags":["events"],"title":"2021-12-10 : CONECT seminar - \"Sequence anticipation and STDP emerge from a voltage-based predictive learning rule\" (Matteo Saponati)","type":"event"},{"authors":null,"categories":null,"content":"There will be a third meeting of the CONECT group for the Thursday meeting devoted to the kick-off of these initiatives for INT3. It will consist of an internal meeting (14:00-15:00) and of a talk open to the institute (15:00-16:00).\n","date":1623938400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1623938400,"objectID":"40500105df6a37d6eff5372e56316661","permalink":"https://conect-int.github.io/talk/2021-06-17-conect-meeting-#3/","publishdate":"2021-05-27T09:00:00Z","relpermalink":"/talk/2021-06-17-conect-meeting-#3/","section":"event","summary":"There will be a third meeting of the CONECT group for the Thursday meeting devoted to the kick-off of these initiatives for INT3. It will consist of an internal meeting (14:00-15:00) and of a talk open to the institute (15:00-16:00).","tags":null,"title":"2021-06-17: CONECT meeting #3","type":"event"},{"authors":null,"categories":null,"content":"This is the second meeting of the CONECT group for the Thursday meeting devoted to the kick-off of these initiatives for INT3. It consists of an internal meeting (14:00-15:00) and of a talk open to the institute (15:00-16:00) by Matthias Pessiglione (INSB).\n14:00 - 15:00 coordination on scientific perimeter \u0026amp; objectives\n15:00 - 16:00 - scientific presentation by Matthias Pessiglione (INSB) on his recent work :\n“Computational approach to motivational disorders” a preview in this paper : Why not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases of related interest Les Vacances de Momo Sapiens (see the Le Monde interview on that book ) Zoom link https://univ-amu-fr.zoom.us/j/91200617032?pwd=WHRWNy9kNXoyQWZhUHMzS0RzSW1udz09\n","date":1622124000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1622124000,"objectID":"8f4bf9716445f6039e3217fbd9092adf","permalink":"https://conect-int.github.io/talk/2021-05-27-conect-meeting-talk-by-matthias-pessiglione/","publishdate":"2021-05-27T09:00:00Z","relpermalink":"/talk/2021-05-27-conect-meeting-talk-by-matthias-pessiglione/","section":"event","summary":"This is the second meeting of the CONECT group for the Thursday meeting devoted to the kick-off of these initiatives for INT3. It consists of an internal meeting (14:00-15:00) and of a talk open to the institute (15:00-16:00) by Matthias Pessiglione (INSB).\n","tags":null,"title":"2021-05-27: CONECT meeting - talk by Matthias Pessiglione","type":"event"},{"authors":null,"categories":null,"content":"First meeting of the CONECT group for the Thursday meeting devoted to the kick-off of these initiatives for INT3. It consists of an internal meeting (14:00-15:00) and of a talk open to the institute (15:00-16:00).\n14:00 - 15:00 coordination on scientific perimeter \u0026amp; objectives\npresentation of the working document / round table to discuss these points\nmeans of action / operationalization :\nMaster 2 scholarship (call for topics / we arrive with proposals + application, then selection jury) - possibility of a 3 months extension to arrive at 9 months\nforecast student training day - 2022\n15:00 - 15:30 - pause / café\n15:30 - 16:30 - scientific presentation by Rufin van Rullen on his recent work :\nR VanRullen, A Alamia GAttANet: Global attention agreement for convolutional neural networks - arXiv preprint arXiv:2104.05575, 2021 ","date":1619704800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1619704800,"objectID":"abbd0120b60ec65261994c06c40a6219","permalink":"https://conect-int.github.io/talk/2021-04-29-conect-kick-off-talk-by-rufin-van-rullen/","publishdate":"2021-04-29T14:00:00Z","relpermalink":"/talk/2021-04-29-conect-kick-off-talk-by-rufin-van-rullen/","section":"event","summary":"First meeting of the CONECT group for the Thursday meeting devoted to the kick-off of these initiatives for INT3. It consists of an internal meeting (14:00-15:00) and of a talk open to the institute (15:00-16:00).\n","tags":null,"title":"2021-04-29: CONECT kick-off - talk by Rufin van Rullen","type":"event"},{"authors":null,"categories":null,"content":"Computational neuroscience is an expanding field that is proving to be essential in neurosciences. The aim of this short intensive course will be to provide a common background in computational neuroscience. The course, after a brief historical overview of the field, will focus on the description of a few selected modelling and theoretical approaches that are currently developed, including details about their limits and advantages, and that can be applied to different scales of analysis (from the single neuron to the whole brain). In addition, we will provide a theoretical and a practical session on artificial neuronal networks of spiking neurons.\nObjectives: Understanding how computational modelling can be used to formulate and solve neuroscience problems at different spatial and temporal scales; learning the formal notions of information, encoding and decoding and experimenting their use on specific examples\nWhere: Marseille (France)\nWhat: Session #3 : Realistic spiking neural networks\nComputational Neuroscience Tutorial Friday, April 23, 2021; 9:00-12:30 Objective: Applying the Theory on the eBrains platform https://github.com/albertoarturovergani/CNT-2021 Hands-on practice Friday, April 23, 2021; 14:00-17:00 Objective: replicating Mainen \u0026amp; Sejnowski (1995) github : https://github.com/CONECT-INT/2021-04-23_PhDProgram-course-in-computational-neurosciences/ AMETICE : https://ametice.univ-amu.fr/course/view.php?id=72868#section-4 Hands-on session: reproduction of the article by Mainen \u0026amp; Sejnowski, 1995 The aim of this task is to read a scientific article, to reproduce it with simulations of a neuron and to improve the understanding of the study.\nModalities: students will organize themselves alone, in pairs or in triads to provide a brief in the form of a notebook completed from the model that is provided. Follow the QUESTION tags in the notebook to guide you in this writing. Comments should be made in the notebook (don’t forget to save your changes).\nTools needed: Jupyter, with numpy and matplotlib. These are standard tools and are easily installed on any platform. Other hosted solutions exist:\nebrains / HBP https://deepnote.com/ on GoogleColab https://colab.research.google.com/github/CONECT-INT/2021-04_PhDProgram-neurosciences-computationnelles/blob/master/MainenSejnowski1995.ipynb context The goal of this first task is to create a “raster plot” that shows the reproducibility of a spike train with repetitions of the same stimulus, as in this work in the rodent retina or in the cat cortex (V1). Here, we will attempt to replicate Figure 1 of Mainen \u0026amp; Sejnowski (1995):\nfigure 1 getting to know the tools: numpy and matplotlib we are going to create vectors representing the dynamics of a value as a function of time for that, we create a vector `time’ representing 1 second with a precision of dt=.5ms in a first step, we will create a plot of a spike, a slot \u0026amp; a sinusoid problem definition: leaky-integrate and fire neuron we will simulate 1 neuron for 2 seconds with a precision of dt=1ms for that, we use the equation of a leaky-IF then we show its response to the stimuli created above injection of a noise As in figure 1 of Mainen \u0026amp; Sejnowski (1995), we add a noise to the current injection this noise can be characterized by its amplitude and its characteristic time: what is the impact on the result? what happens when we include an internal noise to the dynamics of the neuron? Appendices an article to read about time in the brain: https://laurentperrinet.github.io/publication/perrinet-19-temps/ direct link\nFrom illusions to visual hallucinations: a door on perception - (slides) - article on visual perception: https://laurentperrinet.github.io/post/2019-06-06-theconversation/ direct link\nModelling spiking neural networks using Brian, Nest and pyNN - (slides)\nTutorial on predictive coding https://laurentperrinet.github.io/talk/2017-06-30-telluride/ https://laurentperrinet.github.io/sciblog/files/2017-06-30_Telluride.html\n","date":1619136000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1619136000,"objectID":"5d9538f50f31dbdb637344d695b63e72","permalink":"https://conect-int.github.io/talk/2021-04-23-phdprogram-course-in-computational-neuroscience/","publishdate":"2021-04-23T00:00:00Z","relpermalink":"/talk/2021-04-23-phdprogram-course-in-computational-neuroscience/","section":"event","summary":"Everything You Always Wanted to Know About Computational Models in Neuroscience (But Were Afraid to Ask): a course provided within the NeuroSchool PhD Program.","tags":null,"title":"2021-04-23 : PhDProgram course in computational neuroscience","type":"event"},{"authors":["Laurent U Perrinet"],"categories":null,"content":"During a seminar at the Institute of Neurosciences Timone in Marseille, Thomas Serre will present his recent work on “Feedforward and feedback processes in visual recognition”:\nProgress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive fields that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture that addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.\nDr. Thomas Serre is an Associate Professor in Cognitive Linguistic and Psychological Sciences and an affiliate of the Carney Institute for Brain Science at Brown University. He received a Ph.D. in Neuroscience from MIT in 2006 and an MSc in EECS from Télécom Bretagne (France) in 2000. His research seeks to understand the neural computations supporting visual perception and has been featured in the BBC series “Visions from the Future” and other news articles (The Economist, New Scientist, Scientific American, IEEE Computing in Science and Technology, Technology Review and Slashdot). Dr. Serre is the Faculty Director of the Center for Computation and Visualization and the Associate Director of the Initiative for Computation in Brain and Mind at Brown University. He also holds an International Chair in AI within the Artificial and Natural Intelligence Toulouse Institute (France). Dr. Serre has served as an area chair and a senior program committee member for top-tier machine learning and computer vision conferences including AAAI, CVPR, and NeurIPS. He is currently serving as a domain expert for IARPA’s Machine Intelligence from Cortical Networks (MICrONS) program and as a scientific advisor for Vium, Inc. He was the recipient of an NSF Early Career Award as well as DARPA’s Young Faculty Award and Director’s Award. ","date":1599832800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1599199200,"objectID":"19085c22ffdbcc99edfc600bed691add","permalink":"https://conect-int.github.io/talk/2020-09-11-conect-seminar-feedforward-and-feedback-processes-in-visual-recognition-t-serre/","publishdate":"2020-09-11T14:00:00Z","relpermalink":"/talk/2020-09-11-conect-seminar-feedforward-and-feedback-processes-in-visual-recognition-t-serre/","section":"event","summary":"A seminar by Thomas Serre at the Institute of Neurosciences Timone in Marseille.","tags":["events"],"title":"2020-09-11 : CONECT seminar - \"Feedforward and feedback processes in visual recognition\" (T Serre)","type":"event"},{"authors":["admin"],"categories":null,"content":"toto Supplementary notes can be added here, including code and math.\n","date":1554595200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1554595200,"objectID":"557dc08fd4b672a0c08e0a8cf0c9ff7d","permalink":"https://conect-int.github.io/publication/preprint/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/preprint/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example preprint / working paper","type":"publication"},{"authors":["David Hansel","Laurent U Perrinet"],"categories":[],"content":" CoNeCt the Computational Neuroscience Center @ INT https://conect-int.github.io\nCONECT with one N Press S key to view Tremendous technological advances two photon imaging large population recording-array technologies optogenetic circuit control tools transgenic manipulations large volume circuit reconstructions Tremendous technological advances over the past decade\nThese experiments have begun to produce a huge amount of data, on a broad spectrum of temporal and spatial scales, providing finer and more quantitative descriptions of the biological reality than we would have been able to dream of only a decade ago. A transdisciplinary revolution across several disciplines (physics, genetics, biology, robotics, psychiatry, ..) and multiple scales (from micro to macro, from short to long-term, from theory to biology) new frontiers … and new challenges daunting complexity of the biological reality revealed by these technologies highlights the importance of neurophysics to provide a conceptual bridge between abstract principles of brain function and their biological implementations within neural circuits. This revolution is accompanied by a parallel revolution in the domain of Artificial Intelligence. An exponential number of algorithms in sensory processing, such as image classification, or reinforcement learning have realized practical tools which have replaced the classical tools we were using on a daily basis by a novel range of intelligent tools of a new generation. This is the context in which we are creating CONECT. CoNeCt: Computational Neuroscience Center close collaboration between experimentalists and theoreticians share state-of-the-art (experimentalists well aware of theoretical approaches, experimental techniques for theoreticians) complementary in its purpose from neuroinformatics… but distinct We are convinced that close collaboration between experimentalists and theoreticians in neuroscience is essential to develop mechanistic as well as quantitative understandings of how the brain performs its functions. This is in fact a primary motivating force in establishing this center. However, for such collaborations to be effective, experimentalists must be well aware of the approaches and challenges in modeling while theoreticians must be well acquainted with the experimental techniques, their power and the challenges they present. CoNeCt has also the ambition to contribute to the training of a new generation of neuroscientists who will have all these qualities. This approach is therefore complementary but distinct in its purpose from neuroinformatics (creation of tools for analyzing neuroscientific data) or artificial intelligence (creation of algorithms inspired by the functioning of the brain). The field of computational neuroscience is still young but its community is now structured in an autonomous community with strong interaction with the other branches of neuroscience. It is this autonomy that we want to foster at INT.\nObjectives of CoNeCt to create a space for scientific discussion and animation\ntrain students and staff and attract young researchers:\nstructuring the network of computational neurosciences at INT, on Timone, on AMU and in France \u0026amp; International\nlots of work - bottom approach so far no action taken lots of work - existence of top-down initiatives Actions of CoNeCt Actors of CONECT Objectives of CONECT Past events and future\nActors we already organized events within or outside INT objectives : exist Questions? https://conect-int.github.io\nContact us @ int-conect@univ-amu.fr!\nLet’s discuss on Mattermost\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"e171be3b9e583158db0915784cf6d55a","permalink":"https://conect-int.github.io/slides/conect/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/conect/","section":"slides","summary":"CONECT: Computational Neuroscience Center.","tags":[],"title":"Slides","type":"slides"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Wowchemy Wowchemy | Documentation\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026#34;blueberry\u0026#34; if porridge == \u0026#34;blueberry\u0026#34;: print(\u0026#34;Eating...\u0026#34;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne Two Three A fragment can accept two optional parameters:\nclass: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\nOnly the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026#34;/media/boards.jpg\u0026#34; \u0026gt;}} {{\u0026lt; slide background-color=\u0026#34;#0000FF\u0026#34; \u0026gt;}} {{\u0026lt; slide class=\u0026#34;my-style\u0026#34; \u0026gt;}} Custom CSS Example Let’s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://conect-int.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Wowchemy's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":["admin","Robert Ford"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1441065600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1441065600,"objectID":"966884cc0d8ac9e31fab966c4534e973","permalink":"https://conect-int.github.io/publication/journal-article/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/journal-article/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example journal article","type":"publication"},{"authors":["admin","Robert Ford"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1372636800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1372636800,"objectID":"69425fb10d4db090cfbd46854715582c","permalink":"https://conect-int.github.io/publication/conference-paper/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/conference-paper/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. 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