- Comp-neuro - lists.cnsorg.org

Ph.D. student / Postdoc position in machine learning with spiking neurons at University of Bremen, Germany
by David Rotermund Aug. 30, 2021

Aug. 30, 2021

tl;dr: You looked at our paper Back-Propagation Learning in Deep Spike-By-Spike Networks ( https://www.frontiersin.org/articles/10.3389/fncom.2019.00055/full ) and thought "Interesting idea but I can improve that!" then you may want to tell us your idea... You could end up working on it as a Ph.D. student / Postdoc for the next three years in Bremen, Germany. ------------- The Computational Neuroscience group of Klaus Pawelzik invites applications for an open Ph.D. student / Postdoc position (E13 TV-L 100% for 3 years; all genders welcome) in the project "Efficient Implementation of Spike-by-Spike Neural Networks using Stochastic and Approximative Techniques". We are looking for a person with a strong background in mathematics, programming, and machine learning as well as an intense interest in neuroscience. Someone who is not afraid of cooperating with engineers, since this is a joint project with a focus on hardware development. The overarching goal of our project is to improve the efficiency of spiking artificial neural networks using hardware and algorithmic approximation techniques. Specifically, the project focuses on Spike-by-Spike networks since they offer a balance between computational requirements and biological-realism which keeps the advantages of the biological networks while enabling a compact technical realization. To fully take advantage of the unique features of SbS in terms of robustness and sparseness, dedicated hardware architectures are required. You would join in with numerical simulations, theoretical analyses, as well as through the development of new ideas and approaches for boosting the performance and capabilities of the Spike-By-Spike model. Furthermore, you would also work on combining Spike-By-Spike networks with non spiking deep neuronal networks into hybrid models. The details can be found at http://www.neuro.uni-bremen.de/content/open-position-sbs

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Postdoc / Staff Scientist in brain imaging during natural behavior
by Geoffrey Goodhill Aug. 28, 2021

Aug. 28, 2021

Postdoc / Staff Scientist in brain imaging during natural behavior The Goodhill lab at Washington University in St Louis is looking for a postdoc or staff scientist for an NIH-funded project to develop novel microscopy methods for whole-brain calcium imaging in freely-behaving zebrafish. This is a collaboration with Oliver Cossairt at Northwestern University. The lab focuses on the formation of neural circuits underlying behavior. This includes developing novel experimental, computational and theoretical methods and applying them to domains including hunting and social behavior, sleep and autism spectrum disorders. In this project we will be constructing new light-field imaging techniques to record the activity of ~100,000 neurons in the zebrafish brain during unconstrained hunting behavior. We are looking for someone with a background in developing new imaging technologies. The lab is fully integrated between experimental and theoretical work. Washington University in St Louis is ranked in the top 10 globally for Neuroscience and Behavior, and offers an outstanding intellectual environment for neuroscience research. To apply please send a detailed CV and cover letter explaining your interest to g.goodhill(a)wustl.edu<mailto:g.goodhill@wustl.edu>. Professor Geoffrey J Goodhill Departments of Developmental Biology, Neuroscience, Biomedical Engineering, and Electrical and Systems Engineering Washington University School of Medicine St. Louis, MO 63110 https://neuroscience.wustl.edu/people/geoffrey-goodhill-phd Email: g.goodhill(a)wustl.edu<mailto:g.goodhill@wustl.edu>

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Post-doctoral Position in Human Perceptual Neuroscience at NYU
by Hardstone, Richard Aug. 27, 2021

Aug. 27, 2021

The Perception and Brain Dynamics Laboratory (PI: Biyu J. He, Ph.D.; www.med.nyu.edu/helab<http://www.med.nyu.edu/helab>) at New York University currently has two open post-doctoral positions, which can start immediately (with a flexible start date). We are interested in candidates with a background in perceptual/cognitive neuroscience and/or related computational modeling. Experience in human electrophysiology (MEG/EEG or intracranial recordings) or neuroimaging (fMRI) is ideal. Successful applicants will be able to participate in cutting-edge research to understand the neural basis of human perception (broadly defined), and will have opportunities to employ multiple advanced recording (e.g., M/EEG, 7T fMRI, ECoG) and analysis approaches in their research. Research questions may include: * What are the roles of spontaneous brain activity in perception? * How do prior knowledge and past experiences influence perception? * What are the neural differences between conscious and unconscious processing? * What are the temporal dynamics of neural activity underlying perception? To apply, please email PI Biyu J. He at biyu.jade.he(a)gmail.com<mailto:biyu.jade.he@gmail.com>, with subject line "post-doctoral application", and include a CV, a 1-2 page research statement (including accomplishments and interests), and names and contacts for 2-3 referees. To inquire, please email PI Biyu J. He at biyu.jade.he(a)gmail.com<mailto:biyu.jade.he@gmail.com>, with subject line "post-doctoral application inquiry", and attach your CV. Our laboratory is located on the medical campus of New York University in Manhattan, NY, and is affiliated with the Neuroscience Institute, Departments of Neurology, Neuroscience & Physiology, and Radiology. We offer competitive salaries and benefits that adjust for cost of living in New York City. The position is expected to last at least 2 years, and is renewed annually depending on satisfactory performance and availability of funding. Applications will be considered on a rolling basis, starting immediately.

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2nd SMILES workshop, Aug 31st: Sensorimotor Interaction, Language and Embodiment of Symbols
by Xavier Hinaut Aug. 27, 2021

Aug. 27, 2021

* WHAT The SMILES workshop is about Sensorimotor Interaction, Language and Embodiment of Symbols (SMILES). It is a satellite event from the ICDL 2020 (International Conference on Developmental Learning). * WHEN Tuesday 31st of August 2021. Planned schedule: 9am - 8.30pm CEST (UTC+2). * WHERE Online event on Zoom. (Link sent to registered people the day before, see LINKS section) * WHO 8 Invited speakers from multiple fields: neurolinguistics, speech communication, language evolution, developmental science, social robotics, computational neuroscience, language acquisition. - Morten Christiansen, Cornell University, NY, USA & Aarhus University, Denmark - Kaya de Barbaro, University of CaliforniaSan Diego, USA - Cynthia Matuszek, University of Maryland, Baltimore County, USA - Yair Lakretz, NeuroSpin Center, Gif sur Yvette, France - Harm Brouwer, Saarland University, Germany - Jean-Luc Schwartz, Gipsa lab, Grenoble-Alpes University, France - Daniel Dor, Tel Aviv University, Israel - Lauren Emberson, University of British Columbia, Canada - Bart de Boer, AI-lab, Vrije Universiteit Brussel, Belgium * LINKS - ** Website **: https://sites.google.com/view/smiles-workshop/home - ** Registration is free but mandatory **: https://forms.gle/tyvMdbb8UJFyXZky8 - Contact: smiles.conf(a)gmail.com - ICDL conference website: https://icdl-2021.org/ * WHAT+ (Short Description) On the one hand, models of sensorimotor interaction are embodied in the environment and in the interaction with other agents. On the other hand, recent Deep Learning development of Natural Language Processing (NLP) models allow to capture increasing language complexity (e.g. compositional representations, word embedding, long term dependencies). However, those NLP models are disembodied in the sense that they are learned from static datasets of text or speech. How can we bridge the gap from low-level sensorimotor interaction to high-level compositional symbolic communication? The SMILES workshop will address this issue through an interdisciplinary approach involving researchers from (but not limited to): - Sensori-motor learning, - Emergent communication in multi-agent systems, - Chunking of perceptuo-motor gestures (gestures in a general sense: motor, vocal, ...), - Sensori-motor learning, - Symbol grounding and symbol emergence, - Compositional representations for communication and action sequence, - Hierarchical representations of temporal information, - Language processing and acquisition in brains and machines, - Models of animal communication, - Language evolution, - Understanding composition and temporal processing in neural network models, and - Enaction, active perception, perception-action loop. SMILES workshop organisers - Xavier Hinaut, Inria, Bordeaux, France - Clément Moulin-Frier, Inria and Ensta ParisTech, Bordeaux, France - Silvia Pagliarini, Inria, Bordeaux, France - Michael Spranger, Sony AI and Sony CSL, Tokyo, Japan - Tadahiro Taniguchi, Ritsumeikan University, Kyoto, Japan - Anne S. Warlaumont, University of California, Los Angeles, America - Junpei Zhong, Nottingham Trent University, Nottingham, United Kingdom Xavier Hinaut Inria Researcher (CR) Mnemosyne team, Inria LaBRI, Université de Bordeaux Institut des Maladies Neurodégénératives www.xavierhinaut.com

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Recorded talks from TEX2021 - Bringing together Predictive Processes and Statistical Learning
by Yamil Vidal Aug. 26, 2021

Aug. 26, 2021

Dear all, This July we had the honor of hosting TEX2021 - Bringing together Predictive Processes and Statistical Learning. Here you can find the recordings of the talks: https://youtube.com/playlist?list=PLtD9ktPLCbafVngJ_whZL65_KT23XAKnl Thanks again to all of our amazing invited speakers, Tristan Bekinschtein, Lucia Melloni, Padraic Monaghan, Maria Chait, Floris de Lange, Caspar Schwiedrzik, Lori Holt and Nicola Molinaro. Please share!! Best regards, Yamil Vidal and Davide Crepaldi Dr. Yamil Vidal Cognitive Neuroscience Sector Scuola Internazionale Superiore di Studi Avanzati - SISSA via Bonomea, 265 34136 Trieste, ITALY

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CfP: Call for Paper Frontiers in Neuroscience: Special Issue "Graph Learning for Brain Imaging"
by Feng Liu Aug. 25, 2021

Aug. 25, 2021

Dear Colleagues, We are writing to you know that we are organizing a special issue "Graph Learning for Brain Imaging" in Frontiers in Neuroscience (impact factor 4.7). We believe this is a timely special issue to showcase the new developments using graph representation, deep learning on graph-structured data to address important brain imaging and computational neuroscience problems. *Link*: https://www.frontiersin.org/research-topics/23683/graph-learning -for-brain-imaging *Keywords*: Brain Networks, Graph Neural Networks, Brain Imaging, Graph Embedding, Multi-Modal Imaging. *Topics*: We are looking for original, high-quality submissions on innovative research and developments in the analysis of brain imaging using graph learning techniques. Topics of interest include (but are not limited to): • Graph neural networks (GNN) for network neuroscience applications • Graph neural network for brain mapping and data integration • Graph convolution network (GCN) for brain disorder classification • (Dynamic) Functional brain networks • Brain networks development trajectories • Graphical model for brain imaging data analysis • Spatial-temporal brain network modeling • Graph embedding and graph representation learning • Information fusion for brain networks from multiple modalities or scales (fMRI, M/EEG, DTI, PET, genetics) • Generative graph models in brain imaging • Brain network inference: scalable, online, and from non-linear relationships • Machine learning over graphs: kernel-based techniques, clustering methods, scalable algorithms for brain imaging • A few-shot learning for learning from limited brain data • Graph federated learning for brain imaging *Important Dates*: Abstract: 30-Sep-2021 Full paper: 30-Dec-2021 *Background:* Unprecedented collections of large-scale brain imaging data, such as MRI, PET, fMRI, M/EEG, DTI, etc. provide a unique opportunity to deepen our understanding of the brain working mechanisms, improve prognostic predictions for mental disorders, and tailor personalized treatment plans for brain diseases. Recent advances in machine learning and large-scale brain imaging data collection, storage, and sharing lead to a series of novel interdisciplinary approaches among the fields of computational neuroscience, signal processing, deep learning, brain imaging, cognitive science, and computational psychiatry, among which graph learning provides a valuable means to address important questions in brain imaging. Graph learning refers to designing effective machine learning and deep learning methods extracting important information from graphs or exploiting the graph structure in the data to guide the knowledge discovery. Given the complex data structure in different imaging modalities as well as the networked organizational structure of the human brain, novel learning methods based on graphs inferred from imaging data, graph regularizations for the data, and graph embedding of the recorded data, have shown great promise in modeling the interactions of multiple brain regions, information fusion among networks derived from different brain imaging modalities, latent space modeling of the high dimensional brain networks, and quantifying topological neurobiomarkers. The goal of this Research Topic is to synergize the start-of-the-art discoveries in terms of new computational brain imaging models and insights of brain mechanisms through the lens of brain networks and graph learning. --On Behalf of all the Guest Editors Feng Liu, Stevens Institute of Technology, Hoboken, NJ, USA Yu Zhang, Lehigh University, Bethlehem, PA, USA Jordi Solé-Casals, Universitat de Vic - Universitat Central de Catalunya, Barcelona, SpainIslem Rekik, Istanbul Technical University, Istanbul, TurkeyYehia Massoud, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia Best regards Feng Liu

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WWNeuRise series on predictive processing (Sept-Oct 2021)
by William Podlaski Aug. 24, 2021

Aug. 24, 2021

WorldWideNeuRise will host several online talks throughout September-October on the topic of predictive processing from the following speakers (titles TBA): Wednesday, 8 September, 5pm CET Caroline Jahn (Buschman Lab, Princeton University) Ramon Bartolo (Averbeck Lab, National Institute of Mental Health) Wednesday, 22 September, 5pm CET Nick Audette (Schneider Lab, New York University) Colleen J. Gillon (Richards Lab, Mila & McGill University) Wednesday, 6 October, 5pm CET Nuttida Rungratsameetaweemana (Sejnowski Lab, UCSD & Salk Institute) Oded Bein (Niv Lab, Princeton University) Wednesday, 20 October, 5pm CET Selina Solomon (Growth Intelligence) Tomaso Muzzu (Saleem Lab, University College London) For more information, please visit our website (https://neurise.github.io/) find us on WorldWideNeuro (https://www.world-wide.org/Neuro/WWNeuRise/) or follow us on Twitter (https://twitter.com/WWNeuRise) We are actively searching for PhD students and postdocs to give talks in our upcoming seminar blocks. On our website (https://neurise.github.io/) you can find an application form to directly apply, as well as a recommendation form, where you can suggest individuals from your lab, institute, or otherwise who you think might have an interesting story to tell! We are looking forward to seeing you at our upcoming seminars! The organising committee, Ellen Boven (Apps lab, University of Bristol) Christina Buetfering (Häusser lab, University College London) Lucie Descamps (Kentros lab, Norwegian University of Science and Technology) Gily Ginosar (Ulanovsky lab, Weizmann Institute) Loreen Hertäg (Clopath lab, Imperial College London) Joram Keijser (Sprekeler lab, TU Berlin) Bill Podlaski (Machens lab, Champalimaud Research)

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Reminder (one week left): Call for papers: Symposium on Mathematics of Neuroscience
by Ruairidh McLennan Battleday Aug. 23, 2021

Aug. 23, 2021

Two decades into the 21st century, can we claim to be any closer to a unified model of the brain? In this exploratory symposium, we invite submissions for short talks and posters presenting general mathematical models of brain function. We give priority to those models that account for brain or behavioural data, or provide simulations to that effect. The symposium will be held at the applied mathematics conference ICNAAM 2021, to be held virtually and in person on the island of Rhodes, Greece from the 20-26th of September 2021. Submission is by 250-word abstract before the 30th August, sent to the organizers Prof Dan V. Nicolau Jr ( dan.nicolau(a)qut.edu.au) and Dr Ruairidh M. Battleday ( battleday(a)princeton.edu) We already have a great lineup (www.neuromonster.org) and with one week left until the deadline for the abstract submission encourage any remaining interested parties to apply. Ruairidh and Dan -- Dr. Ruairidh McLennan Battleday, BMBCh (Oxon) MA PhD Candidate and Cognitive Science Fellow Computational Cognitive Science Lab, Department of Computer Science Princeton University Department of Statistics St John's College University of Oxford

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[meetings] 2nd SMILES workshop, Aug 31st: Sensorimotor Interaction, Language and Embodiment of Symbols
by Xavier Hinaut Aug. 21, 2021

Aug. 21, 2021

* WHAT The SMILES workshop is about Sensorimotor Interaction, Language and Embodiment of Symbols (SMILES). It is a satellite event from the ICDL 2020 (International Conference on Developmental Learning). * WHEN Tuesday 31st of August 2021. Planned schedule (it might change): 9am - 8.30pm CEST (UTC+2). * WHERE Online event on Zoom. (Link sent to registered people the day before, see LINKS section) * WHO 8 Invited speakers from multiple fields: neurolinguistics, speech communication, language evolution, developmental science, social robotics, computational neuroscience, language acquisition. - Morten Christiansen, Cornell University, NY, USA & Aarhus University, Denmark - Kaya de Barbaro, University of CaliforniaSan Diego, USA - Cynthia Matuszek, University of Maryland, Baltimore County, USA - Yair Lakretz, NeuroSpin Center, Gif sur Yvette, France - Harm Brouwer, Saarland University, Germany - Jean-Luc Schwartz, Gipsa lab, Grenoble-Alpes University, France - Daniel Dor, Tel Aviv University, Israel - Lauren Emberson, University of British Columbia, Canada * LINKS - ** Website **: https://sites.google.com/view/smiles-workshop/home <https://sites.google.com/view/smiles-workshop/home> - ** Registration is free but mandatory **: https://forms.gle/tyvMdbb8UJFyXZky8 <https://forms.gle/tyvMdbb8UJFyXZky8> - Contact: smiles.conf(a)gmail.com <mailto:smiles.conf@gmail.com> - ICDL conference website: https://icdl-2021.org/ <https://icdl-2021.org/> * WHAT+ (Short Description) On the one hand, models of sensorimotor interaction are embodied in the environment and in the interaction with other agents. On the other hand, recent Deep Learning development of Natural Language Processing (NLP) models allow to capture increasing language complexity (e.g. compositional representations, word embedding, long term dependencies). However, those NLP models are disembodied in the sense that they are learned from static datasets of text or speech. How can we bridge the gap from low-level sensorimotor interaction to high-level compositional symbolic communication? The SMILES workshop will address this issue through an interdisciplinary approach involving researchers from (but not limited to): - Sensori-motor learning, - Emergent communication in multi-agent systems, - Chunking of perceptuo-motor gestures (gestures in a general sense: motor, vocal, ...), - Sensori-motor learning, - Symbol grounding and symbol emergence, - Compositional representations for communication and action sequence, - Hierarchical representations of temporal information, - Language processing and acquisition in brains and machines, - Models of animal communication, - Language evolution, - Understanding composition and temporal processing in neural network models, and - Enaction, active perception, perception-action loop. SMILES workshop organisers - Xavier Hinaut, Inria, Bordeaux, France - Clément Moulin-Frier, Inria and Ensta ParisTech, Bordeaux, France - Silvia Pagliarini, Inria, Bordeaux, France - Michael Spranger, Sony AI and Sony CSL, Tokyo, Japan - Tadahiro Taniguchi, Ritsumeikan University, Kyoto, Japan - Anne S. Warlaumont, University of California, Los Angeles, America - Junpei Zhong, Nottingham Trent University, Nottingham, United Kingdom Xavier Hinaut Inria Researcher (CR) Mnemosyne team, Inria LaBRI, Université de Bordeaux Institut des Maladies Neurodégénératives www.xavierhinaut.com <http://www.xavierhinaut.com/>

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Postdoc and PhD Positions in Computational Cognitive Neuroscience at UKE - Hamburg
by Stefano Panzeri Aug. 20, 2021

Aug. 20, 2021

The Panzeri and Donner laboratories at the University Medical Center Hamburg-Eppendorf (UKE) aim to fill joint postdoc and PhD positions starting at any time. The vacant positions are for collaborative projects in computational cognitive neuroscience, and funding is available for several years. Applications will be reviewed when they are received until positions are filled. Both labs aim to understand how higher brain functions such as sensation, perception, decision-making, and memory, emerge from the interactions between brain areas and/or populations of neurons within these areas. These topics are studied in the healthy brain and in neuropsychiatric disorders, and they are addressed through a tight interplay between theoretical and experimental approaches. Joint ongoing work focuses on how feedforward and feedback communication across the cortical hierarchy, excitation-inhibition balance, and neuromodulatory processes, shape decision-making. The Panzeri Lab, newly established at UKE Hamburg, is a theoretical lab which develops advanced mathematical tools for brain data analysis, applies these methods to real data to produce new results on neural information processing, and develops biophysically plausible neural network models of how information is processed in the brain. The Donner lab is an experimental lab that combines pharmacological intervention, psychophysics, eye-tracking, and neuroimaging (MEG and fMRI) in human subjects with advanced data analysis and computational modeling tools to uncover signatures and mechanisms of cognitive computation. The new lab members will join existing teams with extensive expertise in both computational and experimental neuroscience. The new lab members will also benefit from both labs’ extensive international collaborations including Harvard Medical School, Italian Institute of Technology, Baylor College of Medicine, Cornell University, École Normale Supérieure, New York University, Pompeu Fabra University, University of Pennsylvania, SISSA and others. We seek applications from qualified individuals from all demographics, genders, and backgrounds. Both labs are committed to diversity, highly international, and operate in English. Hamburg is a vibrant, international, family-friendly, and green city. It hosts one of the world’s largest harbours, an internationally acclaimed concert hall, a large lake, and dozens of canals, lots of beautiful parks, and many other nice places for leisure time. The UKE also has excellent family-friendly facilities. Candidates should have a solid background in numerate sciences and a keen interest in applying advanced mathematical concepts to reveal the function of the brain. Experience in quantitative or computational neuroscience is a major plus. The main criterion, however, is a commitment to scientific excellence. Applications (full CV, statement of research interest and name and contact of 2 referees) should be sent to t.donner(a)uke.de , stefano.panzeri(a)gmail.com Further information on both labs can be found here: http://www.tobiasdonner.net https://www.uke.de/english/departments-institutes/institutes/department-of-… Recent relevant publications of the labs include: Pfeffer, T. et al (2021), Circuit mechanisms for the chemical modulation of cortex-wide network interactions and behavioral variability*. *Science Advances 7: eabf5620 Valente, M., et al (2021), Correlations enhance the behavioral readout of neural population activity in association cortex. Nature Neuroscience, 24, 975–986 Murphy P., et al (2021), Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments. Nature Neuroscience 7: 987-997. Wilming N., et al (2020), Large-scale dynamics of decision information across human cortex. Nature Communications 11: 5109. Chong, E. et al (2020) Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception. Science 368, 1329. Runyan C. A., et al (2017) Distinct timescales of population coding across cortex, Nature: 548: 92-96. Stefano Panzeri, Professor of Neural Information Processing Tobias H. Donner, Professor of Integrative Neuroscience University Medical Center Hamburg-Eppendorf

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