- Comp-neuro - lists.cnsorg.org

8th Baltic-Nordic Summer School on Neuroscience and Neuroinformatics (BNNI 2021)
by Pawel Herman Sept. 5, 2021

Sept. 5, 2021

Dear Fellow Neuroscientists and Students We would like to invite you to the 8th Baltic-Nordic Summer School on Neuroscience and Neuroinformatics, which will be organised as a virtual event on 21-25 September, 2021. The 8th Baltic-Nordic Summer School on Neuroscience and Neuroinformatics 2021 “Learning in the Brain and NeuroRobots – from Molecules to Behaviour” offers interdisciplinary course and covers modelling at different levels of organization of the brain, from single neurons to microcircuits, neural networks and neurorobotics. The course offers lectures on the latest achievements in understanding learning, neural and network dynamics and function in health and disease, neurorobotic theory and applications, and hands-on tutorials on the EBRAINS services and tools. The summer school targets advanced master students, doctoral students and postdoctoral researchers in biomedical and technology sciences, ranging from medicine, biology, psychology, to mathematics, informatics, information technology, physics and chemistry, who would like to get an introduction to neuroinformatics and computational neuroscience and especially the EBRAINS Infrastructure<http://ebrains.eu/> (ebrains.eu<http://ebrains.eu>). Further information and registration: https://www.humanbrainproject.eu/en/education/BNNI2021/ Best regards Pawel Herman on behalf of the organising committee ------------------------------------ Paweł Herman Associate Professor, PhD, Docent KTH Royal Institute of Technology School of Electrical Engineering and Computer Science (EECS) Division of Computational Science and Technology (CST) Lindstedtsvägen 5 114 28 Stockholm, Sweden room 4442 (4th floor, D-building) tel. +46 8 790 6513

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DEADLINE EXTENDED: Summer School on Network and Control Sciences for Psychiatry
by Jasper Bischofberger Sept. 2, 2021

Sept. 2, 2021

Dear all, We are happy to announce a Summer School on “Current and Future Applications of Network and Control Sciences for Psychiatry” Host Institution: *University of Tübingen, Germany* Location: *Online event* Dates: *September 29th – October 1st 2021* Abstract deadline: *September 10th, 2021* Registration fees: *free* *Focus* This Summer School gives an opportunity to students and young researchers to get acquainted with and exchange within a range of current topics in network and control sciences and its applications in Psychiatry. ____ *Keynotes* Danielle S. Bassett (University of Pennsylvania, USA) Peter Dayan (MPI Tübingen, Germany) John Medaglia (Drexel University, USA) For the full list of speakers visit: https://www.medizin.uni-tuebingen.de/de/summer-school-controlpsychiatry/spe… ____ *Registration* The summer school is free of charge for all participants, but to get all important information please register online via: https://www.medizin.uni-tuebingen.de/de/summer-school-controlpsychiatry ____ *Poster Submission* Participants can present their latest research results as a poster. Some of the most relevant submissions will have the possibility to present their work in a dedicated oral session and receive an official certificate. Please submit your abstract (limited to 250 words) latest by September 10th to: controlpsychiatry(a)med.uni-tuebingen.de ____ Questions? Please contact: controlpsychiatry(a)med.uni-tuebingen.de We are excited to see you soon! Best wishes, The organizing committee

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Faculty Position in Computational Neuroscience at Bristol University.
by Conor Houghton Aug. 31, 2021

Aug. 31, 2021

In the Department of Computer Science at the University of Bristol we are seeking to appoint a faculty member in Computational Neuroscience at Lecturer or Senior Lecturer level, comparable to Assistant or Associate Professorship level. https://www.bristol.ac.uk/jobs/find/details/?jobId=252155 Candidates are sought with expertise in computational neuroscience or computational cognitive science; any area of our broad subject will be considered. The University of Bristol is a significant centre for both computational neuroscience and machine learning and a leader in experimental, clinical and cognitive neuroscience. There is a robust tradition of collaboration between experimental and computational neuroscientists. Bristol itself is a friendly green coastal city with a rich and lively cultural scene and is regarded one of the best places to live in the UK. You are welcome to contact one of us (conor.houghton||laurence.aitchison||n.lepora||rui.costa)(a)bristol.ac.uk, or our head of department Chris Allen, c.b.allen(a)bristol.ac.uk, to discuss the post informally. The closing date for applications is 3 October. We value diversity and are committed to equality of opportunity. All the best Conor Houghton Laurence Aitchison Nathan Lepora Rui Ponte Costa https://bristolcnu.github.io/ http://www.bristol.ac.uk/neuroscience/

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PhD position at the Goethe University Frankfurt, Germany
by Claudius Gros Aug. 30, 2021

Aug. 30, 2021

Applications are invited for a fully funded PhD position at the Institute for Theoretical Physics, Goethe University Frankfurt, Germany Fields: theoretical neurosciences, complex systems theory, Covid-19 modeling Application deadline: Oct 15, 2021 Supervisor: Prof. Dr. Claudius Gros We are developing new models and generative principles for the societies of interacting agents using a range of toolsets from dynamical systems theory and game theory. Examples are Covid-19 modeling and the influence of envy on the structure of human societies. We are also developing operating principles for reference robot systems, and for neuroscience applications. It is expected that applicants study the publication list available on the website of the lab, as given below. Several subjects are available for the announced PhD thesis, depending on the background of the successful candidate. The work will include analytical investigations and numerical simulations. The candidates should have a Diploma/Master's in physics with an excellent academic track record and good computational skills. Experience or strong interest in the fields of complex systems, dynamical systems theory, game theory, and/or artificial or biological cognitive systems is expected. The degree of scientific research experience is expected to be on the level of a German Diploma/Master. The appointment will start early 2022, for three years. Interested applicants should submit a curriculum vitae and a list of publications, and arrange for letters of reference to be sent to the address below. Prof. Claudius Gros Institute for Theoretical Physics Goethe University Frankfurt Max-von-Laue-Str. 1 60438 Frankfurt am Main, Germany cgr(a)itp.uni-frankfurt.de http://www.itp.uni-frankfurt.de/~gros General information on the University of Frankfurt and its employment policy: With its around 46,000 students and 4,600 employees, the Goethe University in Frankfurt is the largest university in the state of Hessen and an internationally renowned, important regional employer. Numerous quality and performance oriented internal reforms have been initiated in the recent years. The reorganized campuses for natural sciences and humanities offer an ideal environment for research and education. Since 2008, the Goethe University is a foundation under public law and enjoys full administrative autonomy.  The fixed-term employment of the academic staff is subject to the provisions of the Temporary Science Employment Law and the Hessian Higher Education Act. The University advocates gender equality and therefore strongly encourages women to apply. People with disabilities are given preference if equally qualified. -- ### ### Prof. Dr. Claudius Gros ### http://itp.uni-frankfurt.de/~gros ### ### Complex and Adaptive Dynamical Systems, A Primer ### A graduate-level textbook, Springer (2008/10/13/15) ### ### Life for barren exoplanets: The Genesis project ### https://link.springer.com/article/10.1007/s10509-016-2911-0 ###

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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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