- Comp-neuro - lists.cnsorg.org

Multiple postdoctoral openings in reinforcement learning in multi-scale brain networks at Washington University in St. Louis
by Ching, ShiNung June 14, 2021

June 14, 2021

Multiple postdoctoral openings in modeling reinforcement learning in multi-scale brain networks at Washington University in St. Louis Multiple postdoctoral openings are available for a new and large, multi-PI project to study the circuit underpinnings of reinforcement learning and decision-making. A key focus is on understanding multiple time-scales of information processing in neural circuits and involvement of neuron-astrocyte interactions. Experiment- and theory-forward positions are available. Applicants should have prior experience in neuroscience (computational or otherwise), modeling of large-scale systems, optimization, machine learning or related areas. Positions will be cross-appointed between Engineering and Neuroscience. Washington University in St. Louis is an independent, Research 1 university in St. Louis, MO, USA, with nearly 60 of the university's graduate and undergraduate programs ranked in the top 25 by US News & World Report. www.wustl.edu Interested applicants are invited to send a CV and brief description of interests to ShiNung Ching: shinung(a)wustl.edu

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EITN Seminar: Paul Miller (Brandeis U, Waltham MA, USA) - Friday 18 June 16:00
by Zélie Tournoud June 14, 2021

June 14, 2021

Dear colleagues, We are pleased to welcome *Paul Miller* from Brandeis University (Waltham MA, USA) for an online seminar this week. It will take place on Zoom on *Friday 18 June at 16:00* CEST (Paris time). *Title**: **Information processing in neural circuits with multiple quasi-stable attractor states* *Abstract: *Circuits containing clusters of neurons with strong within-cluster excitatory connections and random between-cluster connections can possess multiple activity states, with each state defined by the set of clusters with stably high versus low neural firing rates. Noise or adaptive processes can render the states quasi-stable such that in the presence of constant input, or following successive identical inputs, the network can progress through a sequence of different activity states. Here we study the properties of such models, including the role of finite size effects in producing multistability in networks with Gaussian random cross-coupling, and we assess the extent to which such state sequences might subserve information processing and behavior in a range of cognitive tasks. Join on the day on the event at: > Topic: EITN seminar: Paul Miller (Brandeis University) > Time: 18 June 2021 04:00 PM Paris (CEST) > Join Zoom meeting: > https://zoom.us/j/99754103845?pwd=UGlHSjgySTBXeUtvSW5SVUVMRnFiZz09 > <https://zoom.us/j/99754103845?pwd=UGlHSjgySTBXeUtvSW5SVUVMRnFiZz09> > Meeting ID: 997 5410 3845 > Password: 634477 We look forwards to see you at the seminar. Kind regards, The EITN Team

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Adaptive Machines: Leveraging Neuroscience for Lifelong Learning Systems
by Andrea Soltoggio June 14, 2021

June 14, 2021

Dear Connectionists, I’d like to bring your attention to the Research Topic “Adaptive Machines: Leveraging Neuroscience for Lifelong Learning Systems” to be co-hosted by Frontiers in Neuroscience, Robotics and AI, Big Data, Artificial Intelligence, Neurorobotics and Computational Neuroscience. The article submission deadline is the 30th of September. Link: https://www.frontiersin.org/research-topics/20053/adaptive-machines-leverag… Description: Artificial intelligence has always sought inspiration in the brain. Artificial neural networks (ANNs), in particular, were modeled after their biological counterparts (biological neural networks, BNNs). However, state-of-the-art deep networks are drastically different from BNNs. Their architectures (at both the single-neuron and macro levels), learning algorithms, and failure conditions share little in common with brain circuit dynamics. In particular, state-of-the-art deep learning is optimized for single, well-defined, static tasks. Deep networks struggle to learn multiple or evolving tasks over time (i.e., continual learning) or to adjust their processing based on environmental conditions. They are also less modular than BNNs, and thus less energy efficient, and utilize little-to-no feedback signals. We believe that deep networks suffer from these limitations because their modeling of brain dynamics is too superficial. Modeling more sophisticated neural mechanisms is therefore key for deep networks to achieve continual or lifelong learning and to cope with open-ended, dynamic environments. The goal of this Research Topic is to publish novel models and/or algorithms that expand the capabilities of deep learning (e.g., achieve better continual learning) by incorporating additional properties of BNNs. We are also interested in neuroscience research that elucidates mechanisms that can be leveraged by the AI community. Neuroscience has uncovered a wide range of learning and regulatory mechanisms, at scales ranging from single molecules to the entire nervous system, that help the brain learn, remember, and adapt. These include replay (systems-level consolidation), neuromodulation, neurogenesis, neuroevolution, attention, homeostatic plasticity, and synaptic consolidation. There exist some deep learning techniques motivated by these mechanisms (e.g., experience replay or attention networks), but they have rarely been used for lifelong learning. As such, our focus will be on approaches that leverage brain-like principles---ideally biologically well-grounded---to solve problems currently beyond the capabilities of the state of the art. We will also welcome neuroscience research that helps to elucidate the brain’s mechanisms, in the hope that they can be used by the next generation of machine learning models. The scope of this Research Topic covers: (1) biologically inspired techniques in deep learning (2) neuroscience research on how the brain facilitates learning and adapts to different contexts We seek to address continual or lifelong learning, transfer learning, and other ML scenarios that go beyond the traditional, train-then-test learning paradigm. Since machine learning is an experimental field, a paper must include experimental results to be accepted; however, we encourage authors to include theoretical analyses of their methods. We aim to collect the following types of manuscripts: Original Research and Brief Research Reports. Since the goal of this Research Topic is to publish highly novel, experimentally grounded work, we are not interested in Reviews (Systematic, Policy and Practice, etc.), General Commentaries, or Opinions. In addition, since the editorial board does not include medical experts, we cannot accept Clinical Trials or Study Protocols. The Research Topic will be edited by Rolando Jose Estrada, Georgia State University, USA Andrea Soltoggio, Loughborough University, UK Praveen K. Pilly, HRL Laboratories, USA Vincenzo Lomonaco, University of Pisa, Italy, Davide Maltoni, University of Bologna, Italy. Please consider submitting your original research to this Research Topic. Do not hesitate to get in touch if you wish to discuss your contribution. Best wishes, Andrea -- Dr. Andrea Soltoggio (he/him or they/them) Senior Lecturer (Associate Professor) in Artificial Intelligence Department of Computer Science, School of Science & Intelligent Automation Centre https://www.intelligent-automation.org.uk/about-us/centre-staff & Centre for Information Management https://www.lboro.ac.uk/departments/sbe/cim/ Haslegrave Building, N.2.03 Loughborough University LE11 3TU, UK Email: a.soltoggio(a)lboro.ac.uk<mailto:a.soltoggio@lboro.ac.uk> Web: http://www.lboro.ac.uk/departments/compsci/staff/dr-andrea-soltoggio.html

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be a mentor for Neuromatch Academy!
by Marius Pachitariu June 11, 2021

June 11, 2021

Participate as MENTOR in Neuromatch Academy Neuromatch Academy returns this year with twice as many students and TAs participating in twice as many courses. If you are faculty or postdoc with some experience, please sign up to be a mentor for the Computational Neuroscience (syllabus <https://github.com/NeuromatchAcademy/course-content>) and/or the Deep Learning course (syllabus <https://github.com/NeuromatchAcademy/course-content-dl>). NMA is unique in reaching enthusiastic students from all over the world, most of whom cannot afford a traditional summer school. NMA 2020 reached students from ~70(!) countries. This year we have students from >100 !!! Mentors provide guidance and feedback on 3-week long projects that students complete in groups of 4-6. You will be algorithmically matched with students that have similar research interests to you.The mentorship happens in 4-5 sessions of 30-60 minutes each, leading to a time commitment of up to 5 hours per group. Mentors can take on 1-4 groups. Beginner groups are encouraged to use project templates, such as the ones here (link <https://docs.google.com/presentation/d/1v7fuzi9Y9RV12CysMBi5rxZKhjnWD5Js29U…>). Please consider contributing a project template, by simply adding a slide to the public google doc. Mentoring is relatively easy because you don’t need to prepare anything. Nonetheless, your feedback is extremely valuable to the students, who are taking their first steps in neuroscience / deep learning. We also encourage you to provide career advice, and optionally set up meetings with TAs after the summer school. Our collective effort to teach and mentor these young scientists and engineers pays off in the long-term: their positive experience at NMA inspires them to consider careers in our research fields. The application for mentors can be found below. Course starts July 5th, so please apply soon! https://portal.neuromatchacademy.org/

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Senior Postdoc ("Akademischer Rat") in the Reichenbach lab, Friedrich-Alexander-University (FAU) Erlangen-Nuremberg, Germany
by Reichenbach, Tobias June 11, 2021

June 11, 2021

The newly founded Chair for Sensory Neuroengineering at the new Department Artificial Intelligence in Biomedical Engineering is looking for a Senior Postdoc ("Akademischer Rat"). In close collaboration with clinical and industrial partners, the Chair researches on the neural foundations of hearing, as well as on new technology for better treatments of hearing impairment. Reseach topics are, for instance, the development and application of AI methods to analyse neurological data on human speech processing as well as the development of AI methods to construct audiovisual speech signals for better speech comprehension. The Senior Postdoc should shape this research field with their own innovative projects. The appointment will initially be for three years, and can be made permanent upon excellent performance. To apply, please send a letter detailing your motivation, a CV, copies of your degrees and details of three referees. Please combine all documents into a single pdf and send them per email with subject “Application Senior Postdoc” to Prof. Dr. Tobias Reichenbach (tobias.j.reichenbach(at)fau.de<http://fau.de>). FAU is the most innovative university in Germany and ranks second among the most innovative European universities. It is located in one of the most attractive regions in Germany. Erlangen is a bicycle-friendly city with 100,000 inhabitants, former home of the Huguenots as well as a key location for Siemens. It is close to Nuremberg, a city with a population of half a million and a metropolitan flair. The mixture of academia, innovative companies, art and culture makes the region the perfect place for creative minds, keen athletes and nature lovers. More information is available here: https://www.jobs.fau.de/jobs/senior-postdoc-in-computational-neurotechnolog… ------------------------------------------- Prof. Dr. Tobias Reichenbach Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-University (FAU) Erlangen-Nuremberg Henkestrasse 91 91056 Erlangen https://reichenbach.bg-research.cc.ic.ac.uk/index.html

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Information theory workshop at CNS*2021 -- call for contributed talks
by Joseph Lizier June 10, 2021

June 10, 2021

Dear all, We are pleased to announce that the Workshop on Methods of Information Theory in Computational Neuroscience<https://abzinger.github.io/202107-CNS2021-ITW.html> will be held once again at the CNS*2021<http://www.cnsorg.org/cns-2021> conference, online. The workshop will be held during sessions over two days of the main conference, July 6 and 7 (time TBA). Our confirmed speakers so far include the following (schedule available soon): * Tatyana Sharpee, Salk Institute * Raul Vicente, University of Tartu * Viola Priesemann, Max Planck Institute for Dynamics and Self-Organization * Fernando Rosas, Imperial College London * Fleur Zeldenrust, Radboud University * Aaron Gutcknecht, University of Goettingen * Lucas Rudelt, Max Planck Institute for Dynamics and Self-Organization * ... more TBA! Also, we would like to call for contributions of talks. If you are interested in contributing a talk, please send a title and abstract to Abdullah Makkeh (abdullah.alimakkeh(a)uni-goettingen.de<mailto:abdullah.alimakkeh@uni-goettingen.de>) at your earliest convenience (earlier submissions will be prioritised). Please pay attention to the registration<https://www.cnsorg.org/cns-2021-registration-fees> for CNS*2021 (early bird deadline and fees), and see our website<https://abzinger.github.io/202107-CNS2021-ITW.html> for more details. We hope you will join us there! Organising Committee: Michael Wibral (co-chair) Abdullah Makkeh (co-chair) Joseph Lizier

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TEX2021 - Bringing together Predictive Processes and Statistical Learning
by Yamil Vidal June 8, 2021

June 8, 2021

You are all invited to: TEX2021 - Bringing together Predictive Processes and Statistical Learning A free online symposium organized by Yamil Vidal and Davide Crepaldi. Description: While the research communities studying Predictive Processes and Statistical Learning sprout from different traditions, they share a core interest in how our brains pick up on patterns in the environment. In this two-days symposium we will bring these communities closer by sharing talks by some of the leading researchers of these fields. When: 1st and 2nd of July Speakers: - Tristan Bekinschtein (Consciousness and Cognition Lab, Department of Psychology, University of Cambridge) - Lucia Melloni (Department of Neuroscience, Max Planck Institute for Empirical Aesthetics. Department of neurology, NYU Grossman School of Medicine) - Padraic Monaghan (Department of Psychology, Lancaster University) - Maria Chait (Ear Institute, University College London) - Floris de Lange (Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen) - Caspar Schwiedrzik (Neural Circuits and Cognition Lab, European Neuroscience Institute Goettingen. Perception and Plasticity Group, German Primate Center - Leibniz Institute for Primate Research) - Lori Holt (Department of Psychology, Carnegie Mellon University) - Nicola Molinaro (BCBL, Basque Center on Cognition, Brain and Language) Schedule and more info at the registration form: https://sissa-it.zoom.us/webinar/register/WN_nBOv5NWZQ36HSH7nyffazg And here is the announcement that we made through Twitter: https://twitter.com/CrepaldiLab/status/1400856625896902656 We would be very happy to count you among the attendees. Please spread the word. Best regards, Yamil and Davide Dr. Yamil Vidal Cognitive Neuroscience Sector Scuola Internazionale Superiore di Studi Avanzati - SISSA via Bonomea, 265 34136 Trieste, ITALY

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EITN Fall School in Computational Neuroscience : Applications open!
by Zélie Tournoud June 8, 2021

June 8, 2021

Dear colleagues, We are happy to let you know that applications for the *EITN Fall School in Computational Neuroscience* are currently open, until the 2nd of July 2021. This *10-days intensive course in computational neuroscience* will take place in *P**aris, France from 15 to 24 September 2021*. This is a yearly event, although previous occurrences took place in the spring. We choose to keep it relatively small-scale in order to facilitate interactions between participants and teaching staff, there are about 20 seats available. There will, therefore, be a selection made after applications are closed. We encourage you to share the word widely with your collaborators and networks so that everyone who might be interested can get the chance to apply. Applications need to be addressed by email with a *resume (CV)* and a *cover letter* at *eitn(a)neuro-psi.fr*. Please make sure to use an explicit email title such as "Application to EITN Fall School". *Why join?* * 10 days of intensive training in computational neuroscience provided by researchers from all over Europe * Small scale event: get to connect! * A complete program: o Cellular models, and models of brain signals Circuit models and networks o Mean-field models o Whole-brain models * Hands-on learning schedule based on: o Morning classes and tutorials o Group projects in the afternoon o Free time in the evenings to visit Paris *Selection criteria:* 1. Work or study in neuroscience or related 2. Experience in python programming would help 3. Priority to PhD students, although Master students and post-docs are welcome to apply *Practical information:* * Subscription fee 400€. * If selected, you will need to bring your own laptop with a few pre-installed programs. * The tuition fee includes lunch, coffee, access to conference facilities and internet connection during the 10 days of the course. You can also consult these information online <https://www.eitn.org/index.php/calendar-event/eventdetail/737/-/eitn-fall-s…> or by downloading the PDF flyer: https://www.eitn.org/images/events/EITN-FallSchool-2021_Flyer.pdf Thank you very much for applying or sharing with your relevant contacts. Kind regards, The EITN Team

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Postdoc Position at Cardiff University Brain Research Imaging Centre
by Jiaxiang ZHANG June 8, 2021

June 8, 2021

[Apologize for cross-posting] Dear all, We are seeking an enthusiastic early-career scientist for a research associate position at Cardiff University Brain Research Imaging Centre (CUBRIC). The successful candidate will join a collaborative and multidisciplinary research team to work on the neurocognitive mechanisms of human decision-making, integrating multimodal brain imaging (MEG, functional MRI and diffusion MRI) and computational modelling. The post holder will develop computational models that describe data from behavioural and brain imaging experiments, in particular to take a lead role in integrating large-scale computational simulations with imaging results from multiple modalities. The research aims to understand the computational and neurocognitive processes of human decision-making, and how decision deficits may arise due to brain injury or neurodegeneration. The research associate will also be expected to collaborate with other researchers in the team on imaging data analyses and data curation. You will have access to imaging facilities at CUBRIC and several multimodal imaging datasets. Cardiff University Brain Research Imaging Centre (CUBRIC) houses a unique combination of state-of-the-art facilities and world-leading expertise, with 4 human MRI systems (2 x Siemens Prisma, 1 x Siemens Connectom, 1 x Siemens 7T), MEG, EEG, TMS, tDCS, clinical research units and testing labs. Further details of CUBRIC can be found on our webpage (http://sites.cardiff.ac.uk/cubric) This is a full-time, fixed-term position until February 2023, available to start now. Remote working is possible if COVID-19 restrictions are in place. For informal enquiries, please contact Dr Jiaxiang Zhang ( zhangj73(a)cardiff.ac.uk) A formal application needs to be submitted via the link online ( https://www.jobs.ac.uk/job/CEQ831/research-associate) *Application deadline: June 13th, 2021.* *Essential Criteria* 1. A PhD degree in a relevant discipline (e.g., mathematics, computer science, engineering, physics or psychology) 2. Research experience or a strong interest in human decision-making 3. Strong experience in computational models of cognitive processes and/or macroscopic neural systems, including the use of neural mass models or neural field models 4. Experience of analysing brain imaging data in one or more modalities (e.g., diffusion MRI, BOLD functional MRI, MEG/EEG), using common software packages 5. Strong programming skills (e.g., Matlab, Python, C/C++ or R) 6. Experience with Unix/Linux environment for data analysis, including bash scripting and parallel computing on high-performance clusters 7. Proven ability to publish high-quality research in peer-reviewed academic journals 8. Knowledge of software development cycles and best programming practices, including coding standards, version control administration, debug and code sharing, with strong commitments to open science practices 9. Excellent written and verbal communication skills, evidenced by publications and/or conference presentations 10. Strong interpersonal skills, including proven ability to work cooperatively within a team while also being able to take the initiative *Desirable Criteria * 1. Experience of brain functional and structural connectivity analyses 2. Experience in statistical methods (e.g., GLM, mixed models, multivariate statistics, hierarchical models and Bayesian statistics) and/or machine learning methods 3. Experience in deep learning models of imaging processing --- Dr. Jiaxiang Zhang Cardiff University Brain Research Imaging Centre (CUBRIC) School of Psychology Cardiff University Maindy Road, Cardiff, CF24 4HQ https://ccbrain.org

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[jobs] One-year Postdoc on “Sparse coding for bio-inspired vision models” at CentraleSupèlec, Gif-sur-Yvette, France
by PERRINET Laurent June 7, 2021

June 7, 2021

Dear Colleagues, Applications are invited for a one-year Postdoc position, starting in September 2021 or as soon as possible thereafter, to work under the joint supervision of Dario Prandi<https://dprn.github.io/> (CNRS, Université Paris-Saclay), Luca Calatroni<https://sites.google.com/view/lucacalatroni/home> (CNRS, Université Côte d'Azur), and Laurent Perrinet<https://laurentperrinet.github.io/> (CNRS, Institut de Neurosciences de la Timone). The position is financed by the ANR Rubin-Vase (https://anr.fr/Projet-ANR-20-CE48-0003) and the research focus will be on linking the bio-inspired models studied by D. Prandi and L. Calatroni (see e.g. [1]) to the sparsity promoting neural architectures investigated by L. Perrinet (see, e.g., [2]). The successful applicant will have a strong applied mathematical background, ideally in the fields of sparse optimization and/or dynamical systems, and a good familiarity with neural architectures and the associated implementation issues. Strong skills in coding and programming using standard optimization/deep learning languages (e.g., Python, PyTorch, Julia, MATLAB…) are required. The gross salary is of 50k-59k euros (depending on the seniority of the applicant), and the position comes with no teaching duties. Although the position is officially based at CentraleSupèlec (Université Paris-Saclay), the successful applicant is expected to spend fairly long periods at the INT in Marseille and I3S in Sophia-Antipolis, to work with the co-supervisors. Candidates are asked to write directly to Dario Prandi (dario.prandi(a)centralesupelec.fr<mailto:dario.prandi@centralesupelec.fr>), attaching a CV, a short description of current research interests, and names and contact addresses of at least two reference contacts (no formal letters of recommendation are required). Applications are reviewed continuously, until positions are filled, with a first evaluation round starting on the 14th of June, 2021. Thanks for distributing this announcement to potential candidates! References [1] Bertalmío, M., Calatroni, L., Franceschi, V. et al. Cortical-Inspired Wilson–Cowan-Type Equations for Orientation-Dependent Contrast Perception Modelling. J Math Imaging Vis 63, 263–281 (2021). https://arxiv.org/abs/1910.06808 [2] Boutin V, Franciosini A, Chavane F, Ruffier F, Perrinet L (2021) Sparse deep predictive coding captures contour integration capabilities of the early visual system.<https://laurentperrinet.github.io/publication/boutin-franciosini-chavane-ru…> PLoS Comput Biol 17(1): e1008629. https://doi.org/10.1371/journal.pcbi.1008629 -- Laurent Perrinet - INT (UMR 7289)/CNRS https://laurentperrinet.github.io/

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