- Comp-neuro - lists.cnsorg.org

Postdoctoral position - Pinto Lab @ Northwestern University

by Lucas Pinto

Dear colleagues, The Pinto Lab<http://www.pintolab.org> is seeking a postdoc who is excited about studying how neural circuits across many brain areas interact to support decision making, at the interface between experimental and computational neuroscience. In particular, the lab is interested in understanding the circuit mechanisms the control how these interactions are flexibly reconfigured when animals make decisions that use different underlying computations. To answer these questions, the candidate will use a combination high-throughput behavior in virtual reality, optical and genetic tools to measure and/or manipulate the dynamics of single neurons and neuronal populations, and computational approaches to understand both the behavior and its relationship to neural activity. We are a brand new lab that started in January 2021 at the Feinberg School of Medicine<https://www.feinberg.northwestern.edu/> at Northwestern University<http://www.northwestern.edu>, located in the heart of Chicago. Come join a vibrant neuroscience community<https://www.nuin.northwestern.edu/> in a world-class and highly livable city, working with a young PI committed to mentoring. Please visit our website<http://www.pintolab.org> for more details. Applicants should be nearing completion or already hold a PhD in neuroscience, biology, engineering, computer science, psychology or other related fields. Coding and/or experimental skills are preferred, but the applicant will have the opportunity to be trained in these techniques. We strongly encourage candidates from underrepresented groups to apply. Please send a CV, a one-page statement of research interests and skills, and contact information for 2-3 references to Lucas Pinto at lucas.pinto(a)northwestern.edu<mailto:lucas.pinto@northwestern.edu>. Best, Lucas - - - - - - - - - - - - - - - - - - - - - - - - - - - Lucas Pinto, M.D., Ph.D. Assistant Professor Department of Physiology Feinberg School of Medicine Northwestern University lucas.pinto(a)northwestern.edu<mailto:lucas.pinto@northwestern.edu> www.pintolab.org

June 15, 2021

NEST Conference 2021 — Registration Deadline 21 June

by Hans Ekkehard Plesser

Dear all, Just a little reminder that the registration deadline for the NEST Conference 2021 is on Monday next week, June 21. More information on the programme and how to register can be found on the conference website https://nest-simulator.org/conference This year's conference will again take place as a virtual conference on Monday/Tuesday 28/29 June 2021 followed by a virtual NEST User Hackathon until Friday 2 July. The NEST Conference provides an opportunity for the NEST Community to meet, exchange success stories, swap advice, learn about current developments in and around NEST spiking network simulation and its application. We are looking forward to seeing you all! Hans Ekkehard Plesser & many more -- Prof. Dr. Hans Ekkehard Plesser Head, Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway Phone +47 6723 1560 Email hans.ekkehard.plesser(a)nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no> Home http://arken.nmbu.no/~plesser

June 15, 2021

INCF/OCNS Software WG Dev session on SciUnit/NeuronUnit (June 21, 1600 UTC)

by Ankur Sinha

Dear all, Apologies for the cross posts. The INCF/OCNS Software WG[1] is happy to announce the next Dev Session where members of the developer teams of commonly used neuroscience software introduce their software and then discuss their *development* pipelines. ---- In the next Dev Session, Rick Gerkin will introduce the SciUnit framework and discuss its development: https://ocns.github.io/SoftwareWG/2021/06/04/dev-session-rick-gerkin-sciuni… The session will be held at June 21, 2021, 1600 UTC over Zoom: https://ucl.zoom.us/j/91246735902?pwd=WVdVK3I5SGdlaDFZdllDQ0w2djVBdz09 Click here to see the event in your local time: https://www.timeanddate.com/worldclock/fixedtime.html?msg=Dev+session%3A+Ri… SciUnit is a discipline-agnostic framework for model validation, handling all of the testing workflow by using a implementation-independent interface to models. SciUnit also contains code for visualization of model results, and command line tools for incorporating testing into continuous integration workflows. SciUnit is used in model validation in neuroscience via NeuronUnit, which implements an interface to several simulators and model description languages, handles test calculations according to domain standards, and enables automated construction of tests based on data from several major public data repositories. ------- The aim of these sessions is to stimulate discussion of the development practices and tools used by different teams to improve the software we use while also improving our knowledge of these practices and tools. We also hope to encourage more users of these tools to contribute to their development to ensure their longevity. If you develop software for neuroscience, we would love to hear about your development pipeline. Please get in touch with the Software WG on our communication channels[2]. The WG is a community based group that is open to everyone at all levels of their careers (academic or otherwise). Please introduce yourself to the community on our channels to get involved. [1] https://ocns.github.io/SoftwareWG/ [2] https://ocns.github.io/SoftwareWG/pages/contact.html On behalf of the Software WG, -- Thanks, Regards, Ankur Sinha (He / Him / His) Research Fellow at the Silver Lab | http://silverlab.org/ Department of Neuroscience, Physiology, & Pharmacology University College London, London, UK Time zone: Europe/London

June 14, 2021

Postdoc on Alzheimer's Disease modeling (Inria, UCL, BCAM)

by desroche

Dear all, This is an invitation to join a multidisciplinary research on Alzheimer’s Disease (AD) for a 24-month postdoc. The project aims at combining mathematics and novel experiments to better understand the causes of the progressive symptoms of memory impairment in persons living with AD. Despite progress, AD research has hit a bottleneck and to date there are no effective treatments to prevent AD. It is now recognised that this is partially due to limitations in AD mice models that do not recapitulate human AD, to conflicting results and research focus on molecular mechanisms. We aim to overcome these limitations by exploring a gab in our understading of abnormal synaptic process (including neural-glia, inflammatory processes) and neural tissue dynamics that lead to AD. To this end, we will combine state-of-the-art experiments based on human brain tissue data, recent mice model that recapitulate human AD, mathematics and computations, which in tandem will address the multi-factorial nature of the disease for future successful drug development. The project is part of a long-standing collaboration between Mathieu Desroches (MathNeuro Project-Team, Inria Sophia Antipolis, France), Serafim Rodrigues (MCEN research group, Basque Center for Applied Mathematics, Bilbao, Spain) and Afia B Ali (School of Pharmacology, University College London, UK) on neurotransmission and its possible disruptions (see here <https://www.pnas.org/content/113/8/E1108.short>). The postdoc will strongly interact with these three researchers. Regarding profiles that we are seeking, familiarity of the candidate with mathematical modeling in Neuroscience. Experience with multiple-timescale systems is wishable but not mandatory. Ability and willingness to do programming, as well as analyse experimental data, will also be a strong point to select candidates. Please apply online via the following page: https://recrutement.inria.fr/public/classic/fr/offres/2021-03775 <https://recrutement.inria.fr/public/classic/fr/offres/2021-03775> For informal enquiries, please contact me: mathieu.desroches(a)inria.fr <mailto:mathieu.desroches@inria.fr> --------------------------------------------------------------------- Dr. Mathieu Desroches Research Scientist (Chargé de Recherche) MathNeuro Project Team Leader <https://team.inria.fr/mathneuro/> Inria Sophia Antipolis - Méditerranée, France http://www-sop.inria.fr/members/Mathieu.Desroches/ <http://www-sop.inria.fr/members/Mathieu.Desroches/>

June 14, 2021

CFP Workshop on Spatio-temporal Aspects of Embodied Predictive Processing @ ICDL2021

by Stefan Heinrich

Call For Contributions - Workshop on Spatio-temporal Aspects of Embodied Predictive Processing @ IEEE ICDL2021 Aims and Scope: Understanding human intelligence and building strong AI systems is a key challenge for our generation. A particularly puzzling aspect is that the human brain seems to cope very well with the highly variable and uncertain nature of perception and action, regarding both their signal characteristic and how they extend over time. Furthermore, it seems apparent that the information processing in the brain always involves previous bodily experiences and all our senses, thus is embodied and crossmodal. For explaining these characteristics, there are strong accounts that the brain is constantly predicting sensory input and feedback while minimising free energy in the prediction and is hierarchically abstracting the perception as well as hierarchically composing action. Furthermore, it is also hypothesised that mechanistic priors in the brain’s information processing induce a failure of hierarchical inference in the brain, accounting for atypical perception and action of psychiatric disorders. For example, some brains might have developed to focus too strongly on current sensory input while others might focus too strongly on memorised previous experience. A typically developed brain, in contrast, would show a fine balance compared to these two extreme priors. The big open mystery is: how is the brain developing this on a mechanistic level and thus how can this get learned within an AI system? Thus, to lift this mystery, we further need to bring together research from computational neuroscience, cognitive psychology, and artificial intelligence. With this workshop, we particularly want to wrap up different recent hypotheses, models, and experiments and discuss in-depth how to shape future imaging, behavioural, and developmental robotics studies as a complement to computational modelling and bio-inspired artificial intelligent algorithms. As a guiding theme, we aim to approach the following central questions: - How does the brain learn spatio-temporal stochasticity in perception and action? - What is the role of priors in learning spatio-temporal adaptive prediction? - How can developmental robotics help us to study spatio-temporal stochasticity as an analogy to infant learning? We examine these questions with the insight from invited key speakers from complementary fields as well as original contributions in breakout sessions in order to conclude the next steps within in a panel discussion. All information online: https://sites.google.com/view/stepp21/home Keynotes/Panellists: - Jun Tani, Cognitive Neurorobotics, Okinawa Institute of Science and Technology (OIST) - Marcel van Gerven, Artificial Cognitive Systems, Donders Institute, Radboud University - Takuya Isomura, Brain Intelligence Theory, RIKEN Center for Brain Science - Takamitsu Watanabe, Brain Network Dynamics, International Research Center for Neurointelligence (IRCN), The University of Tokyo Contributions: We invite submissions of extended abstracts on ongoing research that focuses on one or several of our guiding questions as contributions. In addition to the live presentation and discussion during the workshop, the contributions will be made public in workshop proceedings. - Extended abstract submission deadline: 11.07.2021 - Decision & Notification: 25.07.2021 - Camera-ready version due: 08.08.2021 - Workshop online: 22.08.2021 Submission website: https://sites.google.com/view/stepp21/ => Contribute Organisers: Stefan Heinrich, IRCN, The University of Tokyo, Japan Shingo Murata, Dept. Electronics and Electrical Engineering, Keio University, Japan Yukie Nagai, IRCN & Institute for AI and Beyond, The University of Tokyo, Japan Yuichi Yamashita, National Center of Neurology and Psychiatry, Japan -- ************************************************************* Dr. Stefan Heinrich Postdoctoral Project Researcher Cognitive Developmental Robotics Lab The University of Tokyo, Institutes for Advanced Studies, International Research Center for Neurointelligence Email: heinrich.stefan(a)ircn.jp https://developmental-robotics.jp/en/members/stefan_heinrich/ https://ircn.jp/en/ https://stefanheinrich.net/ *************************************************************

June 14, 2021

Multiple postdoctoral openings in reinforcement learning in multi-scale brain networks at Washington University in St. Louis

by Ching, ShiNung

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

June 14, 2021

EITN Seminar: Paul Miller (Brandeis U, Waltham MA, USA) - Friday 18 June 16:00

by Zélie Tournoud

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

June 14, 2021

Adaptive Machines: Leveraging Neuroscience for Lifelong Learning Systems

by Andrea Soltoggio

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

June 14, 2021

be a mentor for Neuromatch Academy!

by Marius Pachitariu

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/

June 11, 2021

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

by Reichenbach, Tobias

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

June 11, 2021