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[CFP] MDPI Molecules (IF: 4.411, ISSN: 1420-3049) - Special Issue "Practical Aspects of Molecular Communications"

by Murat Kuşcu

MDPI Molecules (IF: 4.411, ISSN: 1420-3049) Special Issue on Practical Aspects of Molecular Communications https://www.mdpi.com/journal/molecules/special_issues/molecular_communicati… --------------------------- CALL FOR PAPERS The Internet of Bio-Nano Things (IoBNT) is an emerging technology that aims to extend our connectivity to nanoscale and biological environments with collaborative nanonetworks of artificial nanomachines and biological entities integrated into the Internet infrastructure. To enable the IoBNT and its groundbreaking applications, such as continuous intrabody health monitoring, it is imperative to devise low-complexity nanoscale communication techniques suitable for the envisioned nanomachines of simple architectures. The most promising communication technology for realizing the IoBNT is Molecular Communications (MC), as it ubiquitously manifests itself in many complex biological systems in the Universe, and thus, stands as one of the most common communication modalities, optimized from many aspects as a result of billions of years of evolutionary advancement. Making use of this naturally existing technology requires understanding its foundations through our existing modeling and analysis tools. This quest, which started almost 15 years ago, has received increasing attention from ICT researchers, which have been overly inspired by conventional electromagnetic communication technologies in their approach to this radically different paradigm. These theoretical approaches, however, have not always come with sufficient physical relevance. Currently, this emerging field has come to a critical turning point, as many researchers have started to report on initial MC experiments following different approaches and using different materials, while consistently pointing out a discrepancy between the obtained experimental results and the past theoretical work. This reveals the need to rethink the previous efforts and come up with new interdisciplinary strategies, thus building practical MC techniques and developing feasible MC system components, experimental testbeds, and prototypes in order to close the gap between theory and practice, and expedite the transfer of this emerging technology to the market. Among the challenges stated above, this Special Issue is particularly focused on the practical aspects of MC. Therefore, we are calling for technical papers that report on new research with the potential to move this field forward towards its practical applications, as well as surveys/tutorials focusing on the practical challenges of MC research. Hence, its scope encompasses a wide range of interdisciplinary research topics, with some examples listed as follows: ** The physical design, modeling, and implementation of MC system components (e.g., transmitter, receiver, channel); ** The design and implementation of MC testbeds; ** Practical and low-complexity MC methods (e.g., modulation, detection, channel estimation, synchronization, coding methods); ** The testing and validation of MC transceiver/channel models and MC methods; ** Applications of microfluidics, biosensors, and nanomaterials for MC system design; ** Bio-cyber interfaces between MC networks and conventional macroscale networks; ** Synthetic biology-based MC transceiver architectures; ** Optimization of ligand-receptor interactions for MC; ** Biocompatibility and co-existence challenges; ** Energy harvesting and power transfer techniques for MC networks; ** The design and demonstration of MC applications (e.g., those in healthcare, agriculture, biocomputing). --------------------------- Deadline for manuscript submissions: 15 December 2021 --------------------------- Guest Editors Murat Kuscu, Koc University, Turkey (mkuscu(a)ku.edu.tr) Sasitharan Balasubramaniam, TSSG/Walton Institute, Waterford Institute of Technology (sasi.bala(a)waltoninstitute.ie) Kerstin Lenk, Graz University of Technology, Austria (kerstin.lenk(a)tugraz.at ) Ergin Dinc, University of Cambridge, UK (ed502(a)cam.ac.uk) Michael Barros, University of Essex, UK (m.barros(a)essex.ac.uk) Bige D. Unluturk, Michigan State University, USA (unluturk(a)msu.edu) --------------------------- Paper submission Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI. Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions. --------------------------- Additional information: Please visit the MDPI Molecules Special Issue webpage at: https://www.mdpi.com/journal/molecules/special_issues/molecular_communicati… Inquiries can be addressed to Ms. Emity Wang at: emity.wang(a)mdpi.com, molecules(a)mdpi.com

Sept. 20, 2021

New "Developing Minds" global online lecture series; September 30: Pierre-Yves Oudeyer

by Jochen Triesch

What: New bi-monthly "Developing Minds" global online lecture series; inaugural lecture by Pierre-Yves Oudeyer, INRIA, Bordeaux Sud-Ouest, France on "Developmental AI: machines that learn like children and help children learn better" When: Thursday, September 30, 13:00 UTC Where: online via zoom; please register at: https://sites.google.com/view/developing-minds-series Background: In 1950, Alan Turing asked "Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's?" Today, over 70 years later, constructing a computer program that can learn like a child and that develops a human-like general intelligence is still considered a grand, if not the ultimate, challenge for artificial intelligence (AI). An interdisciplinary community of scientists from AI, Cognitive Science, Psychology, Engineering, and Neuroscience are tackling this grand challenge. In the Developing Minds global lecture series we showcase the progress being made. It is organized by the IEEE Technical Committee on Cognitive and Developmental Systems of the IEEE Computational Intelligence Society. The inaugural lecture by Pierre-Yves Oudeyer is entitled: "Developmental AI: machines that learn like children and help children learn better" Abstract: Current approaches to AI and machine learning are still fundamentally limited in comparison with the amazing learning capabilities of children. What is remarkable is not that some children become world champions in certains games or specialties: it is rather their autonomy, open-endedness, flexibility and efficiency at learning many everyday skills under strongly limited resources of time, computation and energy. And they do not need the intervention of an engineer for each new task (e.g. they do not need someone to provide a new task specific reward function or representation). I will present a research program, which I call Developmental AI, that studies models of open-ended development and learning. These models are used as tools to help us understand better how children learn, as well as to build machines that learn like children with applications in educational technologies, automated discovery, robotics and human-computer interaction. I will ground this research program into several fundamental ideas proposed by developmental psychologists: 1) the child is autotelic, setting its own goals and spontaneously exploring the world like a curious little scientist (e.g. Piaget, Berlyne); 2) intelligence develops in a social context, where language and culture are internalized to become cognitive tools (e.g. Vygostky and Bruner); 3) intelligence is embodied and develops through self-organization of the dynamical system formed by the brain-body-environment interactions (e.g. Thelen and Smith). I will show how, together with many colleagues and students, we have worked on operationalizing these ideas in computer and robotic models, and explain how this has enabled to advance child development understanding, open new possibilities for AI systems (including for recent language-guided intrinsically motivated Deep RL systems and for training more classical Deep RL systems to foster generalization), and led to applications now used by thousands of children in the world to help them learn (educational technologies). Speaker Bio: Dr. Pierre-Yves Oudeyer has been Research Director at Inria, France, and head of the Flowers lab (Inria, Univ. Bordeaux, Ensta ParisTech). Before, he has been a permanent researcher in Sony Computer Science Laboratory for 8 years (1999-2007). He studies models of open-ended development and learning, at the frontiers of AI and cognitive sciences. These models are used as tools to help us understand better how children learn, as well as to build machines that learn like children within the field of developmental artificial intelligence. He has been developing models of intrinsically motivated learning, pioneering curiosity-driven learning algorithms working in real world robots, and developed theoretical frameworks to understand better human curiosity and autonomous learning. He also studied mechanisms enabling machines and humans to discover, invent, learn and evolve language. He is also working on applications in educational technologies, automated discovery, video games, robotics and human-computer interaction. He received several prizes for his work in developmental AI and on the origins of language. In particular, he is laureate of the Inria-National Academy of Science young researcher prize in computer sciences, and of an ERC Starting Grant EXPLORERS. Finally, he is also working actively for the diffusion of science towards the general public, through the writing of popular science articles and participation to radio and TV programs as well as science exhibitions. Web: http://www.pyoudeyer.com and http://flowers.inria.fr Upcoming speakers: Linda B. Smith, Indiana University Joshua B. Tenenbaum, MIT Please visit https://sites.google.com/view/developing-minds-series/home for up-to-date information. -- Prof. Dr. Jochen Triesch Johanna Quandt Research Professor Frankfurt Institute for Advanced Studies http://fias.uni-frankfurt.de/~triesch/ Tel: +49 (0)69 798-47531 Fax: +49 (0)69 798-47611

Sept. 19, 2021

CFP - Annual Conference of Cognitive Science (ACCS8) - Jan 20--22, 2022, India

by Shyam Diwakar

Dear All, As the local organizer, it is our pleasure to invite you to participate at ACCS8. Here's more details. ACCS8: Call for contributions 8th Annual Conference of Cognitive Science (ACCS8) Amrita University, India – 20 – 22 January 2022 https://www.amrita.edu/accs8 The 8th edition of ACCS, to be hosted by Amrita Vishwa Vidyapeetham (Amrita University), Kollam, India will be held virtually. List of our invited speakers, TPC and other info will soon be made available at https://www.amrita.edu/event/accs8 CONFERENCE TOPICS Like in our previous years, the ACCS8 conference is open to all topics with Cognitive Science as a discipline, and in various areas of study, including Neuroscience, Artificial Intelligence, Linguistics, Skilling, Medicine, Anthropology, Psychology, Philosophy, and Education. SUBMISSION GUIDELINES We welcome submissions from all areas of cognitive science and from anyone worldwide. For the 2022 online event, we are accepting only abstracts containing the salient details of your study. Please copy and paste the title and abstract into the submission form. Contribution size limit for the abstract is 1000 words. We are not accepting any paper-length submissions this year, so do include all relevant details in the abstract itself. A small number of abstracts will be selected for oral presentations/talks. Submission link: https://easychair.org/conferences/?conf=accs8 For CFP: https://easychair.org/cfp/accs8 IMPORTANT DATES Abstract registration deadline November 25, 2021 Submission deadline November 25, 2021 BEST PAPER AWARDS We are planning some best poster and oral presentations. Please stay tuned. CONTACT All questions about submissions should be emailed to accs8conference(a)gmail.com<mailto:accs8conference@gmail.com> Previous ACCS events - https://www.amrita.edu/event/accs8/about With best regards, Shyam Diwakar -- Prof. Shyam Diwakar, Ph.D. Director - Amrita Mind Brain Center Faculty Fellow - Amrita Center for International Programs Amrita Vishwa Vidyapeetham (Amrita University) Amritapuri, Clappana P.O. Kollam, India. Pin: 690525 Ph:+91-476-2803116 Fax:+91-476-2899722 http://amrita.edu/mindbrain [https://docs.google.com/uc?export=download&id=1_nOxaCXob6wVcuQyYI5KVk1PhMgi…] [https://intranet.cb.amrita.edu/sig/RankingLogo.png] Disclaimer : The information transmitted in this email, including attachments, is intended only for the person(s) or entity to which it is addressed and may contain confidential and/or privileged material. Any review, retransmission, dissemination or other use of, or taking of any action in reliance upon this information by persons or entities other than the intended recipient is prohibited. Any views expressed in any message are those of the individual sender and may not necessarily reflect the views of Amrita Vishwa Vidyapeetham. If you received this in error, please contact the sender and destroy any copies of this information.

Sept. 18, 2021

(Call for papers) Adaptive Machines: Leveraging Neuroscience for Lifelong Learning Systems

by Andrea Soltoggio

Dear All, This is a reminder of the upcoming deadline (30th of September) to submit to the Research Topic “Adaptive Machines: Leveraging Neuroscience for Lifelong Learning Systems”, co-hosted by Frontiers in Neuroscience, Robotics and AI, Big Data, Artificial Intelligence, Neurorobotics and Computational Neuroscience. 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 is 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

Sept. 17, 2021

ISRC Computational Neuroscience, Neurotechnology and Neuro-inspired AI Autumn School (ISRC-CN3)

by Wong-Lin, Kongfatt

Dear All, We are excited to announce that our ISRC Computational Neuroscience, Neurotechnology and Neuro-inspired AI Autumn School (ISRC-CN3) is now live and open for registration at the following link: https://store.ulster.ac.uk/product-catalogue/faculty-of-computing-engineeri… This Autumn School (from 25th October to 29th October), which will be conducted in a hybrid format, is organised by our Intelligent Systems Research Centre (ISRC) at Ulster University, Magee campus. More details can be found at the website: https://sites.google.com/view/isrc-cn3/home<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsites.goo…> If you have any further queries about this event, please contact Dr. KongFatt Wong-Lin (k.wong-lin(a)ulster.ac.uk) This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster(a)ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA

Sept. 17, 2021

Postdoctoral Associate in Neural Circuits of Learning and Memory

by Yuri Dabaghian

NIH-funded postdoctoral position is immediately available in the group of Dr. Yuri Dabaghian in the Department of Neurology at McGovern Medical School of the University of Texas, Houston. We are looking for scientists with research interests at the intersection of mathematics, physics, and neuroscience, in the general area of emergent phenomena and representations, network dynamics, topological data analyses. Some projects aim to explain electrophysiological data patterns, some are theoretically motivated. Specifically, we are interested in circuit mechanisms of learning and memory and their involvement in neurological disorders, notably Alzheimer's Disease. Successful candidates will develop data-driven hippocampal neuronal network models explaining spatial learning dynamics, develop and creatively apply tools for the data analysis including novel methods of brain waves (EEG) analyses, Topological Data Analysis of spiking data, etc. The Position requires strong background in quantitative disciplines (PhD and ongoing interest in computational or theoretical neuroscience, physics, mathematics, or related), plus willingness to do programming and to analyze experimental data. Previous experiences in data analyses are preferred but not required. All Appointments are initially for one year and are renewable for at least three years given satisfactory performance. The salary is competitive. The postdoc will be integrated into the large, vibrant neuroscience community of the Texas Medical Center and have the opportunity to participate in collaborations with groups at Baylor College of Medicine and Rice University. To apply, please send application materials including a detailed CV, a brief statement of research interests, and contact information of 3 references to Dr. Yuri Dabaghian at [Yuri.A.Dabaghian [at] uth.tmc.edu) The position is available immediately, until filled. For Additional information and informal inquiries, please contact Dr. Dabaghian. We Strive for a diverse and inclusive environment, and encourage applications from members of any identity.

Sept. 17, 2021

COSYNE 2022: Meeting announcement

by Tomas Hromadka

==================================================== Computational and Systems Neuroscience 2022 (Cosyne) MAIN MEETING 17 - 20 March 2022 Lisbon, Portugal WORKSHOPS 21 - 22 March 2022 Cascais, Portugal www.cosyne.org ==================================================== ---------------------------------------------------- MEETING ANNOUNCEMENT ---------------------------------------------------- The annual Cosyne meeting provides an inclusive forum for the exchange of empirical and theoretical approaches to problems in systems neuroscience, in order to understand how neural systems function. The MAIN MEETING is single-track. A set of invited talks is selected by the Executive Committee, and additional talks and posters are selected by the Program Committee, based on submitted abstracts. The WORKSHOPS feature in-depth discussion of current topics of interest, in a small group setting. All abstract submissions will be reviewed double blind. The deadline for Abstract submission will be 20 November 2021. Cosyne topics include but are not limited to: neural basis of behavior, sensory and motor systems, circuitry, learning, neural coding, natural scene statistics, dendritic computation, neural basis of persistent activity, nonlinear receptive field mapping, representations of time and sequence, reward systems, decision-making, synaptic plasticity, map formation and plasticity, population coding, attention, neuromodulation, and computation with spiking networks. We would like to foster increased participation from experimental groups as well as computational ones. Please circulate widely and encourage your students and postdocs to apply. IMPORTANT DATES Abstract submission opens: 15 October 2021 Travel grant submission opens: 01 November 2021 Abstract submission deadline: 20 November 2021 When preparing an abstract, authors should be aware that not all abstracts can be accepted for the meeting. Abstracts will be selected based on the clarity with which they convey the substance, significance, and originality of the work to be presented. ORGANIZING COMMITTEE General Chairs: Anne-Marie Oswald (U Pittsburgh) and Srdjan Ostojic (Ecole Normale Superieure Paris) Program Chairs: Laura Busse (LMU Munich) and Tim Vogels (IST Austria) Workshop Chairs: Anna Schapiro (U Penn) and Blake Richards (McGill) Tutorial Chair: Kanaka Rajan (Mount Sinai) DEIA Committee: Bianca Jones Martin (Columbia), Gabrielle Gutierrez (Columbia), and Stefano Recanatesi (U Washington) Undergraduate Travel Chairs: Angela Langdon (Princeton) and Bob Wilson (U Arizona) Fundraising Chair: Michael Long (NYU) Social Media Chair: Grace Lindsay (Columbia) Poster Design: Maja Bialon PROGRAM COMMITTEE Laura Busse (U Munich) Tim Vogels (IST Austria) Athena Akrami (UCL) Omri Barak (Technion) Brice Bathellier (Paris) Bing Brunton (U Washington) Yoram Burak (Hebrew University) SueYeon Chung (Columbia) Christine Constantinople (NYU) Victor de Lafuente (UNAM Mexico) Jan Drugowitsch (Harvard) Alexander Ecker (Göttingen) Tatiana Engel (Cold Spring Harbor) Annegret Falkner (Princeton) Kevin Franks (Duke) Jens Kremkow (Berlin) Andrew Leifer (Princeton) Sukbin Lim (Shanghai) Scott Linderman (Stanford) Emilie Mace (MPI Neurobiology) Mackenzie Mathis (EPFL Lausanne) Ida Momennejad (Microsoft) Jill O'Reilly (Oxford) Il Memming Park (Stony Brook) Adrien Peyrache (McGill Montréal) Yiota Porazi (FORCE) Nathalie Rochefort (Edinburgh) Christina Savin (NYU) Daniela Vallentin (MPI Ornithology) Brad Wyble (U Penn) EXECUTIVE COMMITTEE Stephanie Palmer (U Chicago) Zachary Mainen (Champalimaud) Alexandre Pouget (U Geneva) Anthony Zador (CSHL) CONTACT meeting [at] cosyne.org COSYNE MAILING LISTS Please consider adding yourself to Cosyne mailing lists (groups) to receive email updates with various Cosyne-related information and join in helpful discussions. See Cosyne.org -> Mailing lists for details.

Sept. 16, 2021

Online workshop "Towards multipurpose neural network models II: Model testing and model fitting"

by Anton Arkhipov

Dear Colleagues, Registration is now open for the online workshop "Towards multipurpose neural network models II: Model testing and model fitting" organized by Gaute Einevoll (NMBU/University of Oslo) and Anton Arkhipov (Allen Institute, Seattle) and supported by the European Institute for Theoretical Neuroscience (EITN). Please join us for three days of talks about cutting-edge science by fantastic speakers, as well as panel discussions. Dates: September 29 – October 1, 2021 Time: 8AM – 12:15PM US Pacific time / 5PM – 9:15PM Central European Time Daily. Registration: https://cnrs.zoom.us/webinar/register/WN_saMXfg31Roe4tqHbWtTdFg Please see the agenda below and more details at this web page: https://www.eitn.org/index.php/calendar-event/eventdetail/750/-/workshop-on… We hope to see you there! Wednesday, September 29, 2021 Gaute Einevoll (NMBU/U. Oslo) Introduction Jacob Macke (U. Tübingen) Keynote. Simulation-based inference: Bridging the gap between mechanistic models and machine learning. Aaron Milstein (Rutgers U.) Nested parallel simulation and multi-objective optimization of neuronal cell and circuit models Atle Rimehaug (U. Oslo) Enhancing model constraints by utilizing current source densities Kristin Tøndel (NMBU) Facilitating optimization using metamodelling Frances Skinner (Krembil Brain Institute, University Health Network, and University of Toronto) Clarity in model development and goals leads to model linkages and biological insights Panel debate – All participants of the day. Thursday, September 30, 2021 Anton Arkhipov (Allen Institute) Introduction James DiCarlo (MIT) Keynote. Reverse Engineering Visual Intelligence Sharon Crook (Arizona State U.) Testing the Data-driven Model Szabolcs Kali (Institute of Experimental Medicine, Budapest, Hungary) Systematic construction and evaluation of models of rodent hippocampal neurons Peter Jedlicka (U. Giessen) Building consistent and robust models of hippocampal granule cells and CA1 pyramidal cells Stefan Mihalas (Allen Institute) Computing with a mess: How nonstationary, heterogeneous and noisy components help the brain’s computational power Panel debate – All participants of the day. Friday, October 1, 2021 Markus Covert (Stanford) Special lecture. Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation Kanaka Rajan (Mount Sinai) Keynote. Data-constrained neural network models of adaptive learning in the brain Julijana Gjorgjeva (Max Planck Institute and TUM) Biologically plausible learning in developing networks Carsen Stringer (Janelia) Rastermap: Extracting structure from high-dimensional neural data Arvind Kumar (KTH Stockholm) Structure and activity dynamics relationship in biological neuronal networks: Measurements and models Panel debate – All participants of the day.

Sept. 14, 2021

PhD position in computational neuroscience/machine learning at UC Santa Barbara

by Michael Beyeler

The Bionic Vision Lab lab at the University of California, Santa Barbara (UCSB) invites applications for a fully funded PhD position to develop computational models of neural activity in mice navigating through real-world and virtual environments. The successful candidate will have a strong quantitative background that includes machine learning, programming experience, and a thorough interest in neuroscience. This is an opportunity to work on an impactful real-world problem within a highly interdisciplinary and collaborative environment. Interested applicants should send a CV and transcript (unofficial is fine) to Michael Beyeler (mbeyeler(a)ucsb.edu) Official applications will have to be submitted through UCSB Grad Admissions before the application deadline indicated below. All genders welcome. Application deadline: December 1, 2021 Start date: Fall 2022 UCSB is a dynamic research institution, particularly in physical sciences and engineering, and was recently rated one of the Top 5 public schools in the United States (US News & World Report). Neuroscience research is well established here, featuring >35 faculty members whose research interests span the range from molecular to theoretical neuroscience.

Sept. 14, 2021

Call for community feedback on the endorsement of Neurolucida XML as a standard

by Malin Sandström

Dear all, INCF is seeking community feedback and comments on the review of the MBF neuromorphological file format v 4.0, a format for digital reconstruction & modeling structure for microscopic anatomies, also known as “Neurolucida XML”, which has been submitted to INCF for endorsement as a community standard. If you have experience of using Neurolucida XML, we ask you to read the review report and comment based on your experiences, especially if you share or reuse data in the format, or work on models or tools that use the format. Link to the review: https://f1000research.com/documents/10-712 *Why endorse community standards? * INCF (incf.org) has implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. Our mission is to make neuroscience more open and FAIR, to ensure that research funds and efforts are well invested, and that neuroscientific findings are robust and replicable. Open community review is a very important step in the process. Quality community standards are necessary to make FAIR resources and processes work, but too many neuroscience communities lack robust standards or have competing incompatible standards. The rapid development of new techniques also means that there is a continuous need for new and updated standards, and that old standards need an active developer and user community keeping them up to date. By endorsing standards, INCF wants to make it easy to find the best, most reliable standard appropriate for your research, and ensure recognition for community members investing their time and effort in standards. The review criteria (https://incf.org/incf-standards-review-criteria-v20) include Openness; FAIRness; Design, Testing and Implementation; Governance; Adoption and use; Stability and support; Extensibility; Comparison (similarity to other standards). -- Malin Sandström, PhD Community Engagement Officer malin.sandstrom(a)incf.org ORCID: 0000-0002-8464-2494 International Neuroinformatics Coordinating Facility Karolinska Institutet Nobels väg 15 A SE-171 77 Stockholm Sweden http://www.incf.org

Sept. 14, 2021

workshop on Brain Criticality: The Past, the Present and the Future

by Claudius Gros

You are invited to attend the upcoming Bernstein workshop on Brain Criticality: The Past, the Present and the Future https://www.bernstein-network.de/en/bernstein-conference/2021-online/satell… Tuesday, September 21, 2021, 2pm CEST It is free, but you need to register with the Bernstein conference (see link above). Claudius Gros ### ### 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 ###

Sept. 14, 2021

Bernstein Conference 2021 - abstracts are published

by Anja Dorrn

Each year the Bernstein Network invites the international computational neuroscience community to the annual Bernstein Conference for intensive scientific exchange. It has established itself as one of the most renown conferences worldwide in this field, attracting students, postdocs and PIs from around the world to meet and discuss new scientific discoveries. Due to the ongoing pandemic the Bernstein Conference will be held online. www.bernstein-conference.de ____ IMPORTANT DATES & NEWS * Bernstein Conference: September 21-23, 2021 * Abstracts are published * Registration is open and free of charge * PhD Symposium on September 24, 2021 ____ ABSTRACTS All accepted abstracts are now published online and are citable via Digital Object Identifiers (DOI). Please find them here: https://abstracts.g-node.org/conference/BC21/abstracts#/ ____ REGISTRATION The participation at the online Bernstein Conference 2021 will be free of charge for all participants. Thus the Bernstein Network contributes to keep the scientific exchange alive in the ongoing challenges during the pandemic. Please register here: https://hopin.com/events/bernstein-conference-2021 ____ PHD SYMPOSIUM The Bernstein PhD Symposium on September 24, 2021 is an opportunity for junior researchers to meet their peers, share their experience and make new connections. This year's topic is Gaming meets neuroscience. Please register here: https://hopin.com/events/bernstein-phd-symposium-2021 ____ INVITED SPEAKERS Keynote Timothy Behrens (University of Oxford, UK) Invited Talks Athena Akrami (University College London, UK) Nadine Gogolla (Max Planck Institute for Neurobiology, Martinsried, Germany) Guillaume Hennequin (University of Cambridge, UK) Jennifer Li & Drew Robson (Max Planck Institute for Biological Cybernetics, Tübingen, Germany) Scott Lindermann (Stanford University, USA) Ashok Litwin-Kumar (Columbia University, New York City, USA) Ida Momennejad (Microsoft Research, New York City, USA) Cristina Savin (New York University, USA) Marion Silies (Johannes Gutenberg University, Mainz, Germany) Daniela Vallentin (Max Planck Institute for Ornithology, Seewiesen, Germany) ____ CONFERENCE COMMITTEE Christian Leibold (Conference Chair) Julijana Gjiorgjieva (Program Chair) Christian Machens (Program Vice Chair) Marion Silies (Workshop Chair) Moritz Helias (Workshop Vice Chair) & Matthew Botvinick, Megan Carey, Peter Dayan, Brent Doiron, Rainer Friedrich, Ann Hermundstad, Srdjan Ostojic, Nathalie Rochefort, Nicole Rust, Gasper Tkacik ____ For any further questions, please contact: bernstein.conference(a)fz-juelich.de -- Dr. Anja Dorrn Scientific Coordinator Bernstein Network Computational Neuroscience | Bernstein Coordination Site (BCOS) Branch Office of the Forschungszentrum Jülich at the University of Freiburg Hansastr. 9A | 79104 Freiburg, Germany phone: (+49) 0761 203 9589 web: www.bernstein-network.de<http://www.bernstein-network.de> ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Dr. Astrid Lambrecht, Prof. Dr. Frauke Melchior ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

Sept. 13, 2021

Biologically plausible models of path planning in humans and rodents

by Jeffrey Krichmar

Dear Colleagues, We recently published two papers on biologically plausible models of path planning that simulate and analyze navigation strategies in rodents and humans. Some of the ideas presented in these papers may be applicable to autonomous systems. If you are interested, please see: Krichmar, J.L., and He, C. (2021). Importance of Path Planning Variability: A Simulation Study. Topics in Cognitive Science. https://onlinelibrary.wiley.com/doi/10.1111/tops.12568 Individuals vary in the way they navigate through space. Some take novel shortcuts, while others rely on known routes to find their way around. We wondered how and why there is so much variation in the population. To address this, we first compared the trajectories of 368 human subjects navigating a virtual maze with simulated trajectories. The simulated trajectories were generated by strategy-based path planning algorithms from robotics. Based on the similarities between human trajectories and different strategy-based simulated trajectories, we found that there is a variation in the type of strategy individuals apply to navigate space, as well as variation within individuals on a trial-by-trial basis. Moreover, we observed variation within a trial when subjects occasionally switched the navigation strategies halfway through a trajectory. In these cases, subjects started with a route strategy, in which they followed a familiar path, and then switched to a survey strategy, in which they took shortcuts by considering the layout of the environment. Then we simulated a second set of trajectories using five different but comparable artificial maps. These trajectories produced the similar pattern of strategy variation within and between trials. Furthermore, we varied the relative cost, that is, the assumed mental effort or required timesteps to choose a learned route over alternative paths. When the learned route was relatively costly, the simulated agents tended to take shortcuts. Conversely, when the learned route was less costly, the simulated agents showed preference toward a route strategy. We suggest that cost or assumed mental effort may be the reason why in previous studies, subjects used survey knowledge when instructed to take the shortest path. We suggest that this variation we observe in humans may be beneficial for robotic swarms or collections of autonomous agents during information gathering. Krichmar, J.L., Ketz, N.A., Pilly, P.K., and Soltoggio, A. (2021). Flexible Path Planning through Vicarious Trial and Error. bioRxiv, 2021.2009.2008.459317. https://www.biorxiv.org/node/2148912.abstract Flexible planning is necessary for reaching goals and adapting when conditions change. We introduce a biologically plausible path planning model that learns its environment, rapidly adapts to change, and plans efficient routes to goals. Unlike prior models of hippocamapl replay, our model addresses the decision-making process when faced with uncertainty. We tested the model in simulations of human and rodent navigation in mazes. Like the human and rat, the model was able to generate novel shortcuts, and take detours when familiar routes were blocked. Similar to rodent hippocampus recordings, the neural activity of the model resembles neural correlates of Vicarious Trial and Error (VTE) during early learning or during uncertain conditions. Similar to rodent studies, after learning, the neural activity resembles forward replay or preplay predicting a future route, and VTE activity decreases. We suggest that VTE, in addition to weighing possible outcomes, is a way in which an organism may gather information for future use. Best regards, Jeff Krichmar Department of Cognitive Sciences 2328 Social & Behavioral Sciences Gateway University of California, Irvine Irvine, CA 92697-5100 jkrichma(a)uci.edu http://www.socsci.uci.edu/~jkrichma

Sept. 10, 2021

Postdoctoral position available

by Sara A. Solla

*Integrating Human and Machine Learning for Enabling Co-Adaptive * *Body-Machine Interfaces* * Sandro Mussa-Ivaldi and Sara A. Solla PI’s * *Northwestern University* We are seeking postdoctoral applicants with interests and experience in computational methods, machine learning, and human-machine interactions to participate in an NSF-funded project. The goal of the project is customizing human-machine interfaces through a collaborative interaction between human and machine learning. This has relevance to the recovery of function after injury to the spinal cord. The same goals apply also to non-clinical domains. Common to the different domains is the necessity to learn novel movement skills. The project will consider how this challenge is met through the interaction with an adaptive interface, the body-machine interface, that maps body motions onto commands to external devices. The working hypotheses are H1) that through a dynamical process of learning the human operators form an internal model of the interface and connected device, and H2) that the human machine interface can be adapted to its users by an update rule that tracks the learning process of the human operator. The success of the research project will lead to a new family of intelligent human machine interfaces capable of customizing themselves to the evolving ability of their users. Applications, including a brief statement, a bio sketch, and the names of two references, should be sent to bomiproject2021(a)gmail.com

Sept. 10, 2021

Postdoc position on neural mechanisms of flexible behavior

by Luca Mazzucato

Dear colleagues, We are looking for a computational neuroscience postdoc to lead a project on the neural mechanisms of flexible behavior. The theory position will be co-supervised by Luca Mazzucato and David McCormick and funded by the NIH BRAIN Initiative collaboration between the McCormick, Niell, Jaramillo and Mazzucato labs at University of Oregon. The project aims at elucidating the whole-brain dynamics enabling flexible modality switching in an audiovisual decision-making task. A successful candidate will use state-space models and multi-area recurrent network models to elucidate the activity and functional connectivity of cortical circuits from large datasets in behaving mice (2-photon mesoscope with ~10k neurons; multiple neuropixel probes). Experience with deep learning is recommended for integrating behavioral analyses with task performance and neural activity. The postdoc will be part of the vibrant neuroscience and machine learning community at the University of Oregon including the Institute of Neuroscience <https://ion.uoregon.edu/> and the Knight Campus <https://accelerate.uoregon.edu/> for Accelerating Science. The University is located in beautiful Eugene, the country's leader in the "20-minute friendly <https://tinyurl.com/y8jd8pmr>" bike commute movement, at close range between the Cascades ski areas and the Pacific Ocean. Successful candidates will have a PhD in Physics, Neuroscience, Mathematics, Computer Science, Statistics or a related discipline and, preferably, experience in theoretical neuroscience and statistical analysis of large neuroscience datasets. For inquiries please contact Luca Mazzucato or David McCormick.

Sept. 5, 2021

8th Baltic-Nordic Summer School on Neuroscience and Neuroinformatics (BNNI 2021)

by Pawel Herman

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

Sept. 5, 2021

DEADLINE EXTENDED: Summer School on Network and Control Sciences for Psychiatry

by Jasper Bischofberger

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

Sept. 2, 2021

Faculty Position in Computational Neuroscience at Bristol University.

by Conor Houghton

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/

Aug. 31, 2021

PhD position at the Goethe University Frankfurt, Germany

by Claudius Gros

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 ###

Aug. 30, 2021

Ph.D. student / Postdoc position in machine learning with spiking neurons at University of Bremen, Germany

by David Rotermund

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

Aug. 30, 2021

Postdoc / Staff Scientist in brain imaging during natural behavior

by Geoffrey Goodhill

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>

Aug. 28, 2021

Post-doctoral Position in Human Perceptual Neuroscience at NYU

by Hardstone, Richard

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.

Aug. 27, 2021

2nd SMILES workshop, Aug 31st: Sensorimotor Interaction, Language and Embodiment of Symbols

by Xavier Hinaut

* 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

Aug. 27, 2021

Recorded talks from TEX2021 - Bringing together Predictive Processes and Statistical Learning

by Yamil Vidal

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

Aug. 26, 2021

CfP: Call for Paper Frontiers in Neuroscience: Special Issue "Graph Learning for Brain Imaging"

by Feng Liu

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

Aug. 25, 2021