September 2018 - Comp-neuro - lists.cnsorg.org

Creating a list of important computational predictions
by Yiota Poirazi 04 Jan '21

04 Jan '21

Dear all, I believe you would agree that, the greatest recognition of our computational work comes with the experimental verification of our computational predictions. To highlight the most important outcomes of our theoretical work and facilitate a closer interaction with experimental labs so as to increase the chances of our predictions being experimentally tested, I would like to encourage you to nominate your favorite prediction at the following google form: https://docs.google.com/forms/d/e/1FAIpQLSeJZ2RrXJ0nZonIuyNzN0Cl gXDKDb7xTzoJOiqZTGxm9Kh7ow/viewform?c=0&w=1&usp=mail_form_link This effort started during the CNS 2017 meeting in Antwerp with the support of the OCNS Board. The resulting list will be posted on the CNS 2017 web site and disseminated in social media, as a reference for experimentalists. Thank you very much for supporting this important effort! Best wishes, Yiota Poirazi -- Panayiota Poirazi, Ph.D. Research Director Computational Biology Laboratory Institute of Molecular Biology and Biotechnology (IMBB) Foundation of Research and Technology-Hellas (FORTH) Vassilika Vouton, P.O.Box 1385, GR 70013, Heraklion, Crete GREECE Tel: +30 2810 391139 Fax: +30 2810 391101 Εmail: poirazi(a)imbb.forth.gr Lab site: www.dendrites.gr FENS-Kavli Network of Excellence <http://www.fens.org/Outreach/FENS-Kavli-Network-of-Excellence/> -- This message has been scanned for viruses and dangerous content by *E.F.A. Project* <http://www.efa-project.org>, and is believed to be clean. Click here to report this message as spam. <http://mailgate1.imbb.forth.gr/cgi-bin/learn-msg.cgi?id=4BCD7A00A1.A57D7&to…> -- Panayiota Poirazi, Ph.D. Research Director Computational Biology Laboratory Institute of Molecular Biology and Biotechnology (IMBB) Foundation of Research and Technology-Hellas (FORTH) Vassilika Vouton, P.O.Box 1385, GR 70013, Heraklion, Crete GREECE Tel: +30 2810 391139 Fax: +30 2810 391101 Εmail: poirazi(a)imbb.forth.gr Lab site: www.dendrites.gr FENS-Kavli Network of Excellence <http://www.fens.org/Outreach/FENS-Kavli-Network-of-Excellence/>

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Postdoc positions on Consciousness Research at Monash Uni, Australia
by Naotsugu Tsuchiya 13 Jan '20

13 Jan '20

Position A: DP180100396 (in collaboration with Masafumi Oizumi, Hiroto Kawasaki, Giulio Tononi) No report paradigm in EEG/MEG/intracranial recordings and the analysis of integrated information across sensory and prefrontal cortex Position B: DP180104128 (in collaboration with Marta Garrido, Ueli Rutishauser, Ralph Adolphs) The analysis of MEG/intracranial recordings, using Granger causality and integrated information We are looking for highly talented postdocs (application from PhD students with a prospect of getting PhD in 1 year are welcomed) for the above two ARC-funded positions. Ideal candidates in both positions would have strong publications, strong programming skills in Matlab/Python, strong background in data analysis, preferably neuroimaging data or neural spikes. The projects include 1) design of various psychophysical paradigms (*pdf <https://academic.oup.com/nc/article/doi/10.1093/nc/niw023/2970153/Are-we-un…>)*, including no-report paradigms (*pdf <https://drive.google.com/file/d/0B9N0ysU85rs7bEF5d1VVX3J4T3c/view>*), which can be used for subsequent EEG/MEG/intracranial recording experiments and 2) analysis of EEG/MEG/intracranial data using the approximations of integrated information (link1 <http://www.eneuro.org/content/4/5/ENEURO.0085-17.2017>, *link <https://drive.google.com/open?id=0B9N0ysU85rs7NFVSQlIza215eVU> *, *link <http://www.pnas.org/content/early/2016/12/02/1603583113.abstract>*, *link <http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.10046…>)* ). Please send the inquiry with your CV to naotsugu.tsuchiya(a)monash.edu. Applicants with most fit background and skills will be contacted for subsequent interview over the internet. The positions will be available until they are filled. Regards Nao Tsuchiya -- -- *Nao (Naotsugu) Tsuchiya* Associate Professor *Monash Institute of Cognitive and Clinical Neurosciences* Attention and Memory Program School of Psychological Sciences Monash University 770 Blackburn Monash Brain Imaging facility, Clayton, VIC 3800 Australia T: +61 3 9905 4564 E: naotsugu.tsuchiya(a)monash.edu W: homepage: http://users.monash.edu.au/~naotsugt/Tsuchiya_Labs_Homepage http://med.monash.edu.au/psych/email-sig/miccn-email-sig.jpg <http://www.monash.edu/neuro-institute/> 2. Visiting Researcher at Department of Dynamic Brain Imaging, Advanced Telecommunications Research (ATR), Japan 3. Visiting Researcher at CiNet, Osaka University, Japan orcid.org/0000-0003-4216-8701

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Professorship in Artificial Intelligence
by Steuber, Volker 08 May '19

08 May '19

Professor of Artificial Intelligence University of Hertfordshire, UK School of Computer Science, College Lane Campus, Hatfield Salary £58,293 to £64,397 pa dependent on skills and experience FTE: Full time position working 37 hours per week (1.0FTE) Duration of Contract: Permanent position Closing date: 3 September 2018 Applications are invited for an exceptionally well qualified Professor of Artificial Intelligence of international standing who shares our commitment to research-informed teaching, embraces the research impact agenda, and values collegiality. Applicants will strengthen existing activity within the School and develop it further, enhancing the research strengths and external reputation of the School of Computer Science and contributing significantly to its performance in future research assessments and collaborations with public and commercial partners. The successful applicant will connect to and complement the research conducted by the Adaptive Systems Research Group within the Centre for Computer Science and Informatics Research. The School hosts the University of Hertfordshire Robot House, a facility ubiquitously equipped with low-cost sensors providing a realistic environment for experiments with robotics in a smart home; a fully-fledged RoboCup lab for development of humanoid robots participating in international competitions, and a large variety of different robotic platforms, including iCub and Baxter. We encourage applications from researchers across a wide spectrum of AI, and particularly encourage applications from researchers working on emerging topics in AI. Possible fields include, but are not limited to: • Machine learning: reinforcement learning, Deep Methods, statistical methods, large scale data modelling/intelligent processing and high-performance learning algorithms • Robotics: embodied and/or cognitive robotics, HRI, robot safety, emotional/social robots, smart homes and sensors, sensor fusion, assistive robotics • Complex Systems: collective intelligence, adaptive, autonomous and multi-agent/robot systems, collective and swarm intelligence, social and market modelling, adaptive, evolutionary and unconventional computation • Mathematical Modelling: statistical modelling, information-theoretic methods, compressive sensing, intelligent data visualization, multiscale models, optimization • Emerging Topics in AI: computer algebra and AI, topological methods (e.g. persistent homology), modern topics in games and AI • AI and applications: financial modelling, AI and biology/physics/cognitive sciences • Foundations: fundamental questions of intelligence and computation, emergence of life/intelligence, Artificial Life As well as providing research supervision at PhD and MSc level, the School of Computer Science offers modules in Artificial Intelligence, machine learning, neural and evolutionary computing, as well as Artificial Life on the BSc and MSc programmes. Applicants will be committed to a strong research and research-informed teaching agenda, and will actively contribute to increase research impact of the School, in the enthusiastic, welcoming and collegiate environment of the school. Applicants must hold a PhD (or equivalent) in a relevant subject, possess excellent communication skills in English, an internationally excellent research record and reputation, including publications of the highest quality suitable for submission to the REF (UK Research Excellence Framework). Applicants are expected to show evidence of experience and success in research/commercial income generation, engagement with the research impact agenda, engagement with key stakeholders in the relevant Artificial Intelligence community and management and leadership ability. The professor will have opportunity to contribute actively to interdisciplinary research projects at the school and university level. Applications must be made online https://www.herts.ac.uk/contact-us/jobs-and-vacancies/academic-vacancies?, reference 016194?. Informal enquiries may be addressed to Dr Volker Steuber (Associate Dean Research, v.steuber(a)herts.ac.uk) or Professor William Clocksin (Dean of School, w.clocksin(a)herts.ac.uk) Please note that applications sent directly by email will not be accepted.

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[journals] CfP : Special Issue on Continual Unsupervised Sensorimotor Learning
by Nguyen, Sao Mai 14 Mar '19

14 Mar '19

Dear Colleagues, IEEE Transactions on Cognitive and Developmental Systems is currently running a Special Issue entitled " Continual Unsupervised Sensorimotor Learning". Dr. José Antonio Iglesias Martínez, Assoc. Prof. We would like to invite you to prepare a research article or a comprehensive review to be published in this special issue. AIM AND SCOPE Although machine learning algorithms continue to improve at a rapid pace enabling technologies and products such as autonomous driving cars and sophisticated image and speech recognition, it is often forgotten that these applications represent tailored solutions to specific tasks. Thus it is not clear if or how these autonomous systems can pave the road to general purpose machines envisioned by many. The pursuit for higher levels of autonomy and versatility in robotics is arguably lead by two main factors. Firstly, as we push robots out of the labs and productions lines, it becomes increasingly difficult to design for all possible scenarios that a particular robot might encounter. Secondly, the cost of designing, manufacturing, and maintaining such systems becomes prohibitive. As the algorithms for learning single tasks in restricted environments are improving, new challenges have gained relevance in order to get more autonomous artificial systems. These challenges include multi-task learning, multimodal sensorimotor learning and lifelong adaptation to injury, growth and ageing. Addressing these challenges promise higher levels of autonomy and versatility of future robots. This special issue on Continual Unsupervised Sensorimotor Learning is primarily concerned with the developmental processes involved in unsupervised sensorimotor learning in a life-long perspective, and in particular the emergence of representations of action and perception in humans and artificial agents in continual learning. These processes include action-perception cycle, active perception, continual sensory-motor learning, environmental-driven scaffolding, and intrinsic motivation. The special issue will highlight behavioural and neural data, and cognitive and developmental approaches to research in the areas of robotics, computer science, psychology, neuroscience, etc. Contributions might focus on mathematical and computational models to improve robot performance and/or attempt to unveil the underlying mechanisms that lead to continual adaptation to changing environment or embodiment and continual learning in open-ended environments. Contributions from multiple disciplines including cognitive systems, cognitive robotics, developmental and epigenetic robotics, autonomous and evolutionary robotics, social structures, multi-agent and artificial life systems, computational neuroscience, and developmental psychology, on theoretical, computational, application-oriented, and experimental studies as well as reviews in these areas are welcome. THEMES This special issue aims to report state-of-the-art approaches and recent advances on Continual Unsupervised Sensorimotor Learning with a cross-disciplinary perspective. Topics relevant to this special issue include but are not limited to: Emergence of representations via continual interaction Continual sensory-motor learning Action-perception cycle Active perception Environmental-driven scaffolding Intrinsic motivation Neural substrates, neural circuits and neural plasticity Human and animal behaviour experiments and models Reinforcement learning and deep reinforcement learning for life-long learning Multisensory robot learning Multimodal sensorimotor learning Affordance learning Prediction learning SUBMISSION Manuscripts should be prepared according to the “Information for Authors” of the journal found at http://cis.ieee.org/component/content/article/7/131-ieee-transactions-on-au…. Submissions must be done through the IEEE TCDS Manuscript center: https://mc.manuscriptcentral.com/tcds-ieee. Please select the category “SI: Continual Unsupervised Sensorimotor Learning”. IMPORTANT DATES 6th January 2019 – Paper submission deadline 15th March 2019 – Notification for authors 31st May 2019 – Deadline revised papers submission 30th June 2019 – Final notification for authors 31st July 2019 – Deadline for camera-ready versions September 2019 – Expected publication date More information on Continual Unsupervised Sensorimotor Learning http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue… GUEST EDITORS Nicolás Navarro-Gerrero Aarhus University, Aarhus, Denmark nng(a)eng.au.dk Sao Mai Nguyen IMT Atlantique, Francenguyensmai(a)gmail.com Erhan Öztop Özyeğin University, Turkeyerhan.oztop(a)ozyegin.edu.tr Junpei Zhong National Institute of Advanced Industrial Science and Technology (AIST), Japanjoni.zhong(a)aist.go.jp ---- Nguyen Sao Mai nguyensmai(a)gmail.com Researcher in Cognitive Developmental Robotics http://nguyensmai.free.fr

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Postdoctoral positions in computational neuroscience and artificial intelligence
by Maxim Bazhenov 01 Mar '19

01 Mar '19

Applications are invited for post-doctoral research positions in the laboratory of Dr. Maxim Bazhenov at the University of California, San Diego to study memory and learning and to develop neuroscience inspired computer algorithms capable of continual learning and adapting to the novel situations and contexts.This project involves close collaboration with the experimental laboratory of Dr. Bruce McNaughton (UC Irvine). The ultimate goal of the work is to advance the knowledge of how human and animal brains learn from experience and apply these principles to the artificial systems to enable continuous learning without catastrophic forgetting. The successful candidate will collaborate with a team of researchers to design neural network models of dynamic interactions between the hippocampus and neocortex during learning and memory consolidation in sleep and awake based on experimental data. These models will be used to derive learning principles that can be combined with advances in artificial intelligence and machine learning. An ideal candidate should have experience in computational/theoretical neuroscience and a basic knowledge of machine learning, or, alternatively, experience in machine learning algorithms and some basic knowledge of neuroscience. Experience with hierarchical learning, reinforcement learning, and/or goal-directed decision-making would be particularly helpful. The University of California offers excellent benefits. Salary is based on research experience. Applicants should send a brief statement of research interests, a CV and the names of three references to Maxim Bazhenov at mbazhenov(a)ucsd.edu -- Maxim Bazhenov, Ph.D. Professor, Department of Medicine, Neurosciences Graduate Program, UCSD, School of Medicine http://www.bazhlab.ucsd.edu/

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Columbia Workshop on Brain Circuits, Memory and Computation 2018
by Aurel A. Lazar 16 Dec '18

16 Dec '18

Columbia Workshop on Brain Circuits, Memory and Computation 2018 <http://www.bionet.ee.columbia.edu/workshops/bcmc/2018> BCMC 2018 March 15-16, 2018 Center for Neural Engineering and Computation <http://cnec.columbia.edu/> Columbia University, New York, NY, USA Overview The goal of the workshop is to bring together researchers interested in developing executable models of neural computation/processing of the brain of model organisms. Of interest are models of computation that consist of elementary units of processing using brain circuits and memory elements. Elementary units of computation/processing include population encoding/decoding circuits with biophysically-grounded neuron models, non-linear dendritic processors for motion detection/direction selectivity, spike processing and pattern recognition neural circuits, movement control and decision-making circuits, etc. Memory units include models of spatio-temporal memory circuits, circuit models for memory access and storage, etc. A major aim of the workshop is to explore the integration of various sensory and control circuits in higher brain centers. A Fruit Fly Brain Hackathon <http://www.bionet.ee.columbia.edu/hackathons/ffbh/2018> is being conducted in conjunction with the workshop. Workshop participants are welcome to attend the hackathon. Registration Registration is free but all participants have to register <https://bcmc2018.eventbrite.com/>. Thank you! Organizer and Program Chair Aurel A. Lazar <http://www.ee.columbia.edu/~aurel>, Department of Electrical Engineering, Columbia University. Program Overview (Confirmed Speakers) Misha B. Ahrens <https://www.janelia.org/lab/ahrens-lab>, Janelia Research Campus, Ashburn, VA. Davi Bock <https://www.janelia.org/lab/bock-lab>, Janelia Research Campus, Ashburn, VA. Claude Desplan <https://wp.nyu.edu/desplanlab/>, Department of Biology, New York University. Mark A. Frye <http://www.fryelab.net/>, Department of Integrative Biology and Physiology, University of California, Los Angeles. C. Giovanni Galizia <http://neuro.uni-konstanz.de/03team/content/galizia.html>, Department of Biology, University of Konstanz. Maria N. Geffen <https://geffenlab.weebly.com/maria.html>, Department of Otorhinolaryngology and Department of Neuroscience, University of Pennsylvania. Hokto Kazama <http://kazama.brain.riken.jp/index.php>, Riken Brain Science Institute, Tokyo. Donggen Luo <http://mgv.pku.edu.cn/?co=posts&ac=faculty&catalog=enpiintro&pname=en_Dongg…>, McGovern Institute for Brain Research, Peking University, Beijing. Thomas Nowotny <http://www.sussex.ac.uk/profiles/206151>, Department of Informatics, University of Sussex. Pavan Ramdya <https://ramdya-lab.epfl.ch/home>, Brain Mind Institute, EPFL, Lausanne. Aravinthan D.T. Samuel <https://scholar.harvard.edu/aravisamuel/home>, Department of Physics and center for Brain Science, Harvard University. Simon G. Sprecher <http://www.sprecherlab.com/simon-sprecher/>, Department of Biology, University of Fribourg. Seth Tomchik <http://www.scripps.edu/tomchik/>, The Scripps Research Institute Florida. Joshua T. Vogelstein <http://jovo.me/>, Dept. of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University. Scott Waddell <https://www.dpag.ox.ac.uk/research/waddell-group>, Department of Physiology, Anatomy and Genetics, University of Oxford. Anthony M. Zador <http://zadorlab.cshl.edu/>, Cold Spring Harbor Laboratory. Aurel http://www.bionet.ee.columbia.edu

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Postdoc position in Geometric Methods for Machine Learning and Deep Learning at RIST
by Luigi Malagò 14 Dec '18

14 Dec '18

=========================================================== *Subject*: Postdoc position in Machine Learning (1 year, renewable up to 2 years) *Institution*: RIST - Romanian Institute of Science and Technology, Cluj-Napoca *Keywords*: Geometry of Latent Spaces, Optimization over Manifolds, Information Geometry, Natural Gradient, Riemannian Geometry *Application deadline*: 10 October 2018 (applicants are encouraged to apply earlier) *Salary*: around 2200 euro net *Official announcement*: http://rist.ro/en/details/news/postdoc-positions-in-deep-learning-and-machi… =========================================================== Dear colleagues, the Romanian Institute of Science and Technology (RIST) has an opening for a postdoc position, in the context of the DeepRiemann project “Riemannian Optimization Methods for Deep Learning”, funded by European structural funds through the Competitiveness Operational Program (POC 2014-2020). The appointments will be for 1 year, with possible extensions up to 2 years. The DeepRiemann project aims at the design and analysis of novel training algorithms for Neural Networks in Deep Learning, by applying notions of Riemannian optimization and differential geometry. The task of the training a Neural Network is studied by employing tools from Optimization over Manifolds and Information Geometry, by casting the learning process to an optimization problem defined over a statistical manifold, i.e., a set of probability distributions. The project is highly interdisciplinary, with competences spanning from Machine Learning to Optimization, Deep Learning, Statistics, and Differential Geometry. The objectives of the project are multiple and include both theoretical and applied research, together with industrial activities oriented to transfer knowledge, from the institute to a startup or spin-off of the research group. The positions will be part of the new Machine Learning and Optimization group https://rist.ro/en/teams.html, which performs research at the intersection of Machine Learning, Stochastic Optimization, Deep Learning, and Optimization over Manifolds, using geometric methods based on Information Geometry. The group is one of two newly-formed groups in Machine Learning at RIST. The official job announcement can be seen here: http://rist.ro/en/details/news/postdoc-positions-in-deep-learning-and-machi… Informal inquiries can be sent to Dr. Luigi Malagò <malago(a)rist.ro>, principal investigator of the DeepRiemann project. Application deadline: 10 October 2018 (applicants are encouraged to apply earlier) best regards, Luigi Malagò

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CFP: FLAIRS 2017, Marco Island, FL,
by bisant＠umbc.edu 18 Oct '18

18 Oct '18

CALL FOR PAPERS FLAIRS 2017, Florida Artificial Intelligence Research Symposium Special Tracks on Data mining and Special Track on Cyber Security Marco Island, Florida http://www.flairs-30.info/ Paper submission deadline: November 21, 2016. Notifications: January 23, 2017. Camera ready version due: February 27, 2017. FLAIRS is an interdisciplinary artificial intelligence conference which especially encourages novel ideas and which features a double-blind review process. Data Mining Track This special track will be devoted to data mining with the aim of presenting new and important contributions in this area. Papers and contributions are encouraged for any work related to Data Mining. Topics of interest may include (but are in no way limited to): 1. Applications such as Intelligence analysis, medical and health applications, text, video, and multi-media mining, E-commerce and web data, financial data analysis, cyber security, remote sensing, earth sciences, bioinformatics, and astronomy. 2. Modeling algorithms such as hidden Markov models, decision trees, neural networks, statistical methods, or probabilistic methods; case studies in areas of application, or over different algorithms and approaches. 3. Feature extraction and selection. 4. Post-processing techniques such as visualization, summarization, or trending. 5. Preprocessing and data reduction. 6. Knowledge engineering or warehousing. Papers dealing with Cloud-based unstructured data or Cloud-based tool suites, such as Mahout or Apache Spark, are also encouraged. Cyber-Security Track Original high quality papers are sought for leading edge research and applications relating to the application of AI and Cyber Security. Topics of interest may include but are not limited to: · Machine Learning for Intrusion Detection Systems · NLP for text analysis in Cyber Security · Video and CCTV semantic analysis for terrorist or criminal action detection · AI & Biometrics, Internet of Things, and Cloud Computing security · AI & SCADA systems, Network Systems, and Embedded Systems security · Formal and theoretical approaches of intelligent security in WSNs · Legal and policy issues in intelligent security · Integrity, privacy, authentication and access control · Intelligent secure systems in multimedia databases · Social networks security · Intelligent incident response techniques in network and communication systems · Risk analysis and intelligent management · Virtual Private Networks intelligent security · Automated reasoning techniques of incidents and evidence analysis · Evidentiary aspects of digital forensics · Collaborative intelligent investigation · Intelligent systems in management, storage and reassembly · Steganography, watermarking, and forensic techniques in multimedia content processing · Lightweight security techniques · Intelligent Large-scale investigations and Big Data · Innovative non-repudiation techniques · Intelligent vulnerability analysis and assessment · Attacks tracking · Data hiding, tamper detection and recovery techniques · Security of e- healthcare systems and applications Note: We invite original papers (i.e. work not previously submitted, in submission, or to be submitted to another conference during the reviewing process). Submission Guidelines Interested authors should format their papers according to AAAI formatting guidelines. The papers should be original work (i.e., not submitted, in submission, or submitted to another conference while in review). Papers should not exceed 6 pages (or 4 pages for short paper, to be presented as a poster) and are due by November 21, 2016. For FLAIRS-30, the 2017 conference, the reviewing is a double blind process. Author names and affiliations should be Xd out on submitted papers to provide double-blind reviewing. Papers must be submitted as PDF through the EasyChair conference system, which can be accessed through the main conference web site (http://www.flairs-30.info/) Note: do not use a fake name for your EasyChair login - your EasyChair account information is hidden from reviewers. Authors should indicate which special track they are submitting to. The proceedings of FLAIRS will be published by the AAAI. Authors of accepted papers will be required to sign a form transferring copyright of their contribution to AAAI. FLAIRS requires that there be at least one full author registration per paper.

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Frontiers Research Topic on Data Assimilation
by Axel Hutt 02 Oct '18

02 Oct '18

Announcement of the Frontiers Research Topic -------------------------------------------------------------------------------------- Data Assimilation of Nonlocal Observations in Complex Systems -------------------------------------------------------------------------------------- Natural complex systems exhibit spatio-temporal dynamics on multiple scales that are difficult to predict and understand. To gain deeper insights into the system's dynamics and to be able to predict its evolution, observations of that system are analyzed which allows to derive or motivate models that fit that system. In general, one may distinguish two types of data. The so-called in-situ or local observations capture direct measurements of the system itself, such as temperature at a specific height in the atmosphere or electric potentials in biological cells. Other observations are not measured at a certain location but represent the integral of some relevant quantities manifesting the system's activity. Examples in meteorology for such nonlocal observations are satellite radiances, slant delays and radio occultation based on GPS data, or radar reflectivities. In biological systems, the non-invasive measurement techniques provide nonlocal observations, such as electro- and magnetoencephalogram or Magnetic Resonance Imaging. In addition to observations, realistic models are essential to improve the understanding of natural complex systems and to predict their dynamical evolution. To merge both models and observations, it is essential to develop techniques that optimally estimate the system activity well-adapted to a model and observed data. Data assimilation comprises a number of methods to merge diverse experimental data with an underlying model. Data assimilation optimally combines observations and a model to achieve a certain goal, such as optimal fitting of model parameters or providing optimal forecasts of the system's dynamics. Since the recent years have shown an increasing number of observation techniques capturing integrals of system activity, data assimilation of nonlocal observations becomes more and more important. The present Research Topic aims to bring together recent theoretical work in data assimilation of nonlocal observations with a strong link to specific applications. This article collection reflects the state-of-the-art in this research field. Examples of theoretical topics (as an unconstrained open list) are Kalman filters, variational assimilation techniques, regression techniques and stochastic optimization techniques. Applications may range from the parameter estimation in genetic regulatory networks over prediction of brain dynamics to weather forecast. We invite you to submit an abstract until November 30, 2018 and the manuscript until June 30, 2019. Contributions will be published as soon as they are accepted and synchronously gathered in the Research Topic volume. For more information, do not hesitate to contact us (email of Axel Hutt: digitalesbad(a)gmail.com) The organisers Lili Lei (Nanjing University) Marc Bocquet (Ecole des Ponts ParisTech) Alberto Carrassi (Nansen Environmental Remote Sensing Center) Axel Hutt (Deutscher Wetterdienst) Roland Potthast (Deutscher Wetterdienst) -- Axel Hutt Directeur de Recherche Deutscher Wetterdienst - German Meteorological Service Research and Development, Department FE 12 (Data Assimilation) Frankfurter Strasse 135, 63067 Offenbach, Germany Tel.: +49 69 8062 2750 http://www.geocities.ws/digitalbath/

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Tenure-track faculty position in Computation Neuroscience at Western University, Canada
by Jorn Diedrichsen 29 Sep '18

29 Sep '18

Supported by BrainsCAN, a $66M grant from the Canadian government (www.uwo.ca/brainscan<http://www.uwo.ca/brainscan>), Western University is expanding it’s strength in Neuroscience with a new tenure-track faculty position (Assistant or Associate Professor) in Computational Neuroscience. Successful candidates will join the Brain and Mind Institute (www.uwo.ca/bmi<http://www.uwo.ca/bmi>), one of the leading centres in cognitive Neuroscience in Canada, with a full range of ultrahigh field, research-dedicated MRI scanners (3T, 7T, 9.4T) and state-of-the-art laboratories for cognitive, behavioural, and neurophysiological testing in humans, non-human primates and rodents. The academic appointment of the candidate will be, depending on the candidate’s background, in the Department for Applied Mathematics, Statistics, or Computer Science. The deadline for the position in Computational Neuroscience is November 1st: https://brainscan.uwo.ca/work_with_us/vacancies/index.html Jörn Diedrichsen Western Research Chair Brain Mind Institute Department of Computer Science Department of Statistics Email: jdiedric(a)uwo.ca<mailto:jdiedric@uwo.ca> Tel: 1-519-661-2111 x86994

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Postdoctoral research in theoretical neuroscience
by Brent Doiron 29 Sep '18

29 Sep '18

The Doiron group at the University of Pittsburgh is currently seeking up to three postdoctoral fellows to work on cutting edge problems in theoretical neuroscience. Research topics include 1) Large-scale collaboration on modeling diverse inhibitory circuits in mouse V1. The research will be in collaboration with the Columbia theory center (Ken Miller, Larry Abbott, Misha Tsodyks, and Liam Paninski) as well as experimental labs at Berkeley (Adesnik lab, Ji lab) , UCSF (Scanziani lab) and Caltech (Cai lab). 2) Modeling population activity in the visual system and its modulation through top-down cognitive processes. 3) Modeling the formation and stability mechanics of learning in recurrently coupled cortical networks. All projects will involve heavy collaboration with several prominent experimental groups both in Pittsburgh as well as abroad. The positions are 3 year terms (contingent upon satisfactory progress) with competitive salary and benefits packages offered by the University of Pittsburgh. The successful candidate will join the Mathematics department at the University of Pittsburgh and will have strong ties to the Center for the Neural Basis of Cognition in Pittsburgh (joint between Pitt and Carnegie Mellon University). Candidates should have a Ph.D. in computational neuroscience, mathematics, physics, or engineering and have an excellent track record of scholarship. The University of Pittsburgh is an affirmative action/equal opportunity employer and encourages applications from under-represented groups. Interested persons should send their CV, a cover letter stating research interests and career goals, and names and contact information of two references to: bdoiron(a)pitt.edu . The competition will remain open until the positions are filled. -- Professor of Mathematics Center for the Neural Basis of Cognition University of Pittsburgh 301 Thackeray Hall Pittsburgh, PA, 15260 www.math.pitt.edu/~bdoiron

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Faculty position in computational neuroscience at the University of Pittsburgh
by Brent Doiron 29 Sep '18

29 Sep '18

Dear Computational neuroscience community The Dietrich School at the University of Pittsburgh invites applications for two tenure-track faculty positions at the Assistant Professor level in *Mathematical and Computational Life Sciences *to begin in the fall term of 2019. Successful candidates will be placed in the departments most suitable for their backgrounds and research programs from among the Departments of Mathematics, Biology, Neuroscience, Physics, and Chemistry. We seek outstanding scientists who will enhance and complement our existing strengths and who will collaborate with members of our sizable neuroscience, biology, biophysics, and biochemistry communities. One position is in *Mathematical and Computational Neuroscience. *Candidates working on the following topics are especially encouraged to apply: - Circuits and information flow in neuroscience - Neuronal and population dynamics - Synaptic mechanisms and dynamics - Neuronal membrane properties and ion channel function The other position is in *Mathematics and Theory of Biological Processes*. Candidates working on the following topics are especially encouraged to apply: - Mathematical ecology - Evolutionary theory - Cellular sensing and signal processing - Epigenetic and gene network dynamics The University of Pittsburgh is an active center for interdisciplinary research in the life sciences offering exciting opportunities for collaborations across departments as well as with our highly ranked medical school. The University is currently ranked third in the nation in total NIH funding, and the city of Pittsburgh is often voted “most livable” in the nation. These positions represent part of a vision to grow our community of researchers at the interface of mathematics, physics, chemistry and the life sciences. Successful candidates will have a Ph.D. and should demonstrate substantial research accomplishment and dedication to teaching. They will be expected to establish an extramurally funded research program, train graduate students, and actively participate in undergraduate education. *Review of applications will begin on October 23, 2018, *and will continue until the positions are filled*. *Applicants can apply online at: https://facultysearch.as.pitt.edu/apply/index/MjQy. Candidates should submit (a) a cover letter, (b) a CV, (c) a statement of research accomplishments and future plans, (d) a brief description of teaching interests, (e) a brief description of how your research, teaching or service demonstrates a commitment to diversity and inclusion, and (f) at least three letters of reference (for each reference, you will have the opportunity to input an email address through Interfolio’s Online Application system and a notification will be sent to the designated address with instructions). The positions are pending budgetary approval. The University of Pittsburgh is an Affirmative Action/Equal Opportunity Employer and values equality of opportunity, human dignity and diversity. EEO/AA/M/F/Vets/Disabled. Person to Contact: Erika Carpio 139 University Place Room 302 Pittsburgh, PA 15206 412-624-8361 -- Professor of Mathematics Center for the Neural Basis of Cognition University of Pittsburgh 301 Thackeray Hall Pittsburgh, PA, 15260 www.math.pitt.edu/~bdoiron

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job opening in neural dynamics and control
by Obermayer, Klaus, Prof. Dr. 28 Sep '18

28 Sep '18

Dear moderator, can post our job announcement below on the neuroinformatics mailing list? Thank you very much! Best regards Klaus Obermayer ------------------------------------------------------------------------------------------------------------------------------------------------ Neural Information Processing Group @ Technische Universität Berlin Research Assistant (PhD Position) in Computational Neuroscience Brain network models: Dynamics and Control Salary grade E13 (TV-L Berliner Hochschulen) Duration: max. 4 years Starting date: January 1st, 2019 Working field: The successful candidate is supposed to work on the analysis and simulation of calibrated whole-brain networks using neuronal mass models and on the design and evaluation of novel control schemes for the stabilization of and the switching between their dynamical states. She/he will be part of the Collaborative Research Center "Control of self-organizing nonlinear systems: Theoretical methods and concepts of application" (https://www.itp.tu-berlin.de/sfb910/ sonderforschungsbereich_910/sonderforschungsbereich_910/) as well as the Bernstein Center for Computational Neuroscience Berlin (https://www.bccn-berlin.de/) The candidate is also expected to interact with clinical scientists who are interested in the non-invasive manipulation of the whole-brain dynamics using transcranial current stimulation. Requirements: Successfully completed university degree (Master, Diplom, or equivalent) in Computational Neuroscience, Computer Science, Mathematics, Physics, or related fields; in-depth knowledge in non-linear dynamics and stochastic processes; very good programming and English language skills. How to apply: Please send your application with the usual documents (CV, motivation letter, transcripts of records, certificates, and the names of two persons who can provide recommendation letters) to Prof. Dr. Obermayer, FG Neuronale Informationsverarbeitung, Sekr. MAR 5-6, Marchstr. 23, 10587 Berlin, Germany, or by e-mail to klaus.obermayer(a)tu-berlin.de. For information about the research group see https://www.ni.tu-berlin.de/ Please send copies only. Original documents will not be returned. Aplication deadline: 26. 10. 2018 Reference number: IV-570/18

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Two PhD positions in computational and cognitive neuroscience
by Alireza Soltani 28 Sep '18

28 Sep '18

Two PhD positions are available in the Computational and Cognitive Neuroscience Lab <http://ccnl.dartmouth.edu/> of Alireza Soltani at Dartmouth College. This NIH-funded project aims to uncover circuit-level mechanisms underlying adaptive learning. The project involves computational modeling at different levels (mean-field to spiking networks) as well as analyses of behavioral, electrophysiological, and calcium imaging data in rats during dynamic learning and decision-making tasks. Applicants should have a strong quantitative background (e.g. physics, math, statistics, computational neuroscience, computer science, etc), good programming skills, genuine interest in understanding the brain, and a collaborative attitude. A background in neuroscience is desirable, but not essential. Interested applicants should submit an inquiry to Alireza Soltani (soltani(a)dartmouth.edu <mailto:soltani@darmouth.edu>), and submit their applications to Psychological and Brain Sciences Program <https://pbs.dartmouth.edu/graduate/graduate-admissions>at Dartmouth College. Alireza Soltani, Ph.D. Assistant Professor Department of Psychological and Brain Sciences Dartmouth College, NH Alireza.Soltani(a)Dartmouth.edu <mailto:Alireza.Soltani@Dartmouth.edu> http://ccnl.dartmouth.edu <http://ccnl.dartmouth.edu/> Phone: 603-646-2998 Office: Moore 356

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New ebook: Neural Information Theory
by James V Stone 27 Sep '18

27 Sep '18

This book (published June 2018) is now available in Kindle and pdf formats: Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency by James V Stone. Reviews, synopsis, and table of contents can be found here: http://sebtelpress.com "To understand life, one has to understand not just the flow of energy, but also the flow of information." William Bialek, 2012. regards, Dr James V Stone Honorary Reader, Sheffield University, UK. Web: sebtelpress.com Twitter: @jgvfwstone

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EEG time-varying networks - paper & code
by Williams, Nitin J 27 Sep '18

27 Sep '18

APOLOGIES FOR CROSS-POSTING: Dear all, I am just writing to let you know of our recent paper on a Markov Model-based method to analyse time-varying networks in EEG task-related data: https://doi.org/10.3389/fncom.2018.00076 The method combines sparse-MVAR (Multi Variate Auto Regressive) modelling to estimate networks, with Markov modelling to characterise network changes. We propose this method as complementing existing methods to analyse time-varying networks in EEG/MEG task-related data. MATLAB code for the method can be downloaded from: https://github.com/nitinwilliams/eeg_meg_analysis/tree/master/sMVAR_MM_tool… Any comments/questions on the method/code are of course welcome. Regards, Nitin

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Workshop announcement: The HBP CodeJam #9 Workshop
by Andrew Davison 27 Sep '18

27 Sep '18

The HBP CodeJam #9 Workshop - Early-bird registration ends 30. Sept.! Grand Hotel Et Des Palmes, Palermo, Italy. Nov. 26, 2018 at 09:00 – Nov. 28, 2018 at 18:00 The goal of the CodeJam workshops is to catalyze open-source, collaborative software development in computational and systems neuroscience and neuroinformatics, by bringing together researchers, students and engineers to share ideas, present their work, and write code together. The general format of the workshops is to dedicate the mornings to invited and contributed talks, leaving the afternoons free for discussions, tutorials and code sprints. The theme of this 9th installment is "co-design": scientists, students and engineers from different disciplines collaborating to build research infrastructure for computation-based neuroscience, and to use this infrastructure to model brain circuits, behaviour and learning, develop novel bio-inspired computing systems, and help discover new drugs for neurological disorders. Topics of CodeJam#9: • Large-scale brain modelling: towards a mouse whole brain model • Cortical and subcortical microcircuit models: circuit and model workflows, simulation and analysing tools, computing resources and user interaction • Infrastructure and open data: Beyond traditional HPC, electrophysiology data formats and analysis • Visio-motor integration and hierarchical navigation planning: Integration with HBP Infrastructure (Neurorobotics platform) • Biological deep learning: Plasticity implementation on neuromorphic systems • Modelling for drug discovery and medicine Topics might change slightly, we are still finalizing the programme. Confirmed speakers (not yet complete): • Matteo Cantarelli • Oliver Schmid • Markus Diesmann • Stine Vennemo • Håkon Mørk • Armandi Romani • Genrich Ivaska • Shailesh Appukuttan • Bernd Schuller • Michael Denker • Alper Yegenoglu • Lorenzo Vannucci More information and registration details at https://neuralensemble.org/meetings/CodeJam9/ Registration fee: Early-bird registration ends on 30 September 2018 Programme Committee Luca L. Bologna (CNR), Andrew Davison (CNRS), Anna Kreshuk (U. Heidelberg), Eric Müller (U. Heidelberg), Jeff Muller (EPFL), Bernd Schuller (FZ Jülich) Organisers Luca L. Bologna (CNR), Alessia Bonafede (CNR), Martina Schmalholz (U. Heidelberg)

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Postdoctoral position in memory, sequence replay, and sleep
by Kamran Diba 26 Sep '18

26 Sep '18

<https://sites.lsa.umich.edu/diba-lab/>The Diba Lab <https://sites.lsa.umich.edu/diba-lab/> at the University of Michigan Medical School is currently recruiting a postdoctoral candidate for a project recently funded by two NIMH (R01 and R21) grants. The project involves a collaboration with Dr. Ted Abel’s lab at the University of Iowa to investigate the Molecular, Cellular and Circuit Effects of Sleep Deprivation on Hippocampal Function. The successful candidate will perform and analyze large-scale neuronal recordings from freely-behaving, sleeping and sleep-deprived animals, along with chemogenetic manipulations, immunohistochemistry and biochemical assays. Data analysis of oscillatory and sequential neuronal firing patterns, using MATLAB, python, and other tools, is critical for this research and the prospective candidate should demonstrate quantitative and programming skills. The salary will be based on the National Research Service Award (NRSA) payscale <https://grants.nih.gov/grants/guide/notice-files/NOT-OD-18-175.html>. Our lab is housed in the Department of Anesthesiology and the Center for Consciousness Science <https://consciousness.med.umich.edu/> at Michigan Medicine. We are affiliated with the Department of Biomedical Engineering <http://bme.umich.edu/explore-research/> and the Neuroscience Graduate Program <http://neuroscience.med.umich.edu/program-0>. Collectively these programs and the University of Michigan at large provide a highly exciting and stimulating scientific environment for computational and systems neuroscience. The city of Ann Arbor is also consistently ranked <https://annarborusa.org/insights/communities/facts-rankings/> among the top places in the U.S. to live (with ready access to cultural events, sports, parks and natural beauty. Applicants should contact Kamran Diba: kdiba(a)umich.edu with a detailed C.V. or resume and a description of research background, skills, and interests. ------------- A top-ranked public university, the University of Michigan has a tradition of excellence in research, learning and teaching, sports and the arts, and more. The neuroscience community at the University of Michigan encompasses over 154 participating faculty from among 29 departments from the School of Medicine (including the Department of Anesthesiology), College of Literature Science and Arts, College of Engineering, School of Dentistry, School of Kinesiology and School of Public Health. As the longest standing neuroscience Ph.D. program in the United States the Neuroscience Graduate Program at U-Michigan has built an exceptional graduate program that features collaborative interactions among faculty and integration across individual disciplines. The program is composed of 70 to 80 graduate students (currently, 52% women, 30% under-represented minorities) and has more than 130 successful and accomplished alumni that work in academic research, industrial research and development, academic medicine and biotechnology. The NGP sponsors many activities that foster development of an active and cohesive neuroscience community on campus (e.g. Weekly Neuroscience Research Seminars, Annual Retreat, Spring Symposium, and Community Outreach).

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CFP: The OMG-Empathy Prediction Challenge
by Pablo Barros 26 Sep '18

26 Sep '18

CALL FOR PARTICIPATION The One-Minute Gradual-Empathy Prediction (OMG-Empathy) Competition held in partnership with the IEEE International Conference on Automatic Face and Gesture Recognition 2019 in Lille, France. https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/omg_empathy.html I. Aim and Scope The ability to perceive, understand and respond to social interactions in a human-like manner is one of the most desired capabilities in artificial agents, particularly social robots. These skills are highly complex and require a focus on several different aspects of research, including affective understanding. An agent which is able to recognize, understand and, most importantly, adapt to different human affective behaviors can increase its own social capabilities by being able to interact and communicate in a natural way. Emotional expression perception and categorization are extremely popular in the affective computing community. However, the inclusion of emotions in the decision-making process of an agent is not considered in most of the research in this field. To treat emotion expressions as the final goal, although necessary, reduces the usability of such solutions in more complex scenarios. To create a general affective model to be used as a modulator for learning different cognitive tasks, such as modeling intrinsic motivation, creativity, dialog processing, grounded learning, and human-level communication, only emotion perception cannot be the pivotal focus. The integration of perception with intrinsic concepts of emotional understanding, such as a dynamic and evolving mood and affective memory, is required to model the necessary complexity of an interaction and realize adaptability in an agent's social behavior. Such models are most necessary for the development of real-world social systems, which would communicate and interact with humans in a natural way on a day-to-day basis. This could become the next goal for research on Human-Robot Interaction (HRI) and could be an essential part of the next generation of social robots. For this challenge, we designed, collected and annotated a novel corpus based on human-human interaction. This novel corpus builds on top of the experience we gathered while organizing the OMG-Emotion Recognition Challenge, making use of state-of-the-art frameworks for data collection and annotation. The One-Minute Gradual Empathy datasets (OMG-Empathy) contain multi-modal recordings of different individuals discussing predefined topics. One of them, the actor, shares a story about themselves while the other, the listener, reacts to it emotionally. We annotated each interaction based on the listener's own assessment of how they felt while the interaction was taking place. We encourage the participants to propose state-of-the-art solutions not only based on deep, recurrent and self-organizing neural networks but also traditional methods for feature representation and data processing. We also enforce that the use of contextual information, as well as personalized solutions for empathy assessment, will be extremely important for the development of competitive solutions. II. Competition Tracks We let available for the challenge a pre-defined set of training, validation and testing samples. We separate our samples based on each story: 4 stories for training, 1 for validation and 3 for testing. Each story sample is composed of 10 videos with interactions, one for each listener. Although using the same training, validation and testing data split, we propose two tracks which will measure different aspects of the self-assessed empathy: The Personalized Empathy track, where each team must predict the empathy of a specific person. We will evaluate the ability of proposed models to learn the empathic behavior of each of the subjects over a newly perceived story. We encourage the teams to develop models which take into consideration the individual behavior of each subject in the training data. The Generalized Empathy track, where the teams must predict the general behavior of all the participants over each story. We will measure the performance of the proposed models to learn a general empathic measure for each of the stories individually. We encourage the proposed models to take into consideration the aggregated behavior of all the participants for each story and to generalize this behavior in a newly perceived story. The training and validation samples will be given to the participants at the beginning of the challenge together with all the associated labels. The test set will be given to the participants without the associated labels. The team`s predictions on the test set will be used to calculate the final metrics of the challenge. III. How to Participate To participate to the challenge, please send us an email to barros @ informatik.uni-hamburg.de with the title "OMG-Empathy Team Registration". This e-mail must contain the following information: Team Name Team Members Affiliation Participating tracks We split the corpus into three subsets: training, validation and testing. The participants will receive the training and validation sets, together with the associated annotations once they subscribe to the challenge. The subscription will be done via e-mail. Each participant team must consist of 1 to 5 participants and must agree to use the data only for scientific purposes. Each team can choose to take part in one or both the tracks. After the training period is over, the testing set will be released without the associated annotations. Each team must submit, via e-mail, their final predictions as a .csv file for each video on the test set. Together with the final submission, each team must send a short 2-4 pages paper describing their solution published on Arxiv and the link for a github page to their solution. If a team fails to submit any of these items, their submission will be invalidated. Each team can submit 3 complete submissions for each track. IV. Important Dates 25th of September 2018 - Opening of the Challenge - Team registrations begin 1st of October 2018 - Training/validation data and annotation available 1st of December 2018 - Test data release 3rd of December 2018 - Final submission (Results and code) 5th of December 2018 - Final submission (Paper) 7th of December 2018 - Announcement of the winners V. Organization Pablo Barros, University of Hamburg, Germany Nikhil Churamani, University of Cambridge, United Kingdom Angelica Lim, Simon Fraser University, Canada Stefan Wermter, Hamburg University, Germany -- Dr.rer.nat. Pablo Barros Postdoctoral Research Associate - Crossmodal Learning Project (CML) Knowledge Technology Department of Informatics University of Hamburg Vogt-Koelln-Str. 30 22527 Hamburg, Germany Phone: +49 40 42883 2535 Fax: +49 40 42883 2515 barros at informatik.uni-hamburg.de https://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.html https://www.inf.uni-hamburg.de/en/inst/ab/wtm/

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Postdoc in computational neuroscience/mathematical biology
by Rubchinsky, Leonid L 26 Sep '18

26 Sep '18

NSF-supported postdoctoral position in mathematical biology/computational neuroscience in my group in the Department of Mathematical Sciences at Indiana University-Purdue University Indianapolis (IUPUI). This position involves research in the mechanisms and properties of synchronization in neural and other living systems including collaboration with experimentalists. The position offers excellent interdisciplinary training possibilities in computational neuroscience and mathematical biology. Applicants should have (or expected to get in a near future) Ph.D. in mathematics, physics, neuroscience, biomedical engineering, computer science, or another related field. Applicants should have strong quantitative skills in dynamical modeling and data analysis and a strong interest in life sciences applications. Experience in nonlinear dynamics, computational neuroscience, and mathematical biology is a plus. Please apply via mathjobs.org at https://www.mathjobs.org/jobs/jobs/12619. Screening of applications will continue until position is filled. Please address informal inquires to Leonid Rubchinsky via e-mail: lrubchin(a)iupui.edu Leonid *********************** Leonid Rubchinsky, PhD Associate Professor Department of Mathematical Sciences, Indiana University - Purdue University, Indianapolis Stark Neurosciences Research Institute, Indiana University School of Medicine 402 N. Blackford St Indianapolis, IN 46202-3216 lrubchin(a)iupui.edu http://www.math.iupui.edu/~leo 317-274-9745 317-274-3460 (fax) ***********************

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Software Developer / Research Associate
by Stephanie Jones 24 Sep '18

24 Sep '18

Applications are invited for a research software developer / research associate position in the lab of Dr. Stephanie Jones at Brown University in Providence, RI. The level of the hire depends on experience. Our group combines human electrophysiological recordings and computational neural modeling techniques to study the mechanisms and meaning of brain dynamics in health and disease. We collaborate extensively with animal electrophysiologists and clinicians to develop data constrained models that are translationally relevant. The software developer / research associate will work with a team of researchers at Brown, Mass General Hospital, and Yale, to help develop a new software tool for circuit level investigation of human magneto- and electro-encephalography (MEG/EEG) signals: Human Neocortical Neurosolver, HNN (https://hnn.brown.edu) <https://hnn.brown.edu)>. The goal of this software is to provide the community with a user friendly tool to develop and test hypotheses on the circuit origin of their data. The hired candidate will help support and expand HNN’s capabilities, including software development and working with researchers to apply HNN to specific neuroscience questions. Duties will also include development of tutorials, online resources, and participation in workshops aimed at training the community in the use and applicability of HNN. Candidates should have a Bachelor’s, Master’s, or PhD in computer science, neuroscience, biomedical engineering, and/or related field. Strong Python programming skills are required, and experience with neural modeling, particularly NEURON, is preferred. Familiarity with MEG/EEG analysis methods and/or non-invasive brain stimulation is also beneficial. Candidates should be able to work in a dynamic, interdisciplinary, and fun work environment. Interested candidates should send a cv and a brief description of qualifications and research interests to the Jones lab manager Dylan_S_Daniels(a)brown.edu <mailto:Dylan_S_Daniels@brown.edu>. ******************************************* Stephanie R. Jones, PhD Associate Professor Brown University Department of Neuroscience Stephanie_Jones(a)Brown.edu https://blogs.brown.edu/joneslab/ <https://blogs.brown.edu/joneslab/> Human Neocortical Neurosolver https://hnn.brown.edu <https://hnn.brown.edu/>

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Postdoctoral researchers - computational modeling, EEG, TMS
by Stephanie Jones 24 Sep '18

24 Sep '18

Applications are invited for a postdoctoral position in the lab of Dr. Stephanie Jones at Brown University in Providence RI. Our group combines human electrophysiological recordings and computational neural modeling techniques to study the mechanisms and meaning of brain dynamics in health in disease. We collaborate extensively with animal electrophysiologists and clinicians to develop data constrained models that are translationally relevant. We are expanding our research efforts to including non-invasive brain stimulation. A postdoctoral position is available to integrate simultaneous human EEG and transcranial magnetic stimulation (TMS) in humans, with computational neural modeling developments. The postdoctoral researcher will help expand our new software tool Human Neocortical Neurosolver (https://hnn.brown.edu) <https://hnn.brown.edu)/> for circuit level interpretation of EEG/MEG signals to include TMS studies. She/he will also help with the design, implementation and analysis of our TMS/EEG experiments. Candidates should have a PhD in neuroscience, computational neuroscience, computer science, applied mathematics, biomedical engineering, and/or related field. Strong quantitative and programming skills are required. The ideal candidate would have experience in computational neuroscience methods applied to human EEG and brain stimulation data. However, experience in all areas is not required. Candidates should be able to work in a dynamic, interdisciplinary and fun work environment. Interested candidates should send a cv and a brief description of qualifications and research interests to the Jones lab manager Dylan_S_Daniels(a)brown.edu <mailto:Dylan_S_Daniels@brown.edu>. ******************************************* Stephanie R. Jones, PhD Associate Professor Brown University Department of Neuroscience Stephanie_Jones(a)Brown.edu https://blogs.brown.edu/joneslab/ <https://blogs.brown.edu/joneslab/> Human Neocortical Neurosolver https://hnn.brown.edu <https://hnn.brown.edu/>

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Workshop: High Performance Computing in Neuroscience
by Carnevale, Nicholas 24 Sep '18

24 Sep '18

Does your research involve computationally intensive tasks such as large scale modeling or data analysis? Come to the workshop on High Performance Computing Resources for Parallel Simulations and Data Analysis: NSG and HPAC to share your experience with others who are dealing with similar challenges, and learn about resources that are currently available and/or being developed that may help accelerate your own research. This workshop will be held on Saturday, Nov. 3 from 8:30 AM to 12:30 PM at a location in downtown San Diego. Space is limited, and the registration deadline is Friday, Oct. 12, so you should act early. For the on-line registration form see https://neuron.yale.edu/neuron/static/courses/nsg2018/nsg2018.html The workshop will include a combination of short presentations and discussions. Confirmed speakers and topics include: "The Neuroscience Gateway" Amit Majumdar, Subhashini Sivagnanam, Kenneth Yoshimoto, Ted Carnevale "Research Activities at the Human Brain Project's High Performance Analytics and Computing Platform" Alexander Peyser "Human Neocortical Neurosolver: A New Modeling Platform for Cellular and Circuit Level Interpretation of EEG/MEG" Samuel A Neymotin, Dylan S Daniels, Noam Peled, Robert A McDougal, Ted Carnevale, Christopher I Moore, Michael L Hines, Matti Hamalainen, Stephanie R Jones "Towards a Complete Description of the Hippocampal Circuitry Underlying Sharp Wave-Mediated Memory Replay" Ivan Soltesz "Neuroanatomical models beyond the spatial resolution of MRI require HPC: The Big Brain and fiber tracts" Karl Zilles, Nicola Palomero-Gallagher and Katrin Amunts "The Virtual Brain: personalized large-scale brain network modeling and its applications" Viktor Jirsa

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Neuro-Inspired Computational Elements (NICE) 2019 Abstract Submission Open!
by Aimone, James Bradley 24 Sep '18

24 Sep '18

Dear Colleagues: The call for abstracts for NICE 2019 is open for submissions at https://easychair.org/cfp/NICE2019 As with past NICE workshops, we are seeking submissions describing advances at the intersection of theoretical neuroscience, neural computing hardware and algorithms, and applications of this brain-inspired computing technology. Preference will be given to unpublished results that bridge our community. For agendas and videos of talks from past meetings, please see http://niceworkshop.org Submissions are 2 page extended abstracts, which will be reviewed to select full and short talks, as well as posters. New for NICE this year we are looking at potential options for proceedings papers, to be invited from the top submissions this year. Abstracts are due on November 15th. Registration will open in a few weeks. ---------------- NICE 2019 will be taking place during March 26-29, 2019 in Albany CNSE Campus in Albany, NY Important dates: Abstract call: September 15, 2018 Abstract submission open: September 22, 2018 Abstracts due: November 15, 2018 Selection announcement: January 15, 2019 ----------------- Hope to see you in Albany! Brad Aimone NICE Program Chair -------------------------- James Bradley Aimone, Ph.D. Principal Member of Technical Staff Data-driven and Neural Computing Department Center for Computing Research Sandia National Laboratories Phone: (505) 284-3147 Fax: (505) 844-4728 http://neuroscience.sandia.gov

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BrainScaleS, SpiNNaker and NES: EITN workshop 'Are we building the right user-level documentation?'
by Martina Reske 24 Sep '18

24 Sep '18

Dear users of BrainScaleS, SpiNNaker and NEST, Documentation is where we provide guidance for our users to successfully navigate, understand and use our software. But how do we ensure that the documentation we provide is achieving this goal? We want to hear from you! This EITN workshop 'Are we building the right user-level documentation?' is an opportunity for users to help steer documentation in the right direction and for us to better understand the needs of our communities. Join us November 21 to November 22 in Paris, France. Registration is now open! See program details and register at https://eitnconf-211118.sciencesconf.org/ -- Dr. Martina Reske Scientific Coordinator Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience & Institute for Advanced Simulation (IAS-6) Theoretical Neuroscience Jülich Research Centre and JARA Jülich, Germany Work +49.2461.611916 Work Cell +49.151.26156918 Fax +49.2461.619460 www.csn.fz-juelich.de<http://www.csn.fz-juelich.de> Office hours: TU & FR 9.00-17, WE & TH 9.00-13 ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, Prof. Dr. Sebastian M. Schmidt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

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