- Comp-neuro - lists.cnsorg.org

Bayes annd Information Theory ebooks

by James V Stone

These books are now available as ebooks: Information Theory: A Tutorial Introduction (plus report at https://arxiv.org/abs/1802.05968) Bayes’ Rule: A Tutorial Introduction to Bayesian Analysis Bayes’ Rule With MatLab Bayes’ Rule With Python Bayes’ Rule With R via Amazon and this web site: http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html All ebooks above are pdf files (my kindle books are essentially pdf files), and one (Bayes’ Rule: A Tutorial Introduction to Bayesian Analysis) is available in re-flowable epub format from here: https://sebtel.selz.com/ (Getting the epub format to display equations correctly is tricky, which is why only one of my books has been produced in epub format). Finally, the following book will be published on 8th June 2018 (Crick's birthday) Principles of Neural Information Theory James V Stone -- Dr James V Stone Honorary Reader, Sheffield University, UK. http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html

April 20, 2018

Postdoc position in Geometric Methods for Deep and Reinforcement Learning at RIST

by Luigi Malagò

=========================================================== *Subject*: Postdoc position in Machine Learning (1 year, renewable up to 2.5 years) *Institution*: RIST - Romanian Institute of Science and Technology, Cluj-Napoca *Keywords*: Deep Learning, Reinforcement Learning, Stochastic Optimization, Optimization over Manifolds, Information Geometry, Riemannian Geometry *Application deadline*: 06 May 2018 (applicants are encouraged to apply earlier) *Salary*: around 2190 euro net *Official announcement*: http://rist.ro/en/details/news/postdoc-positio ns-in-deep-learning-and-machine-learning.html =========================================================== 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 2.5 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 http://luigimalago.it/group.html, which performs research at the intersection of Machine Learning, Stochastic Optimization, Deep Learning, and Optimization over Manifolds, from the unifying perspective of Information Geometry. The group is one of two newly-formed groups in Machine Learning at RIST, where about 20 new postdoctoral research associates and research software developers will be hired by 2018. The official job announcement can be seen here: http://rist.ro/en/details/news/postdoc-positions-in-deep- learning-and-machine-learning.html Informal inquiries can be sent to Dr. Luigi Malagò <malago(a)rist.ro>, principal investigator of the DeepRiemann project. Application deadline: 06 May 2018 (applicants are encouraged to apply earlier) best regards, Luigi Malagò

April 20, 2018

Conference announcement: "Social cognition in humans and robots"

by Alexander Maye

Social cognition in humans and robots September 27-28, 2018 University Medical Center Hamburg-Eppendorf, Hamburg, Germany Over decades, a range of theories and concepts for explaining how we understand others have been developed. This conference turns the spotlight on the idea that social cognition is grounded in sensorimotor and informational dynamic coupling of agents. According to this view, interaction dynamics is one of the keys to understand the mechanisms of successful social interaction because it provides substantial clues to social understanding and gives rise to high-level capabilities such as joint shared intentionality and joint action. Moreover it suggests that many disturbances of social cognition may be rooted in the impaired capacity for entraining and sustaining sensorimotor and informational coupling. The conference will provide an interdisciplinary podium which overviews trends and recent developments of concepts, methods, basic research and applications of sensorimotor approaches in social cognitive science, neuroscience, and robotic research. A particularly interesting question will be how concepts and methods from social cognitive and neuroscience transfer to human-robot interaction. The meeting will also gauge the societal impact of progress in understanding dynamic coupling processes in human-human and human-robot interactions. The event is organized by the FET Proactive H2020 project "Socializing Sensorimotor Contingencies - soSMCs" (socSMCs.eu) in partner alliance with the European Society for Cognitive Systems - EUCog (eucognition.org) Keynote speakers - Natalie Sebanz (Central European University, Hungary) - Antonia Hamilton (University College London, UK) - Scott Kelso (Florida Atlantic University, USA) - Luciano Fadiga (University of Ferrara and IIT, Italy) - Iain Couzin (University of Konstanz, Germany) - Paul Verschure (Institute for Bioengineering of Catalonia, Spain) Submissions - Conference contributions (talks, posters) - Research articles (to be published in a Frontiers Research Topic) - Venues for the EUCognition meeting in 2019 For all submission types, please send an abstract of no more than 1.000 words to conference2018(a)socSMCs.eu and indicate the type of your contribution (poster|talk|article|venue), the title (500 characters max.), all author names and affiliations. Important dates - May 25, 2018: Deadline for submission of abstracts - June 29, 2018: Registration closes - December 3, 2018: Submission of research articles Fee The registration fee of 110€ includes coffee, soft drinks and snacks on both days. On September 27th, a social dinner at an extra charge of 20€ will be hosted at the MS Cap San Diego (http://www.capsandiego.de/in-brief.html).Booking of the social dinner is only possible in combination with a conference registration. Register today at https://en.xing-events.com/TEHHWXM -- _____________________________________________________________________ Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen Rechts; Gerichtsstand: Hamburg | www.uke.de Vorstandsmitglieder: Prof. Dr. Burkhard Göke (Vorsitzender), Prof. Dr. Dr. Uwe Koch-Gromus, Joachim Prölß, Martina Saurin (komm.) _____________________________________________________________________ SAVE PAPER - THINK BEFORE PRINTING

April 20, 2018

7th International Summer School of Neuroengineering "Massimo Grattarola": reminder!

by Paolo Massobrio

Dear colleagues, Just a simple reminder! Still 1 month left for the application deadline of the 7th International Summer School of Neuroengineering "Massimo Grattarola" (Neurotechnologies and Computational Methods to Interact with the Brain) that will be held in Genova (Italy) 18th-22nd June 2018. On our website (www.neuroengineering.eu <http://www.neuroengineering.eu> ) you can find the final program of the lectures. The keynote speech will be given by Prof. Miguel Nicolelis (Duke University). The school aims to introduce computational and technological methods to PhD students and post-docs of different backgrounds (life sciences, physics, engineering) to interact with the brain. Dates: 18th-22nd June 2018 Location: Genova (Italy) Registration: Participation is limited to 60 attendees (students, post-docs, .). Registration close 20th May. Website: www.neuroengineering.eu <http://www.neuroengineering.eu> Poster session: Students attending the school will have the opportunity to present their results in a two-days poster session. Practical session: Use of a high-density EEG set-up and a multi-electrode array for in vitro applications. Confirmed speakers: Miguel Nicolelis (Duke University), Shimon Marom (Technion Israel Institute of Technology), Matias Palva (Univ. Helsinki), Andreas Hierlemann (ETH), Tim Harris (Janelia Research Campus), Lucilla de Arcangelis (Univ. of Campania), Annalisa Bonfiglio (Univ. Cagliari), Sylvie Renaud (Bordeaux INP), Valentina Pasquale (Italian Institute of Technology), Michele Giugliano (Univ. Antwerp), Michele Migliore (CNR), Silvia Casarotto (Univ. Milano), Michela Chiappalone (Italian Institute of Technology), Stanisa Raspopovic (ETH), Ioannis Isaias (Uni. Wurzburg), Mario Rosanova (Uni. Milano) Best regards Paolo Massobrio _________________________________________________ Paolo Massobrio, PhD Assistant Professor of Bioengineering Neuroengineering and Bio-Nano Technology - NBT - Lab Department of Informatics, Bioengineering, Robotics and System Engineering - DIBRIS University of Genova Via Opera Pia 13, 16145, GENOVA, ITALY Phone: +39-010-3532761 Fax: +39-010-3532764 URL: <http://www.dibris.unige.it/> www.dibris.unige.it; <http://www.nbt.unige.it> www.nbt.unige.it E-mail: <mailto:paolo.massobrio@unige.it> paolo.massobrio(a)unige.it

April 20, 2018

Workshop on topology and neuroscience - First announcement

by Nicolas Ninin

Dear colleagues, We are pleased to announce the first workshop on topology and neuroscience, which will be held from Wednesday, November 28, to Friday, November 30, 2018 at the EPFL campus in Lausanne, Switzerland. This workshop aims at bringing together people working at the intersection of topology and neuroscience and welcomes anyone who has an interest in these subjects. There will be two lecture series by: Carina Curto, Pennsylvania State University Olaf Sporns, Indiana University There will be contributed talks given by: Paul Expert, Imperial College London Eyal Gal, The Hebrew University of Jerusalem Chad Giusti, University of Delaware Jürgen Jost, Max-Planck-Institut für Mathematik in den Naturwissenschaften in Leipzig Tom Leinster, University of Edinburgh Ran Levi, University of Aberdeen Sofia Olhede, University College London Jose Perea, Michigan State University Erik Rybakken, Norwegian University of Science and Technology (TBC) Bernadette Stolz, University of Oxford Katharine Turner, Australian National University Hubert Wagner, Institute of Science and Technology Austria Additionally, there will be a poster session during the workshop. Registration is now open at the conference website: <https://neurotop2018.org/> https://neurotop2018.org/ and will close September 1, 2018 or when we reach capacity. We have limited funding for students to cover local expenses. Applications for such funding is now open at the conference website and will close on July 1, 2018. All additional relevant information regarding the conference will continuously be updated on the conference website. In particular, travel information on how to get to EPFL as well as suggested accommodation options are already detailed there. You can contact the organizers at neurotop2018(a)neurotop2018.org. With best regards, Nicolas Nininon behalf of the organzing committee Stefania Ebli Daniela Egas Santander Nicolas Ninin Gard Spreemann

April 19, 2018

Call for Papers MICCAI International Workshop on Machine Learning for Clinical Neuroimaging (MLCN): Spatially Structured Data Analysis

by Marquand, A.F. (Andre)

Call for Papers MICCAI International Workshop on Machine Learning for Clinical Neuroimaging (MLCN): Spatially Structured Data Analysis (September 20, 2018, Granada, Spain) Recent advances in neuroimaging and statistical machine learning provide an exceptional opportunity for investigators and physicians to discover complex relationships between brain, behaviors, and mental and neurological disorders. MLCN 2018 workshop (https://mlcn2018.com/) as a satellite event of MICCAI 2018 (http://www.miccai2018.org/en/) aims to bring together researchers in both theory and application from various fields in domains of spatial statistics, pattern recognition in neuroimaging, and predictive clinical neuroscience. Topics of interests include but are not limited to: * Applications of spatio-temporal modeling in predictive clinical neuroscience * Spatial regularization in decoding clinical neuroimaging data * Spatial statistics in neuroimaging * Learning with structured inputs and outputs in clinical neuroscience * Multi-task learning in analyzing structured neuroimaging data * Deep learning in analyzing structured neuroimaging data * Model stability and interpretability in clinical neuroscience Submission Process: The workshop seeks high quality, original, and unpublished work on algorithms, theory, and applications of machine learning in clinical neuroimaging and spatially structured data analysis. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style (https://www.springer.com/gp/computer-science/lncs/conference-proceedings-gu…) of up to 8-pages papers using the CMT system at https://cmt3.research.microsoft.com/MLCN2018<https://mail.ru.nl/owa/redir.aspx?C=cF_JmZM8kIynrj31nweTMkQwN3oMG8T7g2yKH7j…>. This workshop uses a double-blind review process in the evaluation phase, thus authors must ensure anonymous submissions. Accepted papers will be published in a joint proceeding with the MICCAI conference. Important Dates: * Paper submission deadline: June 11, 2018 (23:59 PST) * Notification of Acceptance: July 16, 2018 * Camera-ready Submission: July 23, 2018 * Workshop Date: September 20, 2018

April 19, 2018

CALL FOR PAPERS

by Steve Mehrkanoon

*CALL FOR PAPERS* *Special issue* Neural Systems Engineering and Mathematical Modelling of Brain Dynamics using ECoG/EEG/MEG Oscillations and Machine Learning Methods *Journal* IEEE Transactions on Neural Systems and Rehabilitation Engineering *Publication fee: **Free* Paper Submission Deadline: *31/05/2018* Submission portal: *https://mc.manuscriptcentral.com/tnsre-embs* <https://mc.manuscriptcentral.com/tnsre-embs> *PhD students, postdocs, early and mid career research fellows **are **welcome to **contribute to this spe**cial iss**ue**. * *Contributions will receive prompt and thorough peer review and will be published online ahead of print after acceptance.* In humans, interactions between neuronal circuits, systems and signals among micro-, meso- and macro-scales of brain dynamics underpin the functional organisation of the brain that supports our daily life activity. How efficiently neuronal circuits such as cortico-cortical, cortico-thalamic and thalamo-cortical networks interact among these multiple spatial scales remains to be fully elucidated. Mathematical, computational and experimental neuroscientists apply a variety of methods, techniques and algorithms, both in animals and humans, ranging from single cell recordings to whole brain imaging, in order to identify the core mechanisms that govern the interactions among these scales. Although our knowledge of neural mechanisms, circuits and networks underlying brain dynamics and functions constantly grows, the integration of this knowledge to provide a conceptual framework of emergent behaviour and pattern formation occurring on different levels of spatial organisation remains challenging. Authors are invited to submit original or review articles addressing mechanisms of emergent brain dynamics inferred from the analysis of ECoG/EEG/MEG, on all levels of complexity. Specifically, articles integrating (i) computational neuroscience, and (ii) mathematical models of biologically realistic neuronal networks and machine learning methods are welcome. In this special issue, Guest Editor Dr Steve Mehrkanoon welcomes papers that address many of the challenges of mathematical and computational models of the brain networks and dynamics given the measurement data ECoG/EEG/MEG. The scope of this special issue is interdisciplinary. The list of possible topics includes, but is not limited to: - New developments in computational and mathematical models of mean field theory integrated with ECoG/EEG/MEG modalities - Multi-modal and combinatorial fusion of ECoG/EEG/MEG data and brain connectivity machine learning - Development of data-driven machine learning techniques to identify the mechanisms of emergent spatia-temporal dynamics within the framework of network topology - Development of computational and mathematical models for bridging the scales of brain dynamics - Cortical network modelling and formal conceptual models of cortical oscillations - Application of deep learning and machine learning algorithms in functional, structural and effective network analysis and predictive models of brain dynamics. - New cutting-edge numerical analysis and techniques to simulate/implement - Adaptive mathematical and computational models of network dynamics (e.g., brain development and aging studies) - Statistical methods for assessing individual and population brain connectivity data *Guest Editors* 1. *Dr Steve Mehrkanoon* The University of Queensland Centre for Clinical Research, Faculty of Medicine, School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia Contact: s.mehrkanoon(a)uq.edu.au 1. *Professor David Liley* Centre for Human Psychopharmacology, School of Health Sciences, Swinburne University of Technology, Melbourne, Australia Contact: dliley(a)swin.edu.au 1. *Dr Siamak Mehrkanoon* Department of Electrical Engineering (ESAT), University of Leuven, KU Leuven, Belgium Contact: Siamak.Mehrkanoon(a)esat.kuleuven.be *IMPORTANT DATES* Paper Submission Deadline: 31/05/2018 Notification Of Acceptance: 31/08/2018 Special issue information https://tnsre.embs.org/2018/04/13/call-for-papers-special-issue-on-neural-s… - - - - *Dr Steve Mehrkanoon* Senior Mathematical Neuroscientist, Advance Queensland Research Fellow, Perinatal Research Centre, Faculty of Medicine, Home: http://researchers.uq.edu.au/researcher/14135 Level 4, UQ Centre for Clinical Research, Building 71/918 <https://www.google.com.au/maps/place/UQCCR/@-27.448514,153.028703,15z/data=…> Royal Brisbane and Women’s Hospital, Qld 4029 T: +61 7 33466012 CIRCOS Code 0025B The Faculty of Medicine is embracing the Green Office philosophy. Please consider the environment before printing this email. This email (including any attached files) is intended solely for the addressee and may contain confidential information of The University of Queensland. If you are not the addressee, you are notified that any transmission, distribution, printing or photocopying of this email is prohibited. If you have received this email in error, please delete and notify me. Unless explicitly stated, the opinions expressed in this email do not represent the official position of The University of Queensland.

April 19, 2018

Stanford University Job Opportunities: Postdoctoral Fellowship

by Christina Atkinson

Stanford University’s Department of Psychiatry & Behavioral Sciences is seeking to hire a full time post-doctoral fellow for their Cognitive & Systems Neuroscience Laboratory. This lab is headed by Dr. Vinod Menon who has broad multidisciplinary expertise that spans several scientific disciplines and has published extensively on various aspects of human cognition and brain function. Explore our lab website for more information: http://med.stanford.edu/scsnl.html. POSTDOCTORAL FELLOWSHIP IN COMPUTATIONAL MODELING OF BRAIN NETWORKS Currently Available: The Stanford Cognitive and Systems Neuroscience Laboratory invites applications for a postdoctoralfellowship in computational modeling of human brain imaging data using deep neural networks. The successful candidate will be involved in a multidisciplinary project that seeks to develop novel computational frameworks for robust identification of neurobiological signatures of psychiatric and neurological disorders combining deep learning and convolution networks and dynamic brain circuit analyses with task and resting-state fMRI. Clinical disorders currently under investigation include autism, ADHD, anxiety and mood disorders, learning disabilities, schizophrenia, and Parkinson’s disease. We seek talented and highly-motivated individuals with a strong background in at least one of these areas: deep learning algorithms and optimization (theory and practical implementations); computational modeling, statistical inference, and machine learning, especially as applied to fMRI and MRI data. The candidate will have access to multiple large datasets and state-of-the-art computational resources, including HPCs and GPGPUs. Please email a CV, statement of research interests and relevant background, and have three letters of reference sent to Drs. Vinod Menon and Aarthi Padmanabhan at scsnl.stanford+postdoc(a)gmail.com<mailto:scsnl.stanford@gmail.com>

April 19, 2018

Scientist position in Modeling at the Allen Institute for Brain Science

by Anton Arkhipov

An exciting opportunity at the Allen Institute for Brain Science - a new Scientist position in Modeling is now open. Please apply here: https://alleninstitute.hrmdirect.com/employment/job-opening.php?req=746277&… We are seeking a Scientist to work on bio-realistic modeling of the mouse visual cortex, in close collaboration with in-house experimentalists studying the structure (cell types and connectivity) and in vivo function (patterns of neural activity and computations) of cortical circuits involved in behavior. Our team constructs models of neurons based on in vitro studies (see, e.g., https://www.nature.com/articles/s41467-017-02718-3 and http://celltypes.brain-map.org) and employs them as building blocks for large-scale data-driven models of cortical circuitry (see, e.g., https://www.biorxiv.org/content/early/2018/03/31/292839) The developed models are used to ask questions about activity and mechanisms observed in unprecedented high-throughput in vivo recordings (http://observatory.brain-map.org/visualcoding) We also build and disseminate modeling software to support this work (https://alleninstitute.github.io/bmtk/index.html) Job Responsibilities: - Join ongoing efforts on building and simulating models of cortical areas in the mouse visual cortex, using point-neuron and biophysically detailed (compartmental) approaches. - Use these models to explore mechanisms underlying neural activity and computations observed in vivo in the context of a behavioral task; the aim is to provide mechanistic predictions for subsequent experimental testing by in-house collaborators. - Collaborate with in-house theoreticians and experimentalists to test hypotheses about cortical computations and algorithms in realistic models and in experiments. - Contribute to the development of modeling and analysis software. - Publish/present findings in peer-reviewed journals/scientific conferences. Basic Qualifications: - PhD degree in Neuroscience, Physics, Mathematics, Applied Mathematics, Bioengineering, or related field. SCIENTIST I: 0-2 years postdoctoral experience in computational science SCIENTIST II: 3+ years postdoctoral experience in computational science Desired Qualifications: - Experience in computational neuroscience and/or systems neuroscience is preferred, but other strong applicants will be considered (with background in computational physics, biophysics, and related disciplines). - Candidates with strong background in experimental systems neuroscience, willing to switch to computational work, are encouraged to apply. - Experience with parallel computing is a plus, as well as experience with programming in Python and/or MATLAB. - Experience with models of neurons/neuronal circuits is a plus. - Ability to meet aggressive timelines and deliverables in a collaborative environment is required. - Strong publication record. - Excellent written and verbal communication skills. It is the policy of the Allen Institute to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, the Allen Institute will provide reasonable accommodations for qualified individuals with disabilities. Anton Arkhipov Associate Investigator T: 206.548.8414 antona(a)alleninstitute.org<mailto:antona@alleninstitute.org> Allen Institute for Brain Science www.alleninstitute.org<http://www.alleninstitute.org/>

April 19, 2018

2018 NEURON Summer course

by Ted Carnevale

The 2018 NEURON Summer Course will be held at Emory University in Atlanta, Georgia, from Monday, August 6, through Saturday, August 11. The course will present a thorough introduction to computational modeling of neurons and networks with NEURON, and is suitable for individuals at all levels of expertise. This year continues the 6 day curriculum we introduced last year in order to provide expanded coverage of parallel simulation, and to give participants more time for active learning exercises. The registration deadline is Monday, July 16, 2018. No applications will be accepted after that date, and there will be no on-site registration. Space is limited, and applications will be considered in the order received. For more information and the on-line registration form, see http://www.neuron.yale.edu/neuron/static/courses/summer2018/summer2018.html --Ted

April 18, 2018