- Comp-neuro - lists.cnsorg.org

A biologically motivated and more powerful alternative to locality-sensitive hashing
by Rod Rinkus Aug. 2, 2018

Aug. 2, 2018

I believe this community will be interested in a new downloadable Java app that I 've just made available on my web site (on this page <http://www.sparsey.com/CSA_explainer_app_page.html>), which describes an alternative, biologically motivated/plausible, way to achieve the goals of locality-sensitive hashing (LSH). This alternative model, called Sparsey, achieves a more graded notion of similarity preservation than LSH, and has many other advantages as well. Sparsey has a natural correspondence to the brain's cortex, centering on the idea that all items of information are stored as sparse distributed codes (SDCs), a.k.a., cell assemblies, in superposition in mesoscale cortical modules, e.g., macrocolumns (though other structures, e.g., mushroom bodies, are also candidates). Briefly, Sparsey preserves similarity from input space to SDR code space (measured as intersection size) as follows. - The process of choosing an SDC takes the form of Q independent softmax choices, one in each of the Q WTA competitive modules (CMs) that comprise the SDR coding field. - The familiarity (inverse novelty) of the input, denoted "G", which is in [0,1], is computed. This is an extremely simple computation. - The amount of noise in those Q softmax choice processes is modulated as a function of G. Basically, the softmax is over the distribution of input summations of the competing cells (in a given CM), but we use G to modulate (i.e. sharpen vs. flatten) those distributions. - When G is near 1 (perfect familiarity), the distributions are greatly sharpened, causing the expected number of CMs in which the cell with the highest input summation wins (and thus, the expected intersection of the resulting SDR with the closest matching previously stored SDR) to increase towards Q. When G is near 0 (completely novel), the distributions are flattened, causing the expected number of CMs in which the cell with max input summation wins (and thus, the expected intersection of the resulting SDR with the closest matching previously stored SDR) to decrease towards chance. In other words, this G-based modulation of the distributions, which can be viewed as varying the amount of noise in the choice process, achieves similarity preservation. I encourage members of this community to explore the app to understand this simple and more powerful alternative to LSH. I welcome your feedback. Sincerely, Rod Rinkus -- Gerard (Rod) Rinkus, PhD President, rod at neurithmicsystems dot com Neurithmic Systems LLC <http://sparsey.com> 275 Grove Street, Suite 2-400 Newton, MA 02466 617-997-6272 Visiting Scientist, Lisman Lab Volen Center for Complex Systems Brandeis University, Waltham, MA grinkus at brandeis dot edu http://people.brandeis.edu/~grinkus/ <http://people.brandeis.edu/%7Egrinkus/>

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Postdoc position in Geometric Methods for Deep and Reinforcement Learning at RIST
by Luigi Malagò Aug. 2, 2018

Aug. 2, 2018

=========================================================== *Subject*: Postdoc position in Machine Learning (1 year, renewable up to 2 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*: 15 August 2018 (applicants are encouraged to apply earlier) *Salary*: around 2200 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 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-lea rning-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: 15 August 2018 (applicants are encouraged to apply earlier) best regards, Luigi Malagò

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PhD positions at the Univ. of Leicester (for UK/EU candidates only)
by Okun, Michael (Dr.) Aug. 2, 2018

Aug. 2, 2018

Fully Funded PhD Studentships (for UK/EU candidates only) to work at Prof. Quian Quiroga's lab at the Centre for Systems Neuroscience, University of Leicester, UK ( www.le.ac.uk/csn ). The Centre for Systems Neuroscience at the University of Leicester, UK, offers up to 2, 3-Year Fully Funded (stipend plus fees) PhD positions for UK/EU candidates, to study single cell recordings in humans – performed in epileptic patients for curative surgery. The project will investigate concept cells (a.k.a. Jennifer Aniston neurons) – that means, neurons in the hippocampus and surrounding areas that selectively fire to specific concepts, like different pictures and the written or spoken name of a particular person (e.g. Quian Quiroga et al, Nature 2005; Quian Quiroga Nature Reviews Neuroscience 2012), which are involved in declarative memory functions. The successful candidate will contribute to performing recordings with patients in collaborating hospitals and the analysis of the data to investigate the role of these neurons in memory formation. Candidates should have very good quantitative skills (data processing, and programing e.g. in Matlab) and a strong background or interest in neuroscience. They should be able to work both independently and as part of a research team within the Centre for System Neuroscience at Leicester and with our external collaborators. Informal enquiries are welcome and should be made to Emma Hawkins, Research Centre Manager, eeh18(a)le.ac.uk or 0116 252 3249. Interested candidates should send their curriculum vitae and a brief description of their scientific interests to Emma Hawkins (eeh18(a)le.ac.uk) The closing date for the applications is Friday 5th September. Interviews are anticipated to take place on the 12th September.

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CfP : Workshop on Continual Unsupervised Sensorimotor Learning, IEEE ICDL 2018
by Nguyen, Sao Mai July 31, 2018

July 31, 2018

*2nd Call for Contributions* IEEE ICDL -2018 workshop on Continual Unsupervised Sensorimotor Learning 17th September 2018, Tokyo, Japan Website : http://conferences.au.dk/icdl-epirob-2018-workshop Participants are invited to submit short papers (max 4 pages) following the standard IEEE conference template. Selected contributions will be presented during the workshop as spotlight talks and in a poster session. Authors will be invited to submit extended versions of their papers for a special issue on Continual Unsupervised Sensorimotor Learning at IEEE TCDS. ================================================================== Scope As the algorithms for learning single tasks in restricted environments are improving, new challenges have gained relevance. They include multi-task learning, multimodal sensorimotor learning in open worlds and lifelong adaptation to injury, growth and ageing. In this workshop we will discuss the developmental processes involved in 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 discussion will be strongly motivated by behavioural and neural data. We hope to provide a discussion friendly environment to connect with research with similar interest regardless of their area of expertise which could include robotics, computer science, psychology, neuroscience, etc. We would also like to devise a roadmap or strategies to develop mathematical and computational models to improve robot performance and/or to attempt to unveil the underlying mechanisms that lead to continual adaptation to changing environment or embodiment and continual learning in open-ended environments. The primary list of topics covers the following (but 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 Invited Speakers - Jochen Triesch, Frankfurt Institute of Advanced Studies, Germany - Emre Ugur, Boğaziçi University, Turkey (to be confirmed) - Yukie Nagai, Center for Information and Neural Networks, Japan (to be confirmed) - David Ha, Google Brain (to be confirmed) Call for Papers: Prospective participants in the workshop are required to submit a contribution as a short paper (max 4 pages) Submissions must be in PDF following the standard IEEE conference style <https://www.ieee.org/conferences/publishing/templates.html>. Send your PDF manuscript indicating [ICDL-EPIROB 2018] in the subject to: erhan.oztop(a)ozyegin.edu.tr Selected contributions will be presented during the workshop as spotlight talks and in a poster session. Contributors to the workshop will be encouraged to submit extended versions of the manuscripts to a special issue at IEEE Transactions on Cognitive and Developmental Systems (TCDS). Submissions will be peer reviewed consistent with the journal practices. Important Dates: Paper submission deadline: 5th August 2018 Notification of acceptance: 20th August 2018 Camera-ready version: 9th September 2018 Half-day workshop: 17th September 2018 Organizers: - Nicolás Navarro-Guerrero, Aarhus University, Aarhus, Denmark - Sao Mai Nguyen, IMT Atlantique, France - Erhan Öztop, Özyeğin University, Turkey - Junpei Zhong, National Institute of Advanced Industrial Science and Technology (AIST), Japan Nguyen Sao Mai nguyensmai(a)gmail.com Researcher in Cognitive Developmental Robotics http://nguyensmai.free.fr

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[CFP] IROS-2018 Workshop: Towards Intelligent Social Robots: From Naive Robots to Robot Sapiens
by Amir Aly July 31, 2018

July 31, 2018

CALL FOR PAPERS **Apologies for cross posting ** We are pleased to call for papers for the IROS-2018 workshop: "*Towards Intelligent Social Robots: From Naive Robots to Robot Sapiens*" In conjunction with the *IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)** - Madrid - Spain - October * *5th, 2018* *Webpage: **http://intelligent-social-robots-ws.com/* <http://intelligent-social-robots-ws.com/> *I. Aim and Scope *Robots that cook creatively, clean up our rooms dutifully, entertain our guests wittily and keep us company loyally. Robots that assist human users in their daily chores and provide support in times of need. Researchers around the world have envisioned such robot companions for a long time. Thanks to numerous innovations in sensor technology and software development, robots are now increasingly able to plan complex tasks in unknown environments, learn from experience and adapt to changes in the environment. The greatest challenges in robotics now lie in the development of robot skills and high-level AI-based functionalities that enable robots to work effectively in close collaboration with humans. Aside from the numerous technical challenges, which must be overcome before this vision can become a reality, multi-disciplinary research efforts are also invested into the social engineering of robots. In order to engineer “smart” robots that we accept, trust and welcome into our homes, it is paramount that we identify and investigate the factors that affect social interactions between humans and robots. For this purpose, computer scientists, AI researchers, engineers and psychologists tackle important questions that will determine whether robots will be perceived as helpful and reliable companions or as irritating nuisances. How should robots look, behave and communicate with us? What are our expectations of robot behavior in a social context? How can robots learn using the capabilities of their environments to achieve tasks and to address the needs of their human co-inhabitants? What kind of robot intelligence is required for what kind of tasks? In this workshop, participants will discuss with seasoned experts and young researchers what defines social and artificial intelligence for “smart” robots and how modern technological advances can equip robots with such intelligence. As such, this workshop aims to shed light on the intersection between cognitive science, artificial intelligence, and robotics research both from the theoretical and technical perspectives. Recent advances and possible avenues for future research in the field of “smart” robotics are principal topics of discussion during the workshop. ** *This workshop is endorsed by the IEEE Technical Committees*: *Human-Robot Interaction and Coordination, Cognitive Robotics, and Robot Learning*. *II. Keynote Speakers * 1. * Angelo Cangelosi *– University of Manchester/Plymouth – UK 2. *Yiannis Aloimonos *– University of Maryland – USA 3. * Selma Sabanovic *– Indiana University – USA 4. *Michael Beetz* – University of Bremen – Germany 5. *Matthias Scheutz *– Tufts University – USA *III. Submission * 1. For paper submission, use the following EasyChair web link: *Paper Submission <https://easychair.org/account/signin.cgi?key=75348348.9VXAN4Pe7LmBAKCx;time…>* . 2. Use the IEEE style (two-column format – US letter): *IEEE Templates <https://www.ieee.org/conferences/publishing/templates.html>*. 3. Submitted papers should be limited to 2-6 pages maximum. The primary list of topics covers the following points (but not limited to): - Multimodal human robot interaction - Cognitive modeling of human behavior - Cognitive architectures and strategies for intelligent interaction with the environment - AI and machine learning approaches applied to human-robot-environment interaction - Cloud robotics and ubiquitous computing - Ambient assisted living - Human intention recognition and prediction - Robot acceptance - Social engineering - Language learning, embodiment, and social intelligence - Computational modeling for high-level human cognitive functions - Predictive learning from sensorimotor information - Multimodal interaction and concept formulation - Language and action development - Learning, reasoning, and adaptation in collaborative human-robot tasks - Affordance learning - Learning by demonstration and imitation - Language and grammar induction in robots * IV. Important Dates * 1. Paper submission: *20-August-2018 * 2. Notification of acceptance: *30-August-2018* 3. Camera-ready version:* 7-September-2018 * 4. Workshop: *5-October-2018* *V. Organizers * 1. *Amir Aly *– Ritsumeikan University – Japan 2. *Sascha Griffiths *– Hamburg University – Germany 3. * Verena Nitsch *– Bundeswehr University – Germany 4. *Katerina Pastra* – Cognitive Systems Research Institute – Greece 5. *Tadahiro Taniguchi* – Ritsumeikan University – Japan ---------------------- *Amir Aly, Ph.D.* Senior Researcher Emergent Systems Laboratory College of Information Science and Engineering Ritsumeikan University 1-1-1 Noji Higashi, Kusatsu, Shiga 525-8577 Japan

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3 Independent Academic Fellowship positions Computational Neuroscience
by Mark van Rossum July 31, 2018

July 31, 2018

Academic Fellowship positions The University of Nottingham is a research intensive university ranked in the top 10 in the UK and in the top 30 of Europe. It has a strong tradition in Cognition, Perception, Mathematical Biology, and structural and functional Imaging. To support further development across these areas, and stimulate new research opportunities, the University has recently made a major investment in Computational Neuroscience. As part of this investment we are inviting applications for three independent fellowships (3 years). The positions are ideal for researchers with a strong vision that are planning to develop their own research group in a supportive, interdisciplinary environment with outstanding research facilities. We are interested in candidates working across a broad spectrum of research topics areas in Computational Neuroscience, including functional neuroimaging, neural networks, and models of cognition. Research methodologies could include numerical simulation, machine learning, theoretical neuroscience, and innovative data analysis. The Fellows will be based in the School of Psychology and/or Mathematical Sciences. For the online application form see https://www.nottingham.ac.uk/jobs/currentvacancies/ref/SCI219418 Informal inquiries: Prof Mark van Rossum, mark.vanrossum(a)nottingham.ac.uk, or Prof Mark Humphries, lpzmdh(a)exmail.nottingham.ac.uk. -- Mark van Rossum, Professor, Schools of Psychology and Maths, U Nottingham Psych Bldg Rm LG.19, 44-115-74 86851, 44-7722049644 This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please contact the sender and delete the email and attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham. Email communications with the University of Nottingham may be monitored where permitted by law.

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CFP: Workshop on Crossmodal Learning for Intelligent Robotics in conjunction with IEEE/RSJ IROS 2018
by Pablo Barros July 30, 2018

July 30, 2018

**Apologies for cross-posting** 1st CALL FOR PAPERS for the international workshop: * Crossmodal Learning for Intelligent Robotics * in conjunction with IEEE/RSJ IROS 2018 * Madrid, Spain - Friday 5 October 2018 * * Website: http://www.informatik.uni-hamburg.de/wtm/WorkshopCLIR18/index.php * I. Aim and Scope The ability to efficiently process crossmodal information is a key feature of the human brain that provides a robust perceptual experience and behavioural responses. Consequently, the processing and integration of multisensory information streams such as vision, audio, haptics and proprioception play a crucial role in the development of autonomous agents and cognitive robots, yielding an efficient interaction with the environment also under conditions of sensory uncertainty. Multisensory representations have been shown to improve performance in the research areas of human-robot interaction and sensory-driven motor behaviour. The perception, integration, and segregation of multisensory cues improve the capability to physically interact with objects and persons with higher levels of autonomy. However, the multisensory input must be represented and integrated in an appropriate way so that they result in a reliable perceptual experience aimed to trigger adequate behavioural responses. The interplay of multisensory representations can be used to solve stimulus-driven conflicts for executive control. Embodied agents can develop complex sensorimotor behaviour through the interaction with a crossmodal environment, leading to the development and evaluation of scenarios that better reflect the challenges faced by operating robots in the real world. This half-day workshop focuses on presenting and discussing new findings, theories, systems, and trends in crossmodal learning applied to neurocognitive robotics. The workshop will feature a list of invited speakers with outstanding expertise in crossmodal learning. II. Target Audience This workshop is open to doctoral students and senior researchers working in computer and cognitive science, psychology, neuroscience and related areas with the focus on crossmodal learning. III. Confirmed Speakers 1. * Yulia Sandamirskaya * Institute of Neuroinformatics (INI), University and ETH Zurich 2. * Angelo Cangelosi * Plymouth University and University of Manchester, UK 3. * Stefan Wermter * Hamburg University, Germany IV. Submission 1. Topics of interest: - New methods and applications for crossmodal processing (e.g., integrating vision, audio, haptics, proprioception) - Machine learning and neural networks for multisensory robot perception - Computational models of crossmodal attention and perception - Bio-inspired approaches for crossmodal learning - Crossmodal conflict resolution and executive control - Sensorimotor learning for autonomous agents and robots - Crossmodal learning for embodied and cognitive robots 2. For paper submission, use the following IEEE template: <http://ras.papercept.net/conferences/support/support.php>* 3. Submitted papers should be limited to *2 pages (extended abstract)* or *4 pages (short paper)*. 4. Send your pdf file to barros(a)informatik.uni-hamburg.de AND jirak(a)informatik.uni-hamburg.de Selected contributions will be presented during the workshop as spotlight talks and in a poster session. Contributors to the workshop will be invited to submit extended versions of the manuscripts to a special issue (to be arranged). Submissions will be peer reviewed consistent with the journal practices. V. Important Dates * Paper submission deadline: August 15, 2018 * Notification of acceptance: September 5, 2018 * Camera-ready version: September 15, 2018 * Workshop: Friday 5 October 2018 VI. Organizers * German I. Parisi * Hamburg University, Germany * Pablo Barros * Hamburg University, Germany * Doreen Jirak * Hamburg University, Germany * Jun Tani * Okinawa Institute of Science and Technology, Japan * Yoonsuck Choe * Samsung Research & Texas A&M University, TX, USA -- 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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Four-year PhD in Computational Psychiatry at University College London
by Hrvoje Stojic July 30, 2018

July 30, 2018

Four-year PhD in Computational Psychiatry at University College London The International Max Planck Research School on Computational Methods in Psychiatry and Ageing Research (https://www.mps-ucl-centre.mpg.de/en) seeks applicants for PhD fellowships to be based at University College London (UCL). The PhD programme is strongly interdisciplinary and invites applications from potential students with a broad range of backgrounds including, but not limited to, neuroscience, mathematics, statistics, machine learning, computer science, physics, psychology, and medicine. This is an international doctoral programme of the Max Planck UCL Centre for Computational Psychiatry and Ageing Research, which has sites in London and Berlin. The programme offers unique training in concepts and methods from computer science and statistics in relation to substantive research questions in cognitive neuroscience, psychiatry, and lifespan psychology. Training involves seminars, methods workshops, participation in summer schools, and collaboratively supervised research. Students will take one module per semester for the first two years of the programme on topics that range from psychiatry and decision science to advanced computational and statistical methods. The main focus of the London site is to address cognitive and theoretical neuroscience questions relevant to understanding psychiatric disorders. Methods include neuroimaging and pharmacology, computational modelling of behaviour (learning, decision-making, emotion), and large-scale smartphone- and internet-based data collection. Students will have a primary supervisor within the Centre and possible supervisors include Tobias Hauser (http://v1.tobiashauser.ch/) and Quentin Huys (https://quentinhuys.com/) Collaboration is encouraged within the Centre and with other UCL departments including the Wellcome Centre for Human Neuroimaging (https://www.fil.ion.ucl.ac.uk/) and Gatsby Computational Neuroscience Unit (http://www.gatsby.ucl.ac.uk/) We offer a generous four-year studentship stipend of £23,263 (tax free) per year, PhD registration fees at the current Home/EU rate, research expenses, and funds for travel to conferences or courses. (If you are an international student please note that this studentship only covers UK/EU fees and you will need to provide the difference between UK/EU and overseas fees.) Students will participate in international summer schools, seminars and workshops linked to the Berlin site, and have the opportunity to conduct a research project of up to 6 months in Berlin. Requirements: This is a highly competitive programme. Successful applicants should have, or expect to get, at least an upper 2nd class degree (or the foreign equivalent), and should have some familiarity with computational and statistical methods. Students wishing to undertake a PhD with Quentin Huys are expected to take and pass courses (http://www.gatsby.ucl.ac.uk/teaching/courses/index.html) at the Gatsby Computational Unit. The next intake of students will be October 2018 (with the possibility for a later start date). UCL is committed to employing more people with disabilities and especially encourages them to apply. UCL also seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply. Deadline: 26 August 2018. Shortlisted candidates will be interviewed in early September 2018 via Skype. HOW TO APPLY. Please send: 1) a CV, 2) a statement of why you want to do the PhD (no more than 1 page), 3) a copy of your strongest piece of academic work (e.g., thesis, publication). Please also arrange for two reference letters to be sent to us by referees. Your surname should be the first word in the subject line of these emails. The statement should indicate which of the named supervisors you would be most interested in having as a primary supervisor (multiple faculty can be listed as potential supervisors). Please ensure that your surname is the first word in the subject line of the email and that all documents are clearly labelled with your surname and the type of document. All documents and references should be sent to MaxPlanckPhD(a)ucl.ac.uk by 26 August 2018 at midnight. Questions about the programme can be directed to MaxPlanckPhD(a)ucl.ac.uk.

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PhD Studentship, University College London, Centre for Medical Image Computing (CMIC)
by Andre Altmann July 30, 2018

July 30, 2018

We are looking to fill a 4-year PhD studentship in the field of imaging genetics to work on drug resistant Epilepsy: Epilepsy is a group of neurological conditions that share the common characteristic of epileptic seizures. There are many types of epilepsy and many types of seizure. The diagnosis of epilepsy typically follows the occurrence of two or more seizures. While anyone can develop epilepsy at any point in life, it is most commonly diagnosed in children and people over 65 years of age. Currently, about 60 million people are suffering from epilepsy worldwide and over 500,000 in the UK alone. Anti-epileptic drugs (AED) are intended to reduce the frequency of seizures or even completely eliminate them. If AED treatment works, it allows people living with epilepsy to lead normal lives. However, in about one third of people none of the available drugs or combinations of drugs stop the seizures. These cases are considered treatment-resistant epilepsies and the underlying biology is still poorly understood. In this project, the successful candidate will help to advance knowledge about drug-resistant epilepsy. Using a large database of neuroimaging data from people with epilepsy, the first aim is to establish the imaging signature of drug-resistant epilepsy compared to drug-responsive epilepsy. That is, which brain regions show different cortical thickness or structural and functional connectivity changes in resistant vs responsive epilepsy? The second phase of the project involves an imaging-genetics approach to elucidate the genetic origin of drug-resistant epilepsy, which may help to identify drug targets for currently drug-resistant epilepsy. More information on requirements and the medical imaging CDT can be found here: http://medicalimaging-cdt.ucl.ac.uk/news/machine-learning-based-investigati… Kind regards, Andre

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ANDA 2019 - G-Node Advanced Neural Data Analysis Course, April 1-18, 2019, Jülich, Germany
by Thomas Wachtler July 30, 2018

July 30, 2018

ANDA 2019 - G-NODE ADVANCED NEURAL DATA ANALYSIS COURSE April 1 - 18, 2019 Haus Overbach, Juelich-Barmen, Germany Techniques to record neuronal data from populations of neurons are rapidly improving. Simultaneous recordings from hundreds of channels are possible while animals perform complex behavioral tasks. The analysis of such massive and complex data becomes increasingly challenging. This advanced course aims at providing deeper training in state-of-the-art analysis approaches in systems neuroscience. The course is addressed to excellent master and PhD students and young researchers who are interested in learning advanced techniques in data analytics and in getting hands-on experience in the analysis of electrophysiological data. Internationally renowned researchers will give lectures on statistical data analysis and data mining methods with accompanying exercises. Students will define and perform their own analyses on provided data to solve a challenge. Participants are required to have a strong interest in data analysis, a background in a mathematical or related field, knowledge of algebra, matrix operations, and statistics, and need to have solid programming experience (preferably in Python). FACULTY · Moshe Abeles, Bar-Ilan Univ, Israel · Izhar Bar Gad, Bar-Ilan Univ, Israel · Michael Denker, Juelich Research Center and RWTH Aachen Univ, Germany · Sonja Grün, Juelich Research Center and RWTH Aachen Univ, Germany · Björn Kampa, RWTH Aachen, Germany · Tatiana Korotkova, MPI and University of Cologne, Germany · Christian Machens, Champalimaud Centre for the Unknown, Portugal · Martin Nawrot, University of Cologne, Germany · Yifat Prut, Hebrew Univ Jerusalem, Israel · Alexa Riehle, CNRS, Marseille, France · Alexander Thiele, University of Newcastle, UK · Thomas Wachtler, G-Node, LMU Munich, Germany TOPICS COVERED Single neuron properties and statistics · Stochastic processes · Surrogate methods · Detection of spatio-temporal patterns · Unitary Events · Statistical analysis of massively parallel spike data · Higher-order correlation analyses · Spike-LFP relationship · Population coding · State space analysis · Machine learning · Data mining · Data management, reproducibility, data sharing · Elephant toolbox REQUIREMENTS Applicants should be familiar with linear algebra, probability, differential and integral calculus and experienced using Python or Matlab. Preparatory reading material will be provided. Students should bring their own laptops and should be able to install software on their system. Students that do not have a suitable laptop should indicate this immediately after acceptance to the course. We will be able to provide a small number of laptops for the time of the course. COURSE FEE A course fee of 1000 Euros will be charged to cover costs for accommodation and meals. Limited financial support may be available for students that otherwise would not be able to attend, which is to be indicated in the application. HOUSING Accommodation in 2-bed rooms for students will be provided at the course site. HOW TO APPLY The application should include · a letter of motivation (max 1 page) · curriculum vitae (please indicate the relevant courses you have taken) · description of programming experience · a letter of recommendation. Please send all documents as a single PDF file to <advanced-course(a)g-node.org>. APPLICATION DEADLINE Applications must be received by SEPTEMBER 30, 2018. Early application is encouraged. For further information see http://www.g-node.org/anda2019 ORGANIZERS · Sonja Grün, Juelich Research Center and RWTH Aachen Univ, Germany · Martin Nawrot, University of Cologne, Germany · Yifat Prut, Hebrew University Jerusalem, Israel · Thomas Wachtler, G-Node, Ludwig-Maximilians-Universität München, Germany

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