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International Workshop on Computational Psychiatry at KAIST (Sang Wan Lee)
by Sang Wan Lee Sept. 27, 2016

Sept. 27, 2016

We are pleased to host a single day workshop on computational psychiatry, entitled "Computational approaches to understanding memory, learning, decision making, and emotion processing". The brain is an immensely complex system, making it hard to account for the brain's high-level functions, such as cognitive, emotional, and social components. The impairments of these circuits often lead to mental illness. A new approach, dubbed as computational psychiatry, combines experiments with computational modelling to provide the means to prevent psychiatric disorders. This workshop discusses recent advances in applying neuroeconomics, computational neuroscience, artificial intelligence to the study decision making, learning and memory. A deeper insight into these functions is expected to permit development of model-based diagnosis of mental disorders. * List of speakers : Benedetto de Martino (University of Cambridge, UK) Robb Rutledge (University College London, UK) Shinsuke Suzuki (Tohoku University, Japan) Sukbin Lim (New York University Shanghai, China) Sang Wan Lee (KAIST, South Korea) * Date: OCT 5 Wednesday, 2016 * Venue: Dream Hall (1st floor), CHUNG Moon Soul building (E16), KAIST * Program: http://www.aibrainlab.com/cp2016/ (free registration) - Host: Department of Bio and Brain Engineering, KAIST - Organizer: Sang Wan Lee (inquiries: Hana Park, <mailto:hnpark@kaist.ac.kr> hnpark(a)kaist.ac.kr) -- Sang Wan Lee, Ph. D Assistant Professor Laboratory for Brain and Machine Intelligence Department of Bio and Brain Engineering, KAIST 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea +82-42-350-4334 sangwan(a)kaist.ac.kr <mailto:sangwan@kaist.ac.kr> http://aibrain.kaist.ac.kr

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Two Postdoc Positions in Model-based fMRI and Machine Learning at KAIST (Sang Wan Lee)
by Sang Wan Lee Sept. 27, 2016

Sept. 27, 2016

*Postdoctoral positions in model-based fMRI and machine learning at KAIST* Two postdoctoral positions, funded through Samsung future technology foundation, are immediately available in the Laboratory for Brain and Machine Intelligence, led by Sang Wan Lee, at KAIST (http://aibrain.kaist.ac.kr/openings) The laboratory is affiliated with the department of Bio and Brain Engineering, Brain and Cognitive Engineering program, and KAIST Institute for Health Science and Technology (KIHST). The positions are initially appointed for one year, and may be extended for up to three years. We offer a competitive salary plus benefits. We also provide opportunities for working with our collaborators (refer to our website for more information). The project, AI-human coevolution engine, aims to develop a framework that boosts human learning and inference performance. It requires a combination of model-based neuroimaging techniques and a design of various learning algorithms. Research topics include model-based/model-free reinforcement learning, inductive/deductive/transitive inference, and one-shot learning. Details about the project is confidential, but the research proposal will be outlined during an interview. Qualifications (type1): - PhD in cognitive neuroscience, computational neuroscience, or physics - prior experience in (model-based) fMRI analysis is preferred. Qualifications (type2): - PhD in electrical engineering, computer science, or applied mathematics - strong quantitative background with expertise in machine learning The positions are open until filled. Interested please send a cover letter, a CV and a list of two references to sangwan(a)kaist.ac.kr. Applications will be reviewed on a rolling basis. -- Sang Wan Lee, Ph. D Assistant Professor Laboratory for Brain and Machine Intelligence Department of Bio and Brain Engineering, KAIST 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea +82-42-350-4334 sangwan(a)kaist.ac.kr <mailto:sangwan@kaist.ac.kr> http://aibrain.kaist.ac.kr

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Faculty Positions in Applied Mathematics at Northwestern University
by William L. Kath Sept. 26, 2016

Sept. 26, 2016

The Department of Engineering Sciences and Applied Mathematics at Northwestern University (http://www.mccormick.northwestern.edu/applied-math/) invites applications for two full-time, tenure-track faculty positions. Hiring will be focused on the assistant professor level, although exceptional applicants at a more senior level will be considered. Requirements include a Ph.D. and demonstrated ability to conduct high-impact interdisciplinary research in applied mathematics. Candidates in all areas are encouraged to apply. We are seeking candidates with strong connections to applications who can enhance the breadth of research activities of the department. Duties involve teaching and research. The application package should include a curriculum vita, a statement of research accomplishments and interests, and a statement of teaching experience and philosophy. Send applications to https://facultysearch.mccormick.northwestern.edu/apply/index/ODg In addition, the applicant should arrange for at least three, but no more than four letters of recommendation. Recommendation letters will be automatically solicited from the letter writers by email after the names are entered in the online application system. Questions may be sent to esam-facultysearch(a)northwestern.edu (Subject line: 2017 Faculty Search). Review of applications will begin November 15, 2016, and will continue until the positions are filled. For further information, see: http://www.mccormick.northwestern.edu/applied-math/. Northwestern University is an Equal Opportunity, Affirmative Action Employer of all protected classes including veterans and individuals with disabilities. Women and minorities are encouraged to apply. Hiring is contingent upon eligibility to work in the United States. -- William L. Kath, Professor Engineering Sciences and Applied Mathematics McCormick School of Engineering, Northwestern University 2145 Sheridan Road, Evanston, IL 60208-3125 Phone: 847-491-8784 Fax: 847-491-2178

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Reminder: deadline (October 1st 2016) approaching for manuscript submission to the Research Topic 'Burst coding: from cell to cognition'
by Fleur Zeldenrust Sept. 26, 2016

Sept. 26, 2016

This is a reminder: The deadline for manuscript submission to the Research Topic below is on Extended Article Submission Deadline: 01 Oct 2016 . Research Topic Title: Burst coding: from cell to cognition Topic Editor(s): Wytse J. Wadman, Fleur Zeldenrust, Bernhard Englitz Hosting Field: Frontiers in Neuroscience Journal: Frontiers in Computational Neuroscience Here are quick links to: - Manuscript guidelines: http://home.frontiersin.org/about/author-guidelines - Online submission: http://frontiersin.org/Computational_Neuroscience/researchtopics/Burst_codi… - List of article types and publishing fees: http://www.frontiersin.org/about/PublishingFees With best regards, Fleur Zeldenrust Guest Associate Editor, Frontiers in Computational Neuroscience Department of Neurophysiology Donders Centre for Neuroscience Faculty of Science, Radboud Universiteit Heyendaalseweg 135, postvak 33 6525 AJ Nijmegen tel +31 24 36 53195 www.frontiersin.org

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Brainhack: Anatomy edition in Paris, 24-26 October 2016
by Anastasia Osoianu Sept. 24, 2016

Sept. 24, 2016

Registration is now open for Brain-anatomy-hack in Paris. Brain-anatomy-hack aims at promoting the interaction between scientists on questions concerning (1) the investigation of brain anatomy based on magnetic resonance imaging or post mortem dissection; (2) the evolution of nervous systems across species; (3) the variability of brain features across healthy participants; (4) the insurgence of brain pathologies. Program Each of the three days will begin with an “Ignite Session” in which speakers will give 15-30 minute talks that will address wide questions in neuroanatomy that would benefit from open, interdisciplinary collaborations. The “Ignite Session” will be followed by a “hack session” and a dynamic set of talks organized by attendees. During the “hack sessions”, participants will be encouraged to work together in small groups on relevant projects. The workshop will culminate on the last day, when participants will have the opportunity to present a brief overview of their project. Participants can bring their own dataset, discuss a project and recruit a team of collaborators on site. Access to free big online databases of MRI images will be also available during the three days, for new creative ideas to be tested. This workshop is also an opportunity to learn methods, develop skills and collaborate with other participants. Participants will be supervised and receive advice on methods from a team of very capable nerds including Roberto Toro, Michel Thiebaut de Schotten, Alexandros Goulas, Julien Lefèvre, Daniel Margulies and Leonardo Cerliani throughout the three days. A constant flow of food and drinks (especially coffee…) will be provided during the conference breaks. We kindly request an online preregistration (70€) in order to cover the expenses for the venue, the equipment and the food/drinks. Deadline is 16th of October 2016. For more information and preliminary schedule visit: http://neuroanatomy.github.io/events/brainhack/ To learn more about Brainhack and previous events: http://brainhack.org/

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Job Posting--Senior Data Analyst--DiCarlo Lab
by Christopher Shay Sept. 23, 2016

Sept. 23, 2016

SENIOR DATA ANALYST Massachusetts Institute of Technology Brain and Cognitive Sciences McGovern Institute for Brain Research Lab of James J. DiCarlo http://dicarlolab.mit.edu/ <http://dicarlolab.mit.edu/> To develop and lead an open science framework for a small, but growing consortium of neuroscience research labs that together aim to build computational models of the brain’s visual processing system. Primary responsibilities are to (i.) develop and perform data packaging and analytics on visual neuroscience and behavioral psychophysics data, (ii.) implement existing and novel computational models of the visual system, and (iii.) build metrics and visualizations that quantitatively compare computational models to experimental data. The candidate will be based at the DiCarlo Lab at McGovern Institute for Brain Research at MIT. He or she will develop and implement standardized data formats, apply computational and statistical tools for data analysis, develop web-based graphical visualization interfaces to display results and present reports/results to the PIs, collaborators, and the research group. The data analyst will also oversee the lab’s local and remote computing hardware resources. REQUIRED: Bachelor’s degree, preferably in Computer Science or Software Engineering; Masters degree in (e.g.) Machine Learning, Statistics, or related field highly desirable; extensive familiarity with and high level of demonstrated competence in Python and Javascript absolutely required; working knowledge of C++ and C# languages highly desirable; minimum of five years’ experience with large-scale data-science activities, including organizing, analyzing, and synthesizing complex datasets to produce readable, informative automated web-based reports; demonstrated competence in modern data visualization tools such as Tableau, R, and D3.js; minimum of two years’ experience with large-scale database design and usage; minimum of two years' experience with building and maintaining a medium-to-large scale web-based application stack; experience with statistical languages/programming packages such as MATLAB (preferred), R, Stata, SPSS, or SAS highly desirable; experience with executing neural network feature extraction procedures on GPUs highly desirable. Demonstrated ability to partner with colleagues; ability to independently manage multiple projects/priorities while meeting changing deadlines, adapt to new technologies, and present information in a clear and cohesive manner; creative problem solving skills; ability to approach tasks with an eye toward efficiency and reproducibility; excellent written and oral communications skills; meticulous attention to detail; and proactive approach to investigative analysis. Experience in academic research, particularly computational neuroscience, is highly desirable. Christopher Shay, PhD Lab Manager DiCarlo Lab McGovern Institute Lab: (617) 452-3261 Fax:(617) 253-0104

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Software Engineer position in modeling at the Allen Institute for Brain Science
by Anton Arkhipov Sept. 23, 2016

Sept. 23, 2016

A new position is open at the Allen Institute for Brain Science: http://www.alleninstitute.org/what-we-do/brain-science/careers/job-search/ 2260-Modeling Analysis and Theory Software Engineer II Please apply at the link above! If you have any questions, please feel free to email antona(a)alleninstitute.org<mailto:antona@alleninstitute.org>. Job description. The Modeling, Analysis and Theory group is seeking a Software Engineer to contribute to one of our most crucial efforts - development of software tools for building, simulating, and analyzing models of brain cells and circuits. This individual will design and implement integrated software to enable modeling of brain activity and computations at several levels of resolution - from biophysically detailed to abstract, machine-intelligence-type models. The Software Engineer will be embedded in our Modeling team and will work closely with team members to combine the existing research code with newly designed and developed modules, in order to produce a high-quality modeling software suite for public release and continuous support. Job Responsibilities: * Design, implement, and maintain software for model building, simulation, and analysis/visualization across several modeling approaches using best software engineering practices. * In a team setting, actively participate in algorithm design and software planning and provide thoughtful solutions and alternatives. * Transform complex and high level requirement specifications into software code. * Present design solutions to peers, both within and outside of the Institute. * Manage multiple tasks, set clear priorities, and accomplish tasks with minimal supervision. Basic Qualifications: * BS, MS, or PhD in physical science, math, engineering or computer science discipline. * Strong background in scientific computing; experience in developing scientific modeling software, especially in computational neuroscience, is preferred. Experience with parallel computing is a plus. * At least 5 years of experience with python; familiarity with numpy/scipy. * Familiarity with modern version control systems, such as git. * Experience with Linux is essential; versatility in Windows and Mac platforms is desirable. * Experience in developing, shipping and maintaining robust, data-intensive applications. * Ability to meet aggressive timelines and deliverables in a collaborative environment. Desirable Qualifications: * C/C++ experience. * Graphics and/or data visualization experience. * Some experience in working with relational databases. * Familiarity with SQL. * Experience with maintaining open-source projects.

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postdoctoral position CRM, Barcelona
by Alex Roxin Sept. 22, 2016

Sept. 22, 2016

Dear all, The Computational Neuroscience Group at the Centre de Recerca Matemàtica in Barcelona, Spain is looking for applicants for a two-year postdoctoral position in the framework of the Collaborative Research Program funded by the Caixa Foundation, to foster collaboration between theory and experiment. Project Description: This project involves data analysis from animal experiments, computational modeling and translation of the results to a clinical setting. The candidate will study how cortical, brain stem and spinal cord circuits interact to drive motor function. This will involve analysis and modeling of high-dimensional data from experiments with stimulating electrodes in the motor cortex and spinal cord, and recording electrodes in forelimb muscles of rats, as well as experiments in which the EMG activity of muscles is recorded while the animal is engaged in a reaching and grasping task. In addition, we are initiating a collaboration with the Guttmann Institute for Neurorehabilitation to translate our results to human patients, in order to find a therapy to recover arm/hand function in patients with high spinal cord injuries. The candidate will work in close collaboration with Alex Roxin at the Group for Computational Neuroscience at the CRM and Guillermo Garcia Alias at the Neuroscience Institute at the UAB. Candiate Profile: We are looking for an enthusiastic and scientifically curious researcher with a strong technical background, ideally in mathematics, physics, engineering or related fields. A background in biology or neuroscience is not required. Application Deadline: October 24th. Decision early November. For more information and to apply go to http://www.crm.cat/en/Pages/DetallNoticia.aspx?ItemID=112 best regards, Alex

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MIT searching for tenure-track faculty at the assistant professor level
by Kate White Sept. 21, 2016

Sept. 21, 2016

The Department of Brain & Cognitive Sciences (BCS) (http://bcs.mit.edu<http://bcs.mit.edu/>) at MIT is looking to hire multiple tenure-track faculty at the assistant professor level. Affiliations with the Picower Institute for Learning & Memory and the McGovern Institute for Brain Research are possible. We are most excited about candidates who work in one or more of the following three areas: i. Computational approaches to neuroscience and cognition. Possible areas of focus include but are not limited to: statistical analysis of neural data and neural signal processing; computational modeling of neural circuits, of neural population representations and transformations; and/or of human cognitive processes. Candidates with the ability to build bridges across empirical domains are especially attractive. An affiliation with Electrical Engineering and Computer Science (EECS), the Computer Science and Artificial Intelligence Laboratory (CSAIL), the newly formed Statistics and Data Science Center, and Society (IDSS), or other allied departments is possible. The Department aims to make multiple hires in this area. ii. Systems neuroscience in non-human animals. The ideal candidate will be driven by the goal of reverse-engineering neural circuits underlying complex behaviors, and will employ novel technologies and computational approaches to understand the coding, dynamics, and/or anatomical underpinnings of these circuits. We will consider applicants who are working on a broad range of model organisms. iii. Cognitive neuroscience in humans and/or non-human primates. The ideal candidate would be conducting research that informs our understanding of cognition while bridging levels of analysis and using multiple methods, e.g. ECoG, fMRI, electrophysiology, MEG, computational modeling, genetics and reverse engineering approaches. Successful applicants are expected to develop and lead independent, internationally competitive research programs and to share in our commitment to excellence in undergraduate and graduate education by teaching courses and mentoring graduate and undergraduate students. PhD must be completed by start day of employment and some postdoctoral training is preferred. Please submit application materials - cover letter, CV, statement of research and teaching interests and representative reprints - online at https://academicjobsonline.org/ajo/jobs/8024. Please state research area in cover letter. To help direct the application, applicants should indicate which of the areas listed above is their main research area by selecting from the drop down list included in the application. In addition, please arrange to have three letters of recommendation submitted online. All application materials are due by midnight (EST) on October 31, 2016. MIT is an equal employment opportunity employer<https://www1.eeoc.gov/employers/upload/eeoc_self_print_poster.pdf>. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability. Best regards, Kate White Human Resources Administrator MIT | Department of Brain and Cognitive Sciences (BCS) 77 Massachusetts Ave., Bldg 46-2005A Cambridge, MA 02139 T: 617-253-5749 | E: kowhite(a)mit.edu<mailto:kowhite@mit.edu> Visit us online at http://bcs.mit.edu<http://bcs.mit.edu/> [cid:5DC388A1-6756-4BB2-81B2-79C29D68B533] *Please note, BCS HQ is currently located in temporary office space on the 5th floor, suite 46-5065 until further notice. I am located in office 46-5065C.

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Brian 2.0 release
by Dan Goodman Sept. 21, 2016

Sept. 21, 2016

We are very pleased to announce the release of version 2.0 of the Brian neural network simulator. Brian is a free, open source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms. We believe that a simulator should not only save the time of processors, but also the time of scientists. Brian is therefore designed to be easy to learn and use, highly flexible and easily extensible. You can learn more about Brian at our website (http://briansimulator.org) You can also try out Brian from your web browser, without having to install any software, using our interactive demo (http://mybinder.org/repo/brian-team/brian2-binder/notebooks/demo.ipynb) Major new features in 2.0 ------------------------- * Much more flexible model definitions. The behaviour of all model elements can now be defined by arbitrary equations specified in standard mathematical notation. * Code generation as standard. Behind the scenes, Brian automatically generates and compiles C++ code to simulate your model, making it much faster. * "Standalone mode". In this mode, Brian generates a complete C++ project tree that implements your model. This can be then be compiled and run entirely independently of Brian. This leads to both highly efficient code, as well as making it much easier to run simulations on non-standard computational hardware, for example on robotics platforms. * Multicompartmental modelling. * Python 2 and 3 support. That's just a small fraction of the new features in 2.0. For the full list, see http://brian2.readthedocs.io/en/stable/introduction/release_notes.html. Upgrading from Brian 1.4 ------------------------ Brian 2 is a rewrite from scratch, and introduces some backwards incompatible changes. In most cases, these should be relatively simple. We've written a detailed guide on how to update your simulations: http://brian2.readthedocs.io/en/stable/introduction/changes.html. Note that you can have both Brian 1 and Brian 2 installed simultaneously, so you can switch gradually. Thanks ------ Brian 2 was written by Marcel Stimberg, Dan Goodman and Romain Brette. Do please remember to cite Brian if you use it for your research. We would also like to thank the large number of users (over 40) who contributed code, bug reports, etc.

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MLINI 2016: NIPS Representation Learning in Artificial and Biological Neural Networks Workshop
by Leila Wehbe Sept. 20, 2016

Sept. 20, 2016

NIPS Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016) December 9th, 2016, Centre Convencions Internacional Barcelona, Barcelona, SPAIN *Call for papers and abstracts:* Submission deadline: *Tuesday, September 27th, 2016* Notification of acceptance: Wednesday, October 5th, 2016 Submission website: *https://cmt3.research.microsoft.com/MLINI2016 <https://cmt3.research.microsoft.com/MLINI2016>* Workshop Website: https://sites.google.com/site/mlini2016nips We invite submissions that are related, but not limited to: - Use of neural network and other methods as models of brain function - Machine learning methods, including deep learning, to analyze brain data - Cognitively plausible learning algorithms, or in general models that take insights from human brains or behavior We invite both: - Paper submissions, to be considered for online publication in arXiv proceedings, and poster presentation. The length should not exceed 6 pages in Springer format <http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0> (here are the LaTeX2e style files <ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip>), excluding the references. - Abstract submission, to be considered for poster presentation. The abstract should not exceed 500 words (figures are allowed). This workshop is in conjunction with a Frontiers topic entitled: Artificial neural networks as models of human brain function http://journal.frontiersin.org/researchtopic/4817/artificial -neural-networks-as-models-of-neural-information-processing Participants are strongly encouraged to submit their work to the Frontiers special topic edition (deadline is November 1st). *About the workshop:* This one day workshop is about the interface between cognitive neuroscience and recent advances in AI fields that aim to reproduce human performance, such as natural language processing or computer vision, and specifically the deep learning approaches in these disciplines. When studying the cognitive capabilities of the brain, scientists follow a system identification approach in which they present different stimuli to the subjects and try to model the evoked brain responses. The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus. Experimental stimuli are becoming increasingly complex with more and more researchers studying real life phenomena such as the perception of natural images or natural sentences. There is therefore a need for rich and adequate representations of the properties of the stimulus, that can be obtained using advances in NLP, computer vision or other relevant ML disciplines. In parallel, many new ML approaches, especially in deep learning, are inspired to a certain extent by human behavior or biological principles. Neural networks for example were originally inspired by biological neurons. More recently, processes such as attention are being used which are inspired by human behavior. However, the large bulk of these methods are independent of findings about brain function, and it is unclear whether it is at all beneficial for machine learning to try to emulate brain function in order to achieve the same tasks that humans are capable of performing. In order to shed some light on this difficult but exciting question, we plan to bring together many experts from these seemingly converging fields to discuss these problems, in a new highly interactive format consisting of two short lectures from experts in both fields, followed by a guided discussion. This workshop is a continuation of the Machine Learning and Interpretation in Neuroimaging (MLINI) series. MLINI has already had 5 iterations in which methods for analyzing and interpreting neuroimaging data were discussed in depth. In keeping with tradition, we also invite contributions from the expanding field of machine learning applied to neuroimaging data, and specifically the recent trend of utilizing neural network models to analyze brain data, which is evolving in parallel to the use of these algorithms as models of the information content in the brain. This way we will complete the loop: we will explore how neural networks and other machine learning tools contribute to neuroscience, both as a source of models for brain representations, and as a tool for brain image analysis. *Organizers:* Guillermo Cecchi (IBM T.J. Watson Research Center) Moritz Grosse-Wentrup (Max Plank Institute for Intelligent Systems) Georg Langs (Medical University of Vienna, CSAIL, MIT) Brian Murphy (Queens University, Belfast) Anwar Nunez-Elizalde (Helen Wills Neuroscience Institute, University of California, Berkeley) Irina Rish (IBM T.J. Watson Research Center) Marcel van Gerven (Donders Institute for Brain, Cognition and Behaviour, Nijmegen) *Leila Wehbe (Helen Wills Neuroscience Institute, University of California, Berkeley) - main contact

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MediTec 2016: Extended Last Date of Submission is September 30, 2016
by MediTec 2016 Sept. 20, 2016

Sept. 20, 2016

*(Apologies if you receive multiple copies of this CFP)* ****Last date for paper submission of MediTec 2016 has been extended upto SEPTEMBER 30, 2016 upon receiving many requests from potential authors and researchers for extension.* *Dear Sir/Madam,* This letter is to formally invite you to participate and submit your research works in the “*2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec 2016)*” themed "*Smart Systems for Healthcare*" from *17-18 December 2016,* which will take place at United International University (UIU), Dhaka, Bangladesh. *MediTec 2016* is technically co-sponsored by IEEE-EMBS Bangladesh Chapter and IEEE Bangladesh Section. This conference aims to bring together researchers, engineers, doctors, medical practitioners and experts in Bio-medical engineering, Healthcare, Intelligent Systems and Knowledge Management to share their experience, ideas and future scopes. Distinguished scientists, experts from both academia and industries will deliver keynote speeches and invited talks on trends and significant advances in the emerging technologies related to the conference topics. For the list of confirmed speakers, please *click here.* <http://meditec.uiu.ac.bd/speakers/> *Major areas of interest include, but not limited to, are:* 1. Biomedical and Health Informatics 2. Biomedical Signal and Image Processing 3. Therapeutic Systems and Clinical Technologies 4. Neuro and Rehabilitation Engineering 5. Biorobotics, Bionanotechnology and Wearable Biomedical Sensors and Systems 6. Computational Biology and Bioinformatics 7. Pharmaceutical Biotechnology 8. ICT, Health and Disabilities Please find the attachment for detailed *Call For Paper* or visit *here* <http://meditec.uiu.ac.bd/wp-content/uploads/2016/06/Meditech-2016_A4_Poster…> . *Important Dates:* *Extended Last Date of Submission *: * SEPTEMBER 30, 2016 * Paper Submission Deadline 30 September 2016 Acceptance Notification 15 October 2016 Camera Ready Submission 10 November 2016 Registration Early Bird: 15 November 2016 Late: Afterwards All the accepted papers will be included in Conference Proceedings and all the presented papers will be submitted to *IEEE Xplore Digital Library*. Selected papers from the conference proceedings will be invited for possible publication in special issues of some reputed international journals. Paper submission for *MediTec 2016* is now open. *Click here to Submit Your Paper. <https://easychair.org/conferences/?conf=meditec2016>* For more information, please visit conference website *http://meditec.uiu.ac.bd* <http://meditec.uiu.ac.bd/>*.* Kindly promote *MediTec 2016* to interested individuals, researchers and communities in your vicinity. We look forward to meet you in the conference. Thanking you, Best Regards, Khondaker A. Mamun, *Ph.D.* Conference Chair, MediTec 2016 Email: meditec(a)uiu.ac.bd Cell: +880 1776534220 Director, AIMS Lab (www.aimsl.uiu.ac.bd) Associate Professor, Department of Computer Science and Engineering ( www.cse.uiu.ac.bd) United International University, Dhaka, Bangladesh (www.uiu.ac.bd) Chair, IEEE Engineering in Medicine and Biology Society, Bangladesh Chapter Initiator, Bangladesh Research and Awareness Initiative of Neuroscience (BRAIN) Email: k.mamun(a)ieee.org, mamun(a)cse.uiu.ac.bd, k.mamun(a)utoronto.ca, mamun79bd(a)yahoo.com URL: http://cse.uiu.ac.bd/faculty/kmamun/

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Winter School on the Mathematics of Memory in Barcelona, Spain
by Alex Roxin Sept. 20, 2016

Sept. 20, 2016

Dear all, We are happy to announce a winter school on the Mathematics of Memory, the "Memory School" which will take place Jan.16-20 2017 at the Centre de Recerca Matemàtica in Bellaterra (Barcelona), Spain. This is a week-long intensive course on the biology and mathematics of memory, including plasticity, learning, working memory, long-term memory, hippocampal and cortical network mechanisms and more. The confirmed school instructors are Nicolas Brunel University of Chicago Shaul Druckmann Janelia Research Campus Wulfram Gerstner Laboratory of Computational Neuroscience Gianluigi Mongillo CNRS Sandro Romani Janelia Research Campus Harel Shouval University of Texas Misha Tsodyks Weizmann Institute of Science Mark van Rossum University of Edinburgh This course is appropriate for both junior and senior researchers. Attendees will also have the opportunity to present their own work in a poster session. For more information and to register please go to the website http://www.crm.cat/en/Activities/Curs_2016-2017/Pages/MATHMEM_School.aspx The Memory School is the opening event of a two-month long Intensive Research Program on the Mathematics of Memory at the CRM in Barcelona, Spain. Throughout the months of January and February there will be invited speakers, and the program will culminate in a week-long series of symposia on special topics on memory from leading experimentalists and theoreticians. For more information please go to http://www.crm.cat/en/Activities/Curs_2016-2017/Pages/IRP-MATHMEM.aspx best regards, Alex Roxin (and on behalf of the co-organizers Nicolas Brunel and Sandro Romani)

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[IJCNN 2017] Upcoming deadlines for Tutorials and Workshop Proposals
by Teng Teck Hou Sept. 19, 2016

Sept. 19, 2016

[Apologies for cross-postings] ############################################################ International Joint Conference on Neural Networks May 14-19, 2017, Anchorage, Alaska, USA http://www.ijcnn.org/ ##################### Important Dates ###################### * Tutorial and Workshop Proposals October 15, 2016 * Paper Submission November 15, 2016 * Paper Decision Notification January 20, 2017 * Camera-Ready Submission ebruary 20, 2017 #################### UPCOMING DEADLINES #################### CALL FOR WORKSHOPS http://www.ijcnn.org/call-for-workshops CALL FOR TUTORIALS http://www.ijcnn.org/call-for-tutorials [UPCOMING DEADLINES 23.59hr UTC-10 on Saturday, 15 OCTOBER 2016] ############################################################ The 2017 International Joint Conference on Neural Networks (IJCNN 2017) will be held at the William A. Egan Civic and Convention Center in Anchorage, Alaska, USA, May 14-19, 2017. The conference is organized jointly by the International Neural Network Society and the IEEE Computational Intelligence Society, and is the premiere international meeting for researchers and other professionals in neural networks and related areas. It will feature invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distributed intelligence. In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials and workshops on topics of current interest For the latest updates, follow us on Facebook (https://fb.me/ijcnn2017/) and Twitter (@ijcnn2017). #################### Paper Submission is now Open #################### http://www.ijcnn.org/call-for-papers * Regular paper can have up to 8 pages in double-column IEEE Conference format * All papers are to be prepared using IEEE-compliant Latex or Word templates on paper of U.S. letter size. * All submitted papers will be checked for plagiarism through the IEEE CrossCheck system. * Papers with significant overlap with the authors own papers or other papers will be rejected without review. ##########################Call for Workshops########################## Post-conference workshops offer a unique opportunity for in-depth discussions of specific topics in neural networks and computational intelligence. The workshops should be moderated by scientists or professionals who has significant expertise and /or whose recent work has had a significant impact within their field. IJCNN 2017 will emphasize emerging and growing areas of computational intelligence. Each workshop has a duration of 3 or 6 hours. The format of each workshop will be up to the moderator, and can include interactive presentations as well as panel discussions among participants. These interactions should highlight exciting new developments and current research trends to facilitate a discussion of ideas that will drive the field forward in the coming years. Workshop organizers can prepare various materials including handouts or electronic resources that can be made available for distribution before or after the meeting. Researchers interested in organizing workshops are invited to submit a formal proposal including the following information as a single file (pdf, doc, etc.) to the workshop chair: * Title * Organizers and their short bio * Brief description of the scope and impact of the workshop * Timeliness of the topic * Confirmed and/or potential speakers * Half day (3 hours) or full day (6 hours) * Link to organizer's web page and/or workshop web site (optional) For further details, please refer http://www.ijcnn.org/call-for-workshops. Any questions regarding this proposal can be asked to the Workshop Chair: Lazaros Iliadis, Democritus University of Thrace, Greece. E-mail: liliadis(a)fmenr.duth.gr ##########################Call for Tutorials########################## IJCNN 2017 will feature pre-conference tutorials addressing fundamental and advanced topics in computational intelligence. Tutorial proposals should be emailed to the Tutorial Chair (see below). A tutorial proposal should include the * Title * Presenter/organizer name(s) and affiliations * Expected enrollment * Abstract (less than 300 words) * Additional outline if needed * Presenter/organizer biography * Links to the presenter/organizer web page or the tutorial page (optional) * The proposal should not exceed two pages in 1.5 space, Times 12 point font. The tutorial format (preliminary) is 1 hour and 45 minutes with a 10-minute break. Researchers interested in organizing workshops are invited to submit a formal proposal. For further details, please refer to http://www.ijcnn.org/call-for-tutorials. Any questions regarding this proposal can be asked to the Tutorials Chair: Asim Roy, Arizona State University, USA. E-mail: ASIM.ROY(a)asu.edu ##################Topics and Areas of Interest################## This conference solicits papers addressing original works in topics and areas of interest including, but are not limited to: NEURAL NETWORK MODELS * Feedforward neural networks * Recurrent neural networks * Self-organizing maps * Radial basis function networks * Attractor neural networks and associative memory * Modular networks * Fuzzy neural networks * Spiking neural networks * Reservoir networks (echo-state networks, liquid-state machines, etc.) * Large-scale neural networks * Other topics in artificial neural networks MACHINE LEARNING * Supervised learning * Unsupervised learning and clustering, (including PCA, and ICA) * Reinforcement learning * Probabilistic and information-theoretic methods * Support vector machines and kernel methods * EM algorithms * Mixture models, ensemble learning, and other meta-learning or committee algorithms * Bayesian, belief, causal, and semantic networks * Statistical and pattern recognition algorithms * Visualization of data * Feature selection, extraction, and aggregation * Evolutionary learning * Hybrid learning methods * Computational power of neural networks * Deep learning * Other topics in machine learning NEURODYNAMICS * Dynamical models of spiking neurons * Synchronization and temporal correlation in neural networks * Dynamics of neural systems * Chaotic neural networks * Dynamics of analog networks * Neural oscillators and oscillator networks * Dynamics of attractor networks * Other topics in neurodynamics COMPUTATIONAL NEUROSCIENCE * Connectomics * Models of large-scale networks in the nervous system * Models of neurons and local circuits * Models of synaptic learning and synaptic dynamics * Models of neuromodulation * Brain imaging * Analysis of neurophysiological and neuroanatomical data * Cognitive neuroscience * Models of neural development * Models of neurochemical processes * Neuroinformatics * Other topics in computational neuroscience NEURAL MODELS OF PERCEPTION, COGNITION AND ACTION * Neurocognitive networks * Cognitive architectures * Models of conditioning, reward and behavior * Cognitive models of decision-making * Embodied cognition * Cognitive agents * Multi-agent models of group cognition * Developmental and evolutionary models of cognition * Visual system * Auditory system * Olfactory system * Other sensory systems * Attention * Learning and memory * Spatial cognition, representation and navigation * Semantic cognition and language * Neural models of symbolic processing * Reasoning and problem-solving * Working memory and cognitive control * Emotion and motivation * Motor control and action * Dynamical models of coordination and behavior * Consciousness and awareness * Models of sleep and diurnal rhythms * Mental disorders * Other topics in neural models of perception, cognition and action NEUROENGINEERING * Brain-machine interfaces * Neural prostheses * Neuromorphic hardware * Embedded neural systems * Other topics in neuroengineering BIO-INSPIRED AND BIOMORPHIC SYSTEMS * Brain-inspired cognitive architectures * Embodied robotics * Evolutionary robotics * Developmental robotics * Computational models of development * Collective intelligence * Swarms * Autonomous complex systems * Self-configuring systems * Self-healing systems * Self-aware systems * Emotional computation * Artificial life * Other topics in bio-inspired and biomorphic systems APPLICATIONS * Bioinformatics * Biomedical engineering * Data analysis and pattern recognition * Speech recognition and speech production * Robotics * Neurocontrol * Approximate dynamic programming, adaptive critics, and Markov decision processes * Neural network approaches to optimization * Signal processing, image processing, and multi-media * Temporal data analysis, prediction, and forecasting; time series analysis * Communications and computer networks * Data mining and knowledge discovery * Power system applications * Financial engineering applications * Applications in multi-agent systems and social computing * Manufacturing and industrial applications * Expert systems * Clinical applications * Big data applications * Smart grid applications * Other applications CROSS-DISCIPLINARY TOPICS * Hybrid intelligent systems * Swarm intelligence * Sensor networks * Quantum computation * Computational biology * Molecular and DNA computation * Computation in tissues and cells * Artificial immune systems * Other cross-disciplinary topics ################## Organizing Committee ###################### The full organizing committee can be found at: http://www.ijcnn.org/organizing-committee General Chair * Yoonsuck Choe, Texas A and M University, USA Program Chair * Christina Jayne, Robert Gordon University, UK Technical Co-Chairs * Irwin King, The Chinese University of Hong Kong, China * Barbara Hammer, University of Bielefeld, Germany ##################Sponsoring Organizations################## * INNS - International Neural Network Society * IEEE - Computational Intelligence Society * BSCS - Budapest Semester in Cognitive Science

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Postdoctoral fellowship in computational neuroscience at Harvard
by Jan Drugowitsch Sept. 18, 2016

Sept. 18, 2016

Dear all, Jan Drugowitsch (Department of Neurobiology, Harvard) and Sam Gershman (Department of Psychology, Harvard) are seeking a postdoctoral fellow to work on a project combining psychophysics, computational modeling, and clinical studies. The project focuses on visual structure discovery, using motion perception as a model system. Our goal is to understand how neural circuits represent and reason about complex combinatorial structures, and how these neural circuits break down in autism. Candidates must have a strong background in Bayesian modeling and computational neuroscience. Experience with visual psychophysics experiments is desirable but not essential. Applicants should send a CV and statement of research interests to Jan Drugowitsch (Jan_Drugowitsch(a)hms.harvard.edu) Applications will be reviewed until the position is filled. Best, Jan Drugowitsch Assistant Professor in Neurobiology Harvard Medical School

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JOB: Neuroimaging Data Scientist. Multi-modal MRI study of voice perception.
by Sylvain Takerkart Sept. 15, 2016

Sept. 15, 2016

The 4-year project LIVES (Learning with Interacting Views), funded by the French National Research Agency (ANR), aims at developing innovative multi-view and multi-modal machine learning algorithms. As part of the LIVES consortium, our team at the Institut de Neurosciences de la Timone (INT, http://www.int.univ-amu.fr) is in charge of acquiring a large *multi-sequence MRI* dataset that will be used to benchmark these methods. This will include *anatomical*, *diffusion-weighte*d and *functional* (both resting state and task-based) MRI data. Our goal is to characterize the inter-subject variability observed in the cortical activation patterns measured with task-based functional MRI in the auditory cortex, in a task that involves the processing of vocal information, using the other acquired modalities. *Missions*. The data scientist will set-up processing pipelines dedicated to analyze the different MR scan types (univariate and multi-voxel pattern analysis methods for task-fMRI; advanced characterization of the cortical anatomy using high-level representations such as sulci and sulcal pits; estimation of structural and functional connectomes). He/she will organize the raw and processed data in a standardized fashion in order to make it accessible to the other members of the consortium in a first stage, and to the scientific community at large in a second stage. *Profile*. This position is for a candidate who is comfortable (or proficient) with *computer programming* (*python* will be our language of choice), has knowledge in *signal and image processing* and has a personal interest for *machine learning*. It can be of interest to different types of profiles. Either you are a young graduate (BSc or MSc) in data science, electrical engineering or neuroscience and you want to be at the heart of a project where medical imaging and machine learning meet, with the possibility to continue as a PhD student. Or you already have a PhD in a relevant domain and you want to take this opportunity to fulfill the aforementioned missions while getting further involved in the research aspects of the project, in machine learning or in neuroimaging. *Working environment*. INT is one of the top French neuroscience research institutes with 150 staff members in 10 inter-disciplinary teams examining different aspects of the cerebral organization. It is located on the medical campus of Aix-Marseille University. The successful candidate will join INT’s Neuro-Computing Center, who operates a high-end computing facility and pursues research at the intersection of machine learning and neuroscience. He/she will interact with other teams working on functional imaging of voice perception, computational anatomy, machine learning and MRI data acquisition. Marseille, the second largest city in France, is a vibrant inter-cultural hub located on the Mediterranean shore, and only 2h away from the Alps mountains. The position is initially open for one year, with a possible renewal for a second year. Starting date is around December 2016. If you are interested, please send your resume and cover letter before Septembre 30, 2016, to Sylvain_DOT_Takerkart_AT_univ-amu_DOT_fr and Pascal_DOT_Belin_AT_univ-amu_DOT_fr. -- Sylvain Takerkart Institut des Neurosciences de la Timone (INT) UMR 7289 CNRS-AMU Marseille, France tél: +33 (0)4 91 324 007 http://www.int.univ-amu.fr/_TAKERKART-Sylvain_?lang=en

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Tenure-Track Position in Computational Neuroscience, University of Oregon
by Yashar Ahmadian Sept. 15, 2016

Sept. 15, 2016

*Tenure track position in Computational Neuroscience* University of Oregon Departments of Mathematics and Biology (http://math.uoregon.edu/ http://biology.uoregon.edu/) Institute of Neuroscience (http://www.neuro.uoregon.edu/) The Departments of Biology and Mathematics and the Institute of Neuroscience at the University of Oregon announce a tenure track faculty position in computational neuroscience at the rank of assistant professor with ultimate department and institute affiliations flexible. Theorists seeking synergistic interactions with experimentalists in design, analysis and interpretation of research linking gene function, neuronal activity and behavior in model organisms and humans are especially encouraged to apply. This new position is part of an integrated effort to strengthen research at the nexus of mathematics and biology at the University of Oregon. It also advances a broader “Neurons to Minds” initiative that includes multiple new positions in systems and cognitive neuroscience to accelerate discovery in the neuronal basis of behavior and cognition. Minimum qualifications for candidates are a Ph.D. in an appropriate field (e.g. neuroscience, biology, mathematics, physics, electrical engineering, statistics, computer science, etc.), commitment to excellent teaching at the undergraduate and graduate levels, and an outstanding research record. Candidates should have the ability to work effectively within a diverse community. Applications will be received online at https://www.mathjobs.org/jobs/UO/IONMATHBIO. Candidates are asked to submit a cover letter, a curriculum vitae including a publication list, a statement of research accomplishments and future research plans, a description of teaching experience and philosophy, and three letters of recommendation (sent independently). Submission of 1-3 selected reprints is encouraged. To be assured of consideration, application materials should be uploaded by *November 15, 2016, but the position will remain open until filled*. Requests for information can be sent to Dr. Shawn Lockery, Chair, Computational Neuroscience Search Committee (shawn(a)uoregon.edu) *The University of Oregon is an equal opportunity, affirmative action institution committed to cultural diversity and compliance with the ADA. The University encourages all qualified individuals to apply, and does not discriminate **on the basis of any protected status, including veteran and disability status.* *---------------------------------------------* *Yashar Ahmadian* *Institute of Neuroscience* Departments of Biology and Mathematics *University of Oregon* *http://uoneuro.uoregon.edu/ahmadian/index.php <http://uoneuro.uoregon.edu/ahmadian/index.php>*

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Postdoc and Research Assistant openings in cognitive neuroscience at UCSF/Stanford
by Connolly, Colm Sept. 14, 2016

Sept. 14, 2016

Please respond to info(a)brainlens.org<mailto:info@brainlens.org> Research Assistant Position at UCSF in California, USA on Multilingualism The Hoeft Lab (http://brainLENS.org PI: Fumiko Hoeft MD PhD) is looking for an exceptional research assistant interested in the developmental cognitive neuroscience of multilingualism. This NIH-funded research is in collaboration with Cammie McBride PhD of Chinese Univ of Hong Kong, Ken Pugh PhD of Haskins/Yale/UConn, Linda Siegel of UBC, Manolo Carreiras PhD of BCBL, Ioulia Kovelman PhD of U Michigan, and Yuuko Uchikoshi EdD of UC Davis. The position is suited for a self-motivated, organized and independent thinker, with good interpersonal and multitasking skills, as well as proficiency in the English language and computer software. Neuroimaging, computational, statistical, programming, and neuropsych testing skills are highly desired but not necessary. Native-like proficiency in Spanish or Cantonese is a plus. A major strength of our lab is that there are plenty of opportunities to conduct independent research, to be first author on publications and to give conference presentations as a novice RA (e.g. Myers et al. Psychol Sci ’14, Myers et al. SCAN ’16, Haft et al. Curr Opin Behav Sci ’16). The position begins in January but start date can be sooner and is negotiable. Please email info(a)brainlens.org with a cover letter and your CV. Please add “[RA job]” and your full name in the Subject of the email. Qualified candidates will be asked to have 2-3 letters of reference forwarded. Joint Postdoctocal Position at UCSF and Stanford University in California, USA The Hoeft Lab (http://brainLENS.org PI: Fumiko Hoeft MD PhD) at the UCSF Dept of Psychiatry and Weill Institute for Neurosciences in collaboration with the Stanford Mood and Anxiety Disorders Lab in the Dept of Psychology (http://goo.gl/dc96Cx PI: Ian Gotlib PhD) is looking for an exceptional postdoc in the field of affective neuroscience, with advanced neuroimaging, psychophysiology, computational, programming and organizational skills. Training in genetics is a plus. The primary project that the postdoc will be responsible for is the examination of intergenerational neuroimaging using a ‘natural’ cross-fostering design that allows dissociation of genetic, prenatal and postnatal environment on brain networks that are transmitted across generations. Related articles from our lab can be found here - Yamagata et al. J Neurosci 2016 (http://goo.gl/vMK8iy) Ho et al. Trends in Neurosci 2016 (http://goo.gl/SyXLcK) and Scientific American (http://goo.gl/YTiH6D) There are other funded opportunities to be involved in research that examines the impact of anxiety and stereotype on psychophysiology and cognitive processes. The position can begin immediately. Please email info(a)brainlens.org with your CV, and with brief paragraphs of research interests, career goals and why you feel you are a good fit for the lab. Please add “[Postdoc job]” and your full name in the Subject of the email. Qualified candidates will be asked to have 3 letters of reference forwarded. Regards, -- Colm G. Connolly PhD Dept of Psychiatry & Langley Porter Psychiatric Institute UCSF Weill Institute for Neurosciences University of California, San Francisco 401 Parnassus Avenue, San Francisco, CA 94143

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Conference Aspects of Neuroscience [Warsaw, Poland, 25-27th November]
by Daniel Borek Sept. 13, 2016

Sept. 13, 2016

Dear Friends, On behalf of the Neurobiology Scientific Student Association at the Faculty of Biology of the University of Warsaw we would like to invite you to* the 6th annual International Conference “Aspects of Neuroscience” *to be held in Warsaw, Poland, from *November 25th to 27th, 2016*. As in previous years, the Conference is divided into 4 sessions presenting different approaches to Neuroscience:*Neurobiology, Cognitive Neuroscience, Clinical Neuroscience *and* Computational Neuroscience*. Each thematic session is opened by a lecture delivered by one of the guests who are specialists in a given domain. This year we will host such outstanding neuroscientists as *Prof. Eva Jablonka, Prof. Christian Lüscher, Prof. David Price, Prof. Kevin Warwick, Prof. Giovanna Mallucci *and* Dr. Daniel S. Margulies*. Participants are encouraged to give an oral presentation in one of the thematic sessions following the guest’s lecture or to present a poster during the poster session (either in the experimental or theoretical section). *Abstract submission deadline: 1st November 2016Passive registration deadline: 10th November 2016* The conference is intended for students, PhD students and research workers in neuroscience or related fields. The interdisciplinary character of the Conference is a base for integration of diverse scientific environments in order to create new quality in brain research. The language of the conference is English. *For more information about the Conference visit our new website:* http://neuroaspects.org <http://neuroaspects.us14.list-manage.com/track/click?u=c2e0466a746fafa55697…> Find us on Facebook: https://www.facebook.com/AspectsOfNeuroscience <http://neuroaspects.us14.list-manage.com/track/click?u=c2e0466a746fafa55697…> Yours sincerely, Organizing Committee of the International Conference Aspects of Neuroscience Neurobiology Scientific Student Association at the University of Warsaw

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Brains and Bits: Neuroscience Meets Machine Learning - NIPS Workshop: 9 & 10 December 2016
by Konrad Kording Sept. 13, 2016

Sept. 13, 2016

Join us (submit papers to) for the *Brains and Bits: Neuroscience Meets Machine Learning* *NIPS Workshop: 9 & 10 December 2016 Barcelona, Spain* http://www.stat.ucla.edu/~akfletcher/brainsbits.html The goal of this workshop is to bring together researchers in deep learning, machine learning, statistics, and computational neuroscience, and facilitate discussion about a) shared approaches for analyzing biological and artificial neural systems, b) how insights and challenges from neuroscience can inspire progress in machine learning, and c) machine learning methods for interpreting the revolutionary large scale datasets produced by new experimental neuroscience techniques. We invite high-quality submissions of abstracts for posters and contributed talks though CMT. Instructions are below. OverviewExperimental methods for measuring neural activity and structure have undergone recent revolutionary advances, including in high-density recording arrays, population calcium imaging, and large-scale reconstructions of anatomical circuitry. These developments promise unprecedented insights into the collective dynamics of neural populations and thereby the underpinnings of brain-like computation. However, these next-generation methods for measuring the brain’s architecture and function produce high-dimensional, large scale, and complex datasets, raising challenges for analysis. What are the machine learning and analysis approaches that will be indispensable for analyzing these next-generation datasets? What are the computational bottlenecks and challenges that must be overcome? In parallel to experimental progress in neuroscience, the rise of deep learning methods has shown that hard computational problems can be solved by machine learning algorithms that are inspired by biological neural networks, and built by cascading many nonlinear units. In contrast to the brain, artificial neural systems are fully observable, so that experimental data-collection constraints are not relevant. Nevertheless, it has proven challenging to develop a theoretical understanding of how neural networks solve tasks, and what features are critical to their performance. Thus, while deep networks differ from biological neural networks in many ways, they provide an interesting testing ground for evaluating strategies for understanding neural processing systems. Are there synergies between analysis methods for biological and artificial neural systems? Has the resurgence of deep learning resulted in new hypotheses or strategies for trying to understand biological neural networks? Conversely, can neuroscience provide inspiration for the next generation of machine-learning algorithms? We welcome participants from a range of disciplines in statistics, applied physics, machine learning, and both theoretical and experimental neuroscience, with the goal of fostering interdisciplinary insights. We hope that active discussions among these groups can set in motion new collaborations and facilitate future breakthroughs on fundamental research problems. ------------------------------ Organizers - Eva Dyer <http://kordinglab.com/people/eva_dyer/> Northwestern University - Allie Fletcher <http://www.stat.ucla.edu/~akfletcher/> Statistics, UCLA & UCB Redwood Center for Neuroscience - Konrad Kording <http://koerding.com//> Rehabilitation Institute of Chicago, Northwestern University - Jascha Sohl-Dickstein <http://www.sohldickstein.com/> Google Research - Joshua Vogelstein <http://jovo.me/> Biomedical Engineering, Johns Hopkins University - Jakob Macke <http://www.mackelab.org/> caesar Bonn, an Institute of the Max Planck Society Current Speakers - Christos Papadimitriou <https://people.eecs.berkeley.edu/~christos/> EECS, UC Berkeley - Adrienne Fairhall <https://fairhalllab.com/> Psychology and Biophysics, University of Washington - Yoshua Bengio <http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html> Computer Science and Operations Research, Université de Montréal - Sophie Denève <http://iec-lnc.ens.fr/group-for-neural-theory/members/faculty-in-alphabetic…> Group for Neural Theory, LNC, DEC, ENS - Demis Hassabis <http://demishassabis.com/> Google DeepMind - Terry Sejnowski <http://www.salk.edu/scientist/terrence-sejnowski/> Howard Hughes Medical Institute and Salk Institute, UCSD - Mitya Chklovskii <https://www.simonsfoundation.org/simons-center-for-data-analysis/scda-neuro…> Simons Foundation - Anima Anandkumar <http://newport.eecs.uci.edu/anandkumar/> EECS, UC Irvine - David Cox <http://www.coxlab.org/> Molecular and Cellular Biology and Computer Science, Harvard University - Surya Ganguli <https://web.stanford.edu/dept/app-physics/cgi-bin/person/surya-gangulijanua…> Applied Physics, Stanford University - Maneesh Sahani <http://www.gatsby.ucl.ac.uk/~maneesh/> Gatsby Institute, University College, London - Emily Fox <https://www.stat.washington.edu/~ebfox/> Statistics and Computer Science, University of Washington - Jonathan Pillow <http://pillowlab.princeton.edu/> Center for Statistics and Machine Learning, Princeton University - Fred Hamprecht <https://hciweb.iwr.uni-heidelberg.de/people/fhamprec> Heidelberg Collaboratory for Image Processing (HCI), Heidelberg - Max Welling, University of Amsterdam Submission Information & Important Dates Submissions will be considered both for poster and oral presentation. See instructions on the workshop website at http://www.stat.ucla.edu/~ akfletcher/brainsbits.html Submission deadline: 29 September 2016 11:59 PM PDT (UTC -7 hours) Acceptance notification: 12 October 2016

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2 open faculty positions at U. Colorado Medical School
by Zylberberg, Joel L Sept. 12, 2016

Sept. 12, 2016

Hello All, The Department of Physiology and Biophysics at the University of Colorado Medical School currently has two open tenure-track faculty positions. Specifically, we are searching for a new computational neuroscience / physiology faculty member, and for a new systems neuroscience / physiology faculty member. The positions at our medical school are quite nice for research-focused faculty: light teaching loads, extremely competitive start-up packages, etc. Details are available via this web link: http://www.ucdenver.edu/academics/colleges/medicalschool/departments/physio… If you have any questions, please don't hesitate to ask me. Also, please forward this advertisement to any colleagues of yours who might be interested. Thanks! __ Joel Zylberberg Assistant Professor Department of Physiology and Biophysics University of Colorado School of Medicine www.jzlab.org<http://www.jzlab.org> CIFAR Azrieli Global Scholar, Learning in Machines and Brains program ~the neuroscience is theoretical, but the fun is real

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Third International Conference on Mathematical Neuroscience (ICMNS 2017)
by Robert Rosenbaum Sept. 12, 2016

Sept. 12, 2016

We are delighted to announce the 3rd International Conference on Mathematical Neuroscience (ICMNS 2017), to be held at the Hotel Boulderado in Boulder, Colorado from May 30 to June 2 (Tutorials: May 30; Main Conference: May 31 - June 2). http://www.colorado.edu/amath/international-conference-mathematical-neurosc… *Abstract submission will open in October/November for both contributed talks and posters. *Student presenters can apply for travel awards funded by NSF, Burroughs Wellcome, and SIAM. The conference provides a forum for researchers to discuss current mathematical innovations emerging in neuroscience. We hope to attract and train young researchers on current methods in mathematical neuroscience. The meeting will feature leaders in applied mathematics and neuroscience that are developing new mathematical techniques for understanding high-dimensional data sets, building models to capture activity patterns and emergent computation, and working closely with experimentalists to address targeted questions about brain function. CONFIRMED SPEAKERS Danielle Bassett (University of Pennsylvania) Paul Bressloff (University of Utah) Nicolas Brunel (University of Chicago) Sophie Deneve (École Normale Supérieure) Brent Doiron (University of Pittsburgh) Ila Fiete (University of Texas) Stefano Fusi (Columbia University) Peter Thomas (Case Western Reserve University) Taro Toyoizumi (Riken Brain Science Institute) More TBA! We hope to see you in Boulder this coming May! All the best Zack Kilpatrick (University of Colorado) Julijana Gjorgjieva (Max Planck Institute for Brain Research) Robert Rosenbaum (University of Notre Dame) (conference chairs)

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PhD position in Computational/Systems Neuroscience @ the Institute of Neuroinformatics - ETH Zurich
by Benjamin Grewe Sept. 12, 2016

Sept. 12, 2016

PhD position in Computational/Systems Neuroscience @ the Institute of Neuroinformatics - ETH Zurich – in vivo imaging of neural ensemble learning in awake behaving animals – We are looking for a PhD candidate with a strong motivation to understand neuronal information processing (NIP) as well as learning induced changes in NIP at the level of large neuronal ensembles. To investigate NIP we utilize cutting edge large scale in vivo population calcium imaging techniques that allow recording ensemble activity of hundreds of neurons in freely moving and head-fixed rodents during diverse learning behaviors. Ideal candidates have some expertise in a neuroscience related field. The PhD candidate will acquire a unique skill set to utilize miniaturized and very large-scale calcium imaging technologies and work closely with the PI to perform behavior experiments and post-hoc data analysis. Applicants should hold a diploma or Masters degree in a quantitative discipline (e.g. physics, informatics, mathematics, biomedical-engineering) or neurobiology/neuroscience (e.g. systems neuroscience, neurophysiology, cell physiology). Experience in data analysis using MATLAB/Python/R or similar programming languages and basic knowledge in computational or experimental neuroscience is beneficial. Proficiency in English and a team-oriented working attitude are crucial for the applicant’s success. Candidates should be willing to perform biological in vivo experiments in rodents with an intrinsic motivation to unravel neural algorithms of information processing and learning. Please submit your complete application as one PDF via email to bgrewe(a)ethz.ch <mailto:bgrewe@ethz.ch>. The application should include the following documents: • A cover letter that includes the main motivation for doing a PhD at the INI. • Contact details of 2 references (email and phone number). • CV outlining prior education, research and publications (if available) • Academic transcripts (bachelor, master/diploma). • 1-2 page personal research statement. The research statement should cover the applicant’s future career goals and research project interests. The INI offers advanced, interdisciplinary PhD training and participation in cutting edge neuroscience and neuroinformatics research projects. The institute is home to a lively and interactive educational atmosphere with highly competitive salaries. Successful candidates will participate in the ZNZ doctoral program (http://www.neuroscience.uzh.ch/en.html <http://www.neuroscience.uzh.ch/en.html>) where they receive additional theoretical and practical training. Compensation/salary will be paid according to the guidelines of the Swiss National Science Foundation. Successful candidates will receive their PhD from ETH Zurich. Applications will be accepted until the position is filled and will be reviewed upon receipt. The Swiss Federal Institute of Technology (ETH) in Zurich is consistently ranked the top university in continental Europe and is a leading player in research and education in Switzerland and worldwide. The INI is a joint institute of ETH and the University of Zurich with a primary research focus at the intersection of systems neuroscience and neuroinformatics. The mission of the institute is to discover key principles by which neural networks operate and process information. Comprising a diverse research portfolio (www.ini.uzh.ch <http://www.ini.uzh.ch/>) the INI is uniquely positioned to bridge the gap between traditional systems neuroscience, neurobiology, neuroinformatics and neuroengineering. --------------------------------------------------------------------- Benjamin F. Grewe Institute of Neuroinformatics @ ETH Zurich Winterthurerstrasse 190 CH-8057 Zurich, Switzerland Email: Bgrewe(a)ethz.ch

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Final notice - 2016 EEGLAB workshop following SFN, Nov 17-21, San Diego
by Arnaud Delorme Sept. 10, 2016

Sept. 10, 2016

EEGLAB developers and advanced users will present a four day workshop on the data analysis concepts and functions of the EEGLAB electrophysiological data processing environment and associated toolboxes held at the University of California San Diego in La Jolla, November 17-21, 2016 following the Society for Neuroscience meeting. In particular, we will feature our new improved statistical framework and data management strategies based on the General Linear Model with support from the LIMO EEGLAB extension. The preliminary program, registration instructions, as well as list of accommodation are available at http://sccn.ucsd.edu/wiki/EEGLAB_2016_at_UCSD <http://sccn.ucsd.edu/wiki/EEGLAB_2016_at_UCSD> Registration costs are 350 US dollars for students and post-docs and 500 US dollars for faculty and professionals. The workshop is a non profit event and faculty are volunteering their time. Registration will be handled by Eventbrite below. https://www.eventbrite.com/e/the-ucsd-eeglab-2016-workshop-san-diego-ticket… <https://www.eventbrite.com/e/the-ucsd-eeglab-2016-workshop-san-diego-ticket…> For administrative questions about the location, hotel reservation, registration payments, and workshop overall organization please contact Risa at rshibata(a)ucsd.edu <mailto:antonio.cataldo2@unibo.it>. For questions about the scientific program, please contact EEGLAB organizers at eeglab(a)sccn.ucsd.edu <mailto:eeglab@sccn.ucsd.edu>. We are looking forward to see you at the workshop! A. Delorme S. Makeig

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Neural Processing Letters: CFP - Special Issue on "Off the mainstream: advances in neural networks and machine learning for pattern recognition"
by Friedhelm Schwenker Sept. 9, 2016

Sept. 9, 2016

Call for Papers Special issue on Off the mainstream: advances in neural networks and machine learning for pattern recognition to be published in Neural Processing Letters *** Submission deadline: January 31, 2017 *** Guest editors: Edmondo Trentin, University of Siena, Italy (trentin(a)dii.unisi.it) Friedhelm Schwenker, University of Ulm, Germany (friedhelm.schwenker(a)uni-ulm.de) Nemat El Gayar, Cairo University, Egypt (elgayar.neamat(a)gmail.com) Hazem M. Abbas, Ain Shams University, Egypt (as(a)eng.asu.edu.eg) Aims and scope of the special issue: "Mainstream science is about publishing what everyone else is publishing with very small changes. You'd better at least start off that way if you want to get tenure," the sociologist Rodney Stark said. But "big ideas don't come to those who avoid risk", as John Bohannon added. The area of artificial neural networks (ANN) and machine learning (ML) makes no exception to these ends: Mainstream topics, originally stemming from exciting breakthroughs (the "big ideas") that gradually become trends and end-up being mostly over-beaten publishing tracks, have characterized the scientific literature throughout the whole history of these research fields. A few, widely known instances of such (more or less recent) mainstream trends are: - Supervised support vector machine training (in both primal and dual) - Supervised multilayer perceptron training via regular backpropagation - Radial basis functions networks - Bayesian networks (either shallow or deep) - Deep feed-forward and convolutional neural networks - Countless applications of the aforementioned machineries - The “approximation capabilities” of such machineries Based on these premises, this special issue invites paper submissions on real novel research developments in the areas of neural networks and learning machines that (1) are rooted in (or, aimed at) pattern recognition (PR), and that, above all, (2) do not follow in the footsteps of nowadays established trends. Preference (over applications, theoretical analysis, and variants of established techniques) will thus be given to submissions that hand out fresh and innovative ideas/architectures/algorithms, even if they are in their infancy (e.g., possibly lacking of a complete investigation of their theoretical properties). A detailed list of topics of interest would contradict the very perspective of the present special issue. Nonetheless, some general, topical research directions are (to name a few): - New ANN or ML architectures - New ANN, ML, or PR algorithms - New estimation/optimization/assessment techniques for ANNs, ML, or PR - New and sound combination/hybridization of machines - Solutions to new, relevant PR-related problems - New and sound solutions to established PR-related problems If you are not sure on whether your manuscripts matches the aims and scope of this special issue or not, do not hesitate to get in touch with the guest editors at any time. The special issue will comprise (a) papers submitted in response to this call, and (b) extended versions of selected papers from the ANNPR 2016 Workshop (https://neuro.informatik.uni-ulm.de/ANNPR2016/) sponsored by the International Association for Pattern Recognition. Paper submission: Papers must be submitted online via the Neural Processing Letters website (https://www.editorialmanager.com/nepl/default.aspx) selecting the choice that indicates this special issue (identifier: S.I.:Off_mainstream). Prepare your paper following the Journal guidelines for Authors (http://www.springer.com/computer/ai/journal/11063?detailsPage=pltci_1060677) All submitted papers will undergo a regular peer-review process. Important dates: Opening of electronic submission: September 1, 2016 Submission deadline: January 31, 2017 Completion of 1st round of review process: March 31, 2017 Re-submission of revised manuscripts: May 15, 2017 Final decision: June 30, 2017 Tentative publication of the Special Issue: Fall 2017 -- PD Dr. Friedhelm Schwenker University of Ulm Institute of Neural Information Processing D-89069 Ulm, Germany phone: +49-731-50-24159 fax: +49-731-50-24156 email: friedhelm.schwenker(a)uni-ulm.de www: http://www.uni-ulm.de/in/neuroinformatik/mitarbeiter/f-schwenker.html

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