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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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