October 2014 - Comp-neuro - lists.cnsorg.org

3 year postdoc in London, modelling bee brains
by Lars Chittka Oct. 6, 2014

Oct. 6, 2014

Hello, We have a 3-year postdoc position (HFSP funded) to work on a project entitled: "A neural circuit approach to cognition and its limits in microbrains". The postdoctoral fellow will focus on building computational models of the bee visual system and higher-level brain centres. See here: http://www.jobs.qmul.ac.uk/5111 Informal inquiries can be sent to l.chittka(a)qmul.ac.uk Thank you and best wishes, Lars -- Lars Chittka, PhD, MSc Professor in Sensory & Behavioural Ecology School of Biological & Chemical Sciences Fogg Building Queen Mary, University of London Mile End Road London E1 4NS UK Email: l.chittka(a)qmul.ac.uk Tel: *44 (0) 20 7882 3043 Fax: *44 (0) 20 7882 7732 http://chittkalab.sbcs.qmul.ac.uk/ Room 2.13 via 2.10 -- Lars Chittka, PhD, MSc Professor in Sensory & Behavioural Ecology School of Biological & Chemical Sciences Fogg Building Queen Mary, University of London Mile End Road London E1 4NS UK Email: l.chittka(a)qmul.ac.uk Tel: *44 (0) 20 7882 3043 Fax: *44 (0) 20 7882 7732 http://chittkalab.sbcs.qmul.ac.uk/ Room 2.13 via 2.10

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Multi-faculty hiring initiative in Computational and Theoretical Neuroscience, The University of Texas at Austin
by Ila Fiete Oct. 6, 2014

Oct. 6, 2014

The University of Texas at Austin announces two faculty positions in Computational and Theoretical Neuroscience, as part of a multi-faculty hiring initiative. The search is open at all levels, from Assistant Tenure-track to Tenured Full Professor. Please see the advertisements below. *Full|Associate|Assistant Professor in Computational and Theoretical Neuroscience, Departments of Neuroscience and Mathematics at the University of Texas at Austin * The Department of Neuroscience at the University of Texas at Austin seeks exceptional researchers in the field of computational and theoretical neuroscience for appointment at the levels of Full Professor to Assistant Professor. The position will be joint with the Department of Mathematics, thus we especially encourage applications from leaders in computational and theoretical neuroscience whose work intersects with mathematical approaches including, but not limited to: dynamical systems, partial differential equations, algebraic geometry, statistics, information theory, stochastic processes, and applied and computational topology. This search is part of a multi-faculty hiring initiative in Computational and Theoretical Neuroscience at UT Austin -- including a position in Psychology and a joint position between Neuroscience and Computer Science, with the aim of building on existing strengths to create a Computational and Theoretical Neuroscience center. The successful candidate(s) will orchestrate an outstanding program of independent research and contribute to an environment of diversity in higher education through research, teaching, and service. The position will include full access to the Texas Advanced Computing Center supercomputers, and the option to join various multi-disciplinary advanced computing and neuroscience centers on campus. Austin is a vibrant university town with a large high-tech center, a legendary live-music culture, and has consistently ranked amongst the top five towns to live in the United States in various surveys. Qualified applicants with a Ph.D. or equivalent, appropriate post-graduate experience, and an exceptional research record will be considered for appointment. Applications are being reviewed from September 2014 onwards, until the position is filled. Please submit applications through academicjobsonline (https://academicjobsonline.org/ajo/jobs/4726) A complete application will include: 1. Statement of research interests. 2. Curriculum vita. 3. Letters of reference from at least 3 individuals (Assistant Professor) or 6 individuals (Associate or Full Professor). 4. A statement of teaching philosophy and experience, with evidence of teaching excellence (Assistant Professor). UT Austin is an equal opportunity employer committed to excellence through diversity. We strongly encourage applications from all qualified individuals including women and minorities. UT Austin is responsive to the needs of dual career couples and is dedicated to work-life balance through an array of family-friendly policies (http://www.utexas.edu/provost/policies/family/) For further inquires related to this search, please email Kathleen Pantalion (kpantalion(a)mail.clm.utexas.edu <mailto:kpantalion@mail.clm.utexas.edu>). **Full|Associate|Assistant Professor *in Computational and Theoretical Neuroscience, Departments of Neuroscience and Computer Science at the University of Texas at Austin * The Department of Neuroscience at the University of Texas at Austin seeks exceptional researchers in the field of computational and theoretical neuroscience for appointment at the levels of Full Professor to Assistant Professor. The position will be joint with the Department of Computer Science, thus we especially encourage applications from leaders in computational and theoretical neuroscience whose work intersects with computer science approaches including, but not limited to, information and complexity theory, computational modeling, computational geometry, statistics, machine learning, robotics, graphical models, stochastic processes, coding theory, and data mining. This search is part of a multi-faculty hiring initiative in Computational and Theoretical Neuroscience at UT Austin -- including a position in Psychology and a joint position between Neuroscience and Mathematics, with the aim of building on existing strengths to create a Computational and Theoretical Neuroscience center. The successful candidate(s) will orchestrate an outstanding program of independent research and contribute to an environment of diversity in higher education through research, teaching, and service. The position will include full access to the Texas Advanced Computing Center supercomputers, and the option to join various multi-disciplinary advanced computing and neuroscience centers on campus. Austin is a vibrant university town with a large high-tech center, a legendary live-music culture, and has consistently ranked amongst the top five towns to live in the United States in various surveys. Qualified applicants with a Ph.D. or equivalent, appropriate post-graduate experience if relevant, and an exceptional research record will be considered for appointment. The department prefers to receive applications online, beginning September 2014, onwards. To submit your application, please review the Checklist (http://services.cs.utexas.edu/recruit/faculty/frontmatter/checklist.html) and then go to My Application (http://services.cs.utexas.edu/recruit/faculty/applicant/) A complete application will include: 1. Statement of research interests. 2. Curriculum vita. 3. Letters of reference from at least 3 individuals (Assistant Professor) or 6 individuals (Associate or Full Professor). 4. For appointment as Assistant Professor, a statement of teaching philosophy and experience, with evidence of teaching excellence. UT Austin is an equal opportunity employer committed to excellence through diversity. We strongly encourage applications from all qualified individuals including women and minorities. UT Austin is responsive to the needs of dual career couples and is dedicated to work-life balance through an array of family-friendly policies (http://www.utexas.edu/provost/policies/family/) For further inquires related to this search, please email Kathleen Pantalion (kpantalion(a)mail.clm.utexas.edu <mailto:kpantalion@mail.clm.utexas.edu>).

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CFP: IEEE Computational Intelligence Magazine (CIM) Special Issue: “Computational Intelligence for Changing Environments"
by Dr Amir Hussain Oct. 2, 2014

Oct. 2, 2014

CALL FOR PAPERS IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE SPECIAL ISSUE ON "Computational Intelligence for Changing Environments" AIMS AND SCOPE: Over the past decade or so, computational intelligence techniques have been highly successful for solving big data challenges in changing environments. In particular, there has been growing interest in so called biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors. Examples include the hierarchy of the brain neocortex and neural circuits, which have resulted in biologically-inspired features for encoding, deep neural networks for classification, and spiking neural networks for general modelling. To ensure that these models are generalizable to unseen data, it is common to assume that the training and test data are independently sampled from an identical distribution, known as the sample i.i.d. assumption. In dynamic and non- stationary environments, the distribution of data changes over time, resulting in the phenomenon of ‘concept drift’ (also known as population drift or concept shift), which is a generalization of covariance shift in statistics. Over the last five years, transfer learning and multitask learning have been used to tackle this problem. Fundamental analyses using probably approximately correct (PAC) and Rademacher complexity frameworks have explained why appropriate incorporation of context and concept drift can improve generalizability in changing environments. It is possible to use human-level processing power to tackle concept drift in changing environments. Concept drift is a real-world problem, usually associated with online and concept learning, where the relationships between input data and target variables dynamically change over time. Traditional learning schemes do not adequately address this issue, either because they are offline or because they avoid dynamic learning. However, BIL seems to possess properties that would be helpful for solving concept drift problems in changing environments. Intuitively, the human capacity to deal with concept drift is innate to cognitive processes, and the learning problems susceptible to concept drift seem to share some of the dynamic demands placed on plastic neural areas in the brain. Using improved biological models in neural networks can provide insight into cognitive computational phenomena. However, a main outstanding issue in using computational intelligence for changing environments and domain adaptation is how to build complex networks, or how networks should be connected to the features, samples, and distribution drifts. Manual design and building of these networks are beyond current human capabilities. Recently, computational intelligence methods has been used to address concept drift in changing environments, with promising results. A Hebbian learning model has been used to handle random, as well as correlated, concept drift. Neural networks have been used for concept drift detection, and the influence of latent variables on concept drift in a neural network has been studied. In another study, a timing-dependent synapse model has been applied to concept drift. These works mainly apply biologically-plausible computational models to concept drift problems. Although these results are still in their infancy, they open up new possibilities to achieve brain-like intelligence for solving concept drift problems in changing environments. Taking the current state of research in computational intelligence for changing environments into account, the objective of this special issue is to collate this research to help unify the concepts and terminology of computational intelligence in changing environments, and to survey state-of-the-art computational intelligence methodologies and the key techniques investigated to date. Therefore, this special issue invites submissions on the most recent developments in computational intelligence for changing environments, algorithms and architectures, theoretical foundations, and representations, & their application to real-world problems. We also welcome timely surveys & review papers. TOPICS OF INTEREST include (but are not limited to): • Computational intelligence methodologies and implementation for changing environments •Transfer learning, Multitask learning, Domain adaption •Incremental Learning architectures, Unsupervised and semi-supervised learning architectures •Incremental Knowledge augmentation, Representation learning and disentangling •Incremental Adaptive Neuro-fuzzy systems •Incremental and single-pass data mining •Incremental Neural Clustering & Regression •Incremental Adaptive decision systems •Incremental Feature selection and reduction •Incremental Constructive Learning •Novelty detection in Incremental learning SUBMISSION PROCESS The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via: https://easychair.org/conferences/?conf=ieee-cim-cice2015 IMPORTANT DATES (for August 2015 Issue) 15th November, 2014: Submission of Manuscripts 15th January, 2015: Notification of Review Results 15th February, 2015: Submission of Revised Manuscripts 15th March, 2015: Submission of Final Manuscripts GUEST EDITORS Professor Amir Hussain, University of Stirling, Stirling FK9 4LA, Scotland, UK Email: ahu(a)cs.stir.ac.uk http://cs.stir.ac.uk/~ahu/ Professor Dacheng Tao, University of Technology, Sydney, 235 Jones Street, Ultimo, NSW 2007, Australia Email: dacheng.tao(a)uts.edu.au Professor Jonathan Wu University of Windsor, 401 Sunset Avenue, Windsor, ON, Canada Email: jwu(a)uwindsor.ca Professor Dongbin Zhao Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China E-mail: dongbin.zhao(a)gmail.com ----- A PDF copy of the CFP is attached with this email for forwarding to interested colleagues. It is also available for download from: http://www.cs.stir.ac.uk/~ahu/IEEE-CIM-CICE2015.pdf For more information on the IEEE CIM, see: http://cis.ieee.org/ieee-computational-intelligence-magazine.html -- The University of Stirling has been ranked in the top 12 of UK universities for graduate employment*. 94% of our 2012 graduates were in work and/or further study within six months of graduation. *The Telegraph The University of Stirling is a charity registered in Scotland, number SC 011159.

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Postdoctoral Researcher and Engineer Positions in Multi-scale Neural Modeling, OIST
by Kenji Doya Oct. 2, 2014

Oct. 2, 2014

Postdoctoral Researcher and Engineer Positions in Multi-scale Neural Modeling Neural Computation Unit Okinawa Institute of Science and Technology The Japanese Ministry of Education, Culture, Sports, Science and Technology started a new project “Brain/MINDS” that aims to create structural and anatomical maps of the marmoset brain using innovative technologies. Within this project, the group lead by Prof. Kenji Doya at Okinawa Institute of Science and Technology (OIST) will develop computational frameworks for constructing multi-scale neural models that link cellular and network dynamics to behavioral and cognitive functions by utilizing large-scale anatomical and physiological data sets. Positions are available for postdoctoral researchers and engineers to work on one or more of the following subtopics: 1) Estimation of cellular and synaptic parameters from connectome, imaging, and electrode recording data. 2) Automated reduction of conductance-based models to integrate-and-fire models, and spiking neural network models to population firing rate models. 3) Application and validation of the above methods with the data being produced by the project. Candidates should have Ph.D. or masters degree in mathematics, computer science or relevant areas and must have strong skills in programming, data analysis, and simulation. Experience in statistical machine learning and/or computational neuroscience is desired. OIST offers an excellent interdisciplinary research environment, including high-performance computing, in its campus overlooking beautiful coastline and coral reefs. Researchers will have opportunities to closely collaborate with leading neuroscientists participating in the project. Initial appointment will be from October 2014 to March 2015, with a possibility of renewal till March 2017. Competitive salary is offered based on the candidate’s experience. OIST is an equal opportunity, affirmative action employer and provides strong support for its staff and family members coming from abroad. Applicants should send a CV, publication list, statement of research, and contact information of three referees to Prof. Kenji Doya <doya(a)oist.jp>. ---- Kenji Doya <doya(a)oist.jp> Neural Computation Unit, Okinawa Institute of Science and Technology 1919-1 Tancha, Onna, Okinawa 904-0495, Japan Phone: +81-98-966-8594; Fax: +81-98-966-2891 https://groups.oist.jp/ncu

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Postdoctoral Position at NIH
by Carson C. Chow Oct. 2, 2014

Oct. 2, 2014

NIH Postdoctoral Fellowship: Mathematical Modeling of Food Intake, Food Reward and Habit Formation Position Type: Postdoctoral Position at the NIH Position Title: Mathematical Modeling of Food Intake, Food Reward and Habit Formation Position Description: The successful candidate will develop mathematical models of the neurobiology underlying the regulation of food intake, food reward and habit formation in collaboration with Dr. Kevin Hall and Dr. Carson Chow in the Laboratory of Biological Modeling, NIDDK, NIH. Applicants should possess a Ph.D. degree in Computational Neuroscience, Applied Mathematics, Engineering, Physics, or a related field. The successful candidate will have strong quantitative and mathematical abilities and an interest in studying the regulation of food intake, with a specific emphasis on food reward and habit formation. Salary will be commensurate with experience according to NIH guidelines. Please send your CV and a list of 3 references to: Kevin Hall, Ph. D. Laboratory of Biological Modeling National Institute of Diabetes & Digestive & Kidney Diseases National Institutes of Health 12A South Drive, Room 4007 Bethesda, MD 20892-5621 Email: kevinh(a)niddk.nih.gov Employer Name: Laboratory of Biological Modeling at the National Institute of Diabetes & Digestive & Kidney Diseases Position Location: Bethesda, Maryland. The NIH is dedicated to building a diverse community in its training and employment programs.

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MIT - BCS/PILM Tenure-Track Faculty Search
by Kate White Oct. 1, 2014

Oct. 1, 2014

[s2_r_g] [cid:image002.png@01CFDBBF.FDE34440] [cid:image003.png@01CFDBBF.FDE34440] The Department of Brain & Cognitive Sciences (BCS) (http://bcs.mit.edu) and The Picower Institute for Learning & Memory at MIT (http://picower.mit.edu/) are looking to hire up to three (3) tenure-track faculty at the assistant professor level who work in one or more of the following three (3) areas: i) Computational approaches to intelligence, cognition or neuroscience; an experimental component to the candidate's research would be viewed as a positive but is not necessary. An affiliation with Electrical Engineering and Computer Science, the Computer Science and Artificial Intelligence Laboratory (CSAIL), or other allied departments is possible. ii) Molecular & cellular: The Picower Institute is searching for a candidate studying development, function or plasticity of neuronal circuits at the cellular, circuit, and/or systems levels using a multi-faceted approach combining different methodologies and levels of analysis. Candidates with strong cellular/molecular training who are studying development of brain circuits or using stem cell technologies are particularly encouraged to apply. iii) Human cognition and/or cognitive neuroscience using behavioral methods, especially in the areas of language and/or cognitive development OR using fMRI/neuroscience methods. 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 research. 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/4202. Please state research area in cover letter. To help direct the application, applicants should indicate which of the three areas listed above is their main research area by answering the mandatory questions included in the application. In addition, please arrange to have three letters of recommendation submitted online. Review of applications will begin on November 1, 2014. MIT is an affirmative action employer, and we encourage applications from women and underrepresented minorities.

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RLDM2015: The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making
by Yael Niv Oct. 1, 2014

Oct. 1, 2014

====================================================== The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2015) www.rldm.org June 7-10, University of Alberta, Edmonton, Alberta, Canada ====================================================== Over the last few decades, reinforcement learning and decision making have been the focus of an incredible wealth of research spanning a wide variety of fields including psychology, artificial intelligence, machine learning, operations research, control theory, animal and human neuroscience, economics and ethology. Key to many developments in the field has been interdisciplinary sharing of ideas and findings. The goal of RLDM is to provide a platform for communication among all researchers interested in "learning and decision making over time to achieve a goal". The meeting is characterized by the multidisciplinarity of the presenters and attendees, with cross-disciplinary conversations and teaching and learning being central objectives along with the dissemination of novel theoretical and experimental results. The first meeting, RLDM2013, was a huge success, with over 300 attendees. The second meeting will be single-track, and will consist of a mixture of invited and contributed talks, tutorials, and poster sessions. See below for a list of confirmed speakers. Submissions of 1-4 page extended abstracts describing original research, coming from any discipline or disciplines, describing empirical results from human, animal, robot or artificial agent experiments, and/or theoretical work, simulations and modeling will be peer-reviewed for quality and interdisciplinary communication. This is an abstract-based meeting, with no published conference proceedings. As such, work that is intended for, or has been submitted to, other conferences or journals is also welcome, provided that the intent of communication to other disciplines is clear. More information will be posted at www.rldm.org Important dates: Meeting: 7-10 June 2015 (tutorials on 7th, meeting proper 8-10th) Submissions open: 15 December 2014 Submissions close: 7 February 2015, midnight EST Notification of acceptance: 21 March, 2015 (foreigners needing to request visas can fast-track poster acceptance by request to program chairs) Early registration: 21 April 2015 To ensure that you receive future announcements about RLDM2015 please join our mailing list at http://tinyurl.com/RLDMlist (you must log in to google to see the "join list" button, and choose 'all emails' in the options). Please circulate widely and encourage your students and postdocs to attend. CONFIRMED SPEAKERS: Timothy Behrens Emma Brunskill Alison Gopnik Ann Graybiel Charles Isbell Sridhar Mahadevan David Parkes Elizabeth Phelps Alexandre Pouget Peter Stone Peter Thall Andrea Thomaz Claire Tomlin Ben Van Roy Ilana Witten PROGRAM CHAIRS Joelle Pineau Peter Dayan LOCAL CHAIR Richard Sutton GENERAL CHAIR Yael Niv EXECUTIVE COMMITTEE: Peter Dayan (UCL) Susan Murphy (U Michigan) Yael Niv (Princeton) Nicholas Roy (MIT) Satinder Singh (U Michigan) Richard Sutton (Alberta)

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