February 2015 - Comp-neuro - lists.cnsorg.org

NEURAL COMPUTATION - April, 2014
by Terry Sejnowski April 24, 2016

April 24, 2016

Neural Computation - Contents -- Volume 26, Number 4 - April 1, 2014 Available online for download now: http://www.mitpressjournals.org/toc/neco/26/4 ----- Article Parametric Inference in the Large Data Limit Using Maximally Informative Models Justin Block Kinney, Gurinder Singh Atwal Letters Input Statistics and Hebbian Crosstalk Effects Anca R. Radulescu Learning Nonlinear Regularities in Natural Images by Modeling the Outer Product of Image Intensities Peng Qi, Xiaolin Hu Dissociable Forms of Repetition Priming: A Computational Model Kirill Makukhin, Scott Bolland Refined Rademacher Chaos Complexity Bounds With Applications to the Multi-Kernel Learning Problem Yunwen Lei, Lixin Ding Large Margin Low Rank Tensor Analysis Guoqiang Zhong, Mohamed Cheriet Large-scale Linear RankSVM Ching-Pei Lee, Chih-Jen Lin ------------ ON-LINE -- http://www.mitpressjournals.org/neuralcomp SUBSCRIPTIONS - 2014 - VOLUME 26 - 12 ISSUES USA Others Electronic Only Student/Retired $70 $193 $65 Individual $124 $187 $115 Institution $1,035 $1,098 $926 Canada: Add 5% GST MIT Press Journals, 238 Main Street, Suite 500, Cambridge, MA 02142-9902 Tel: (617) 253-2889 FAX: (617) 577-1545 journals-orders(a)mit.edu ------------

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v5.7 of NeuroMorpho.Org released on 30 May, 2014
by NeuroMorpho Administrator March 10, 2016

March 10, 2016

Version 5.7 of NeuroMorpho.Org was released on 30 May, 2014. The release included 29 new data sets (1341 reconstructions), including from three new animal species (dragonfly, moth, and sheep). The database now contains 11,335 reconstructions from 144 contributing labs. More than 3.2 million reconstructions were downloaded in over 130,000 unique visits from 146 countries. Please visit the What's new<http://neuromorpho.org/neuroMorpho/WIN.jsp> page for details on data included in this release, including new species strains, brain regions, cell types, and experimental conditions. The Acknowledgements <http://neuromorpho.org/neuroMorpho/acknowl.jsp> include details on contributing labs, and the About<http://neuromorpho.org/neuroMorpho/about.jsp> page provides an updated overview of the repository content. A new user-friendly functionality, OntoSearch<http://neuromorpho.org/neuroMorpho/OntoSearch.jsp>, was introduced in this release, enabling more powerful searches of species and strains with automated synonym translation, taxonomical relations, and keyword auto-completion. The literature coverage database was also updated to include publications through March 2014. We are continuously grateful to all the data owners who freely share their data with the community. We always appreciate any and all feedback and comments. Sincerely, The NeuroMorpho.Org team -- Ruchi Parekh, Ph.D. NeuroMorpho.Org Project Lead Postdoctoral Research Fellow Center for Neural Informatics, Structures, and Plasticity Krasnow Institute for Advanced Study MS2A1, George Mason University Fairfax, VA 22030 (USA) Ph - +1-703-993-4382<tel:%2B1-703-993-4382> www.neuromorpho.org<http://www.neuromorpho.org/> nmadmin(a)gmu.edu<mailto:nmadmin@gmu.edu>?

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PhD positions in computational neuroscience
by Dan Goodman Nov. 2, 2015

Nov. 2, 2015

One or more PhD positions in computational neuroscience are available in the Neural Reckoning Group of Dan Goodman at the Department of Electrical and Electronic Engineering, Imperial College London. I am interested in supervising students with a strong mathematical, computational or neuroscience background. Projects could be carried out in several possible areas, including neuroinformatics (i.e simulation and analysis of neural data) or sensory neuroscience (e.g. spiking neural models in the auditory system). For more information about the group, including some suggestions for research topics, see http://neural-reckoning.org/openings.html. In addition to working within the group, studying at Imperial College provides excellent opportunities for interacting with other theoretical and experimental researchers, both at Imperial (recently ranked 2nd in the world in the QS world university rankings) and in the many neuroscience groups in London. Applicants should initially send me a brief CV and cover letter with a description of research interests or a proposed project, and will eventually have to formally apply through the standard Imperial College mechanism (for more information, see http://www3.imperial.ac.uk/electricalengineering/courses/phd) Funding is available for at least one student, and there are several competitive funding schemes at Imperial which would provide funding for additional students. Also, please forward this message on to any of your students or colleagues with students that you think may be interested. Dan Goodman

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Neuroscience Graduate Group at the University of Pennsylvania
by Joshua Gold Oct. 8, 2015

Oct. 8, 2015

The Neuroscience Graduate Group (NGG) at the University of Pennsylvania is a collaborative and interdisciplinary PhD program that is currently accepting applications for matriculation in the fall of 2015. We are particularly interested in attracting exceptional applicants with backgrounds in Neuroscience, Computer Science, Cognitive Science, Mathematics, Engineering, Physics, and/or Psychology and are interested in the broadly defined field of Computational Neuroscience. We have a robust and growing community of scholars in this field, including: Vijay Balasubramanian, Department of Physics Danielle S Bassett, Department of Bioengineering David H Brainard, Department of Psychology Maja Bucan, Department of Genetics Johannes Burge, Department of Psychology Alice S Chen-Plotkin, Department of Neurology Brian Chow, Department of Bioengineering Yale E Cohen, Department of Otorhinolaryngology Diego Contreras, Department of Neuroscience Christopher M Fang-Yen, Department of Bioengineering Maria Neimark Geffen, Department of Otorhinolaryngology Joshua I Gold, Department of Neuroscience John B Hogenesch, Department of Pharmacology Joseph W Kable, Department of Psychology Michael J Kahana, Department of Psychology Max Kelz, Department of Anesthesiology Brian Litt, Departments of Neurology and Bioengineering Joel D Mainland, Department of Neuroscience Javier F Medina, Department of Psychology Nicole C Rust, Department of Psychology Robert G Smith, Department of Neuroscience Alan A Stocker, Department of Psychology Penn also has strong programs in Statistics, Computer Science, Engineering, and Psychology with faculty actively involved in machine learning, decision making, perception, and computational learning theory. NGG students are encouraged to pursue collaborations and interactions with these groups. The application deadline for the NGG is December 1, 2014. For more details please see our website: http://www.med.upenn.edu/ngg/app_info.shtml ----- Joshua Gold University of Pennsylvania Department of Neuroscience 116 Johnson Pavilion 3610 Hamilton Walk Philadelphia, PA 19104-6074 office phone: 215-746-0028 lab phone: 215-746-0168 e-mail: jigold(a)mail.med.upenn.edu http://www.med.upenn.edu/goldlab

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Gatsby Computational Neuroscience Unit, PhD Programme
by Peter Dayan Sept. 24, 2015

Sept. 24, 2015

Gatsby Computational Neuroscience Unit, University College London 4 year PhD Programme The Gatsby Unit is a centre for theoretical neuroscience and machine learning, focusing on unsupervised, semi-supervised and reinforcement learning, neural dynamics, population coding, Bayesian and nonparametric statistics, kernel methods, and applications of these to the analysis of perceptual processing, neural data, machine vision and bioinformatics. It provides a unique opportunity for a critical mass of theoreticians to interact closely with each other, and with other world-class research groups in related departments at UCL, including the Sainsbury Wellcome Centre for Neural Circuits and Behaviour, with which we will shortly share a building, and Biosciences, Computer Science, Functional Imaging, Physics, Physiology, Psychology, Neurology, Ophthalmology and Statistics, the cross-faculty Centre for Computational Statistics and Machine Learning, and also with other UK and overseas universities. The Unit has openings for exceptional PhD candidates. Applicants should have a strong analytical background, a keen interest in neuroscience and/or machine learning and a relevant first degree, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics. The PhD programme lasts four years, including a first year of intensive instruction in techniques and research in theoretical neuroscience and machine learning. All students are fully funded, regardless of nationality. The Unit also welcomes applications from students with pre-secured funding or who are currently soliciting other scholarship/studentships. We will review applications as soon as they are complete (including a CV, statement of research interests and letters from three referees) until the positions are filled. Early application is thus advised. Full details of our programme, and how to apply, are available at: http://www.gatsby.ucl.ac.uk/teaching/phd For further details of research interests please see http://www.gatsby.ucl.ac.uk/research.html and the individual faculty webpages at http://www.gatsby.ucl.ac.uk/members.html

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PhD studentships in computational neuroscience and machine learning
by Steuber, Volker Aug. 28, 2015

Aug. 28, 2015

Applications are invited for PhD studentships in the Biocomputation Research Group at the Science and Technology Research Institute at the University of Hertfordshire. PhD projects involve the development of computer simulations of neurons and neuronal networks to study information processing in the brain, and/or the application of machine learning techniques to analyse electrophysiological data. More details about our research and a list of potential projects can be found on our homepage (http://homepages.herts.ac.uk/~comqvs/research.html) Applicants should have good computational and numerical skills and an excellent first degree in computer science, biology, maths, physics, neuroscience, or a related discipline. Previous experience in neuroscience is not required but would be an advantage. Successful candidates are eligible for a research studentship award from the University (approximately GBP 13,800 per annum bursary plus the payment of the standard UK student fees). Applicants from outside the UK or EU are eligible, but will have to pay half of the overseas fees out of their bursary. Research in Computer Science at the University of Hertfordshire has been recognized as excellent by the latest Research Assessment Exercise, with 55% of the research submitted being rated as world leading or internationally excellent. The Science and Technology Research Institute provides a very stimulating environment, offering a large number of specialized and interdisciplinary seminars as well as general training opportunities. The University of Hertfordshire is situated in Hatfield, in the green belt just north of London. Please contact Dr Volker Steuber (v.steuber @ herts.ac.uk) for informal enquiries. Application forms can be obtained from Mrs Lorraine Nicholls, Research Student Administrator, STRI, University of Hertfordshire, College Lane, Hatfield, Herts, AL10 9AB, Tel: 01707 286083, l.nicholls @ herts.ac.uk. The short-listing process will begin on 9 June 2014.

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postdoc positions - Gatsby Computational Neuroscience Unit
by Peter Latham April 21, 2015

April 21, 2015

The Gatsby Computational Neuroscience Unit invites applications for one or more postdoctoral training fellowships in theoretical neuroscience, including population coding, learning, neural neural dynamics, the interpretation of neural data, perceptual processing, and neuromodulation. The position is available for an initial period of two years, and may be renewed to a maximum of three years. The closing date for applications is 15 March, 2015. Interviews will be held in May 2015. To find out more about the job, and to apply, see http://www.gatsby.ucl.ac.uk/vacancies/TN%20Postdoc2015.html For further details of our research, see http://www.gatsby.ucl.ac.uk/research.html

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Reminder: MBL Methods in Computational Neuroscience Course 2015: applications due March 5
by Mark Goldman Feb. 28, 2015

Feb. 28, 2015

Applications are open for the Methods in Computational Neuroscience course at the Marine Biology Laboratory in Woods Hole, MA. The course will run from July 29 to August 26, 2014, and the online application form can be found at: http://ws2.mbl.edu/studentapp/studentapp.asp?CourseID=MCN. The course application deadline is March 5. The course covers a range of topics in computational neuroscience including neuronal biophysics, neural coding & information processing, circuit dynamics, learning & memory, motor control, and cognitive processing & disease. In addition, numerous tutorials and problem sets will cover a broad range of computational and mathematical modeling methods. The course strongly emphasizes the collaboration between theory and experiment in solving neuroscience problems, and lectures will be given by a mixture of theorists and experimentalists. The final weeks of the course are primarily reserved for development and work on projects that students design in collaboration with the resident faculty. Further information can be found on the MCN website: http://www.mbl.edu/mcn/ 2015 Course Directors: Michale Fee, MIT Mark Goldman, UC Davis 2015 Confirmed Faculty: Larry Abbott, Columbia University Steve Baccus, Stanford University Jeff Beck, Duke University William Bialek, Princeton University Dmitri Chklovskii, HHMI Janelia Farm Peter Dayan, University College London Bard Ermentrout, University of Pittsburgh Adrienne Fairhall, University of Washington Ila Fiete, UT Austin Loren Frank, UCSF Michael Frank, Brown University Surya Ganguli, Stanford University John Huguenard, Stanford University David Kleinfeld, UC San Diego Nancy Kopell, Boston University John Lisman, Brandeis University Eve Marder, Brandeis University Bartlett Mel, University of Southern California Jonathan Pillow, Princeton University Maneesh Sahani, University College London Terry Sejnowski, Salk Institute Michael Shadlen, Columbia University Josh Shaevitz, Princeton University Sara Solla, Northwestern University Haim Sompolinsky, Hebrew University David Tank, Princeton University Josh Tenenbaum, MIT Srini Turaga, Janelia Farm Xiao-Jing Wang, NYU Daniel Wolpert, Cambridge University Ryohei Yasuda, Duke University

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a neural architecture for autonomous spatial and object attention, invariant object category learning and recognition, and Where’s Waldo search with freely moving eyes
by Stephen Grossberg Feb. 27, 2015

Feb. 27, 2015

The following series of articles progressively develop a neural architecture for autonomous spatial and object attention, invariant object category learning and recognition, and Where’s Waldo search with freely moving eyes: ARTSCAN View-invariant object category learning and recognition with freely moving eyes in a 2D scene Fazl, A., Grossberg, S., and Mingolla, E. (2009). View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds. Cognitive Psychology, 58, 1-48. http://cns.bu.edu/~steve/FazGroMin2008.pdf pARTSCAN View-, position-, and size-invariant object category learning and recognition with freely moving eyes in a 2D scene Cao, Y., Grossberg, S., and Markowitz, J. (2011). How does the brain rapidly learn and reorganize view- and positionally-invariant object representations in inferior temporal cortex? Neural Networks, 24, 1050-1061. http://cns.bu.edu/~steve/NN2853.pdf dARTSCAN ARTSCAN with transient and sustained distributed attention and prefrontal priming in a 2D scene Foley, N.C., Grossberg, S. and Mingolla, E. (2012). Neural dynamics of object-based multifocal visual spatial attention and priming: Object cueing, useful-field-of-view, and crowding. Cognitive Psychology, 65, 77-117. http://cns.bu.edu/~steve/FolGroMin2012.pdf ARTSCAN SEARCH Solution of Where’s Waldo Problem: finding a valued object in a 2D scene Chang, H.-C., Grossberg, S., and Cao, Y. (2014) Where's Waldo? How perceptual cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene. Frontiers in Integrative Neuroscience, doi: 10.3389/fnint.2014.0043. http://journal.frontiersin.org/Journal/10.3389/fnint.2014.00043/full 3D ARTSCAN ARTSCAN of a 3D scene whose 3D perceptual representations remain stable as the eyes move Grossberg, S., Srinivasan, K., and Yazdanbakhsh, A. (2014). Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements. Frontiers in Psychology: Perception Science, doi: 10.3389/fpsyg.2014.01457. http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.01457/full Stephen Grossberg Wang Professor of Cognitive and Neural Systems Professor of Mathematics, Psychology, and Biomedical Engineering Director, Center for Adaptive Systems http://www.cns.bu.edu/about/cas.html http://cns.bu.edu/~steve steve(a)bu.edu

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ECMLPKDD 2015 : Call for Papers, Tutorials and Workshops
by ECMLPKDD 2015 Feb. 27, 2015

Feb. 27, 2015

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) will take place in Porto, Portugal, from September 7th to 11th, 2015 (http://www.ecmlpkdd2015.org) This event is the leading European scientific event on machine learning and data mining and builds upon a very successful series of 25 ECML and 18 PKDD conferences, which have been jointly organized for the past 14 years. ECMLPKDD 2015 will host three tracks, tutorials and a set of workshops. Therefore, we invite all researchers and practitioners from different communities to submit papers and/or present tutorial and workshop proposals. ************************* CALL FOR PAPERS ************************* JOURNAL TRACK ********************* Articles for this track are submitted all year long directly to either Machine Learning or Data Mining and Knowledge Discovery, and are reviewed like regular journal articles. Accepted articles appear in full in the journal and the authors are given a presentation slot at the conference. Articles deemed insufficiently mature for journal publication may be accepted for inclusion in the proceedings. Submissions to the journal track will be managed by the Guest Editorial Board. Paper Submission: Cut-off dates for the bi-weekly batches are 18 Jan, 1 Feb, 15 Fev, 1 Mar, 15 Mar, 29 Mar, 12 Apr, 26 Apr of 2015 Web Page: http://www.ecmlpkdd2015.org/submission/journal-track RESEARCH PROCEEDINGS TRACK ******************************************* The research proceedings track, which is organized in the traditional way. Accepted papers will be published in the Lecture Notes in Artificial Intelligence (LNCS/LNAI) of Springer, after reviewing by the programme committee. Abstract Submission Deadline: March 26, 2015 Paper Submission Deadline: April 2, 2015 Paper Acceptance Notification: June 1, 2015 Paper Camera Ready Submission: June 15, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/research-proceedings-track INDUSTRIAL, GOVERNMENTAL & NON-GOVERNMENTAL PROCEEDINGS TRACK **************************************************************************** ************************** The NEW industrial, governmental & non-governmental (NGO) proceedings track is independent and distinct from the Research Track. Submissions to this track should solve real-world problems and focus on engineering systems, applications, and challenges. Accepted papers will be published in the Lecture Notes in Artificial Intelligence (LNCS/LNAI) of Springer, after reviewing by the programme committee. Abstract Submission Deadline: March 26, 2015 Paper Submission Deadline: April 2, 2015 Paper Acceptance Notification: June 1, 2015 Paper Camera Ready Submission: June 15, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/industrial-proceedings-track ***************************************************************** CALL FOR TUTORIAL AND WORKSHOP PROPOSALS ***************************************************************** TUTORIALS ************** The tutorials are intended to provide a comprehensive introduction to established or emerging research topics of interest for the machine learning and the data mining community. These topics include related research fields or applications. The ideal tutorial should attract a wide audience. It should be broad enough to provide a basic introduction to the chosen research area, but it should also cover the most important topics in depth. We welcome half day workshop proposals. Proposal Deadline: March 2, 2015 Proposal Acceptance Notification: March 23, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/call-for-tutorials WORKSHOPS **************** The workshops will be on relevant and current topics in Machine Learning and Data Mining. The scope of the proposal should be consistent with the conference themes as described in the ECMLPKDD 2015 Call for Papers (http://www.ecmlpkdd2015.org/submission) Interdisciplinary workshops that bring together researchers and practitioners from different communities are especially welcome. We encourage workshops that bridge the gap between theoretical advances and important and/or innovative applications of machine learning and data mining. We welcome both full and half day workshop proposals. Proposal Deadline: March 2, 2015 Proposal Acceptance Notification: March 23, 2015 Workshop Websites and Call for Papers Online: March 27, 2015 Workshop Proceedings (Camera-ready): August 3, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/call-for-workshop-proposals Hope to see you all soon in Porto, Portugal!!! The publicity chairs of the ECMLPKDD 2015, Carlos Abreu Ferreira Ricardo Campos --- Este e-mail foi verificado em termos de vírus pelo software antivírus Avast. http://www.avast.com

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