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

PhD student position in theoretical/ computational neuroscience available in Mannheim, Germany

by Seeger, Simone

A PhD student position in theoretical/ computational neuroscience (German payscale 0.67x E13 TV-L) is available for 3 years initially, starting January 2015 or later, at the Dept. of Theoretical Neuroscience (Head: Prof. Daniel Durstewitz), Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health Mannheim, Heidelberg University. The lab focuses on highly data-driven computational models of prefrontal cortical and hippocampal network dynamics underlying higher cognitive function in health and psychiatric disease. Our approach is to estimate (in statistically principled ways) single cell and network models from slice and in-vivo electrophysiological recordings generated in the lab, and then apply these for understanding network function and dysfunction in experimental data (multiple single-unit, behavior) in psychiatrically relevant genetic animal models. Applicants should have a strong mathematical background (dynamical systems and/or statistics / stochastic processes), good programming skills (Matlab, C++), and a keen interest in biological and neural systems. Please send informal inquiries and application documents (cover letter including a brief description of research interests, CV and contact details of two personal references) to Dr. loreen.hertaeg(a)zi-mannheim.de<mailto:loreen.hertaeg@zi-mannheim.de>. *** Simone Seeger, M.A. Administration Bernstein Center for Computational Neuroscience Zentralinstitut für Seelische Gesundheit Postfach 12 21 20, 68072 Mannheim J5, 68159 Mannheim Telefon: 0621/1703-1326 oder 06221/54-8310 Fax: 0621/1703-2915 E-Mail: Simone.Seeger(a)zi-mannheim.de<mailto:Simone.Seeger@zi-mannheim.de> Internet: http://www.bccn-heidelberg-mannheim.de<http://www.bccn-heidelberg-mannheim.de/>

Nov. 4, 2014

CfP : TWELFTH INTERNATIONAL CONFERENCE ON MULTIPLE CLASSIFIER SYSTEMS

by Dr. Schwenker

---- PLEASE, APOLOGIZE MULTIPLE COPIES ---- ****************************************** ** MCS 2015 Preliminary Call for Papers ** ****************************************** *****Paper Submission: JANUARY 30, 2015***** ********************************************************************** TWELFTH INTERNATIONAL CONFERENCE ON MULTIPLE CLASSIFIER SYSTEMS Reisensburg Castle (Günzburg, Germany), Ulm University, June 29 - July 1, 2015 http://mcs.diee.unica.it ********************************************************************** MCS 2015 is the twelfth edition of the well-established series of meetings providing the leading international forum for the discussion of issues in multiple classifier systems and ensemble methods. The aim of the workshop is to bring together researchers from diverse communities concerned with this topic, including pattern recognition, machine learning, neural networks, data mining and statistics. MCS 2015 will be held on June 29 - July 1, 2015, at Reisensburg Castle (Günzburg, Germany) research center of the Ulm University. Updated information:http://mcs.diee.unica.it Best regards, MCS 2015 Co-Chairs Friedhelm Schwenker Joseph Kittler Fabio Roli

Nov. 3, 2014

PhD in “Fragmented neural processing in Parkinson's disease” at University of Manchester, Faculty of Life Sciences

by Mark Humphries

PhD in “Fragmented neural processing in Parkinson's disease”. Applications are invited for this fully-funded PhD position (fees and salary) in the lab of Dr Mark Humphries in the Faculty of Life Science, University of Manchester Deadline: 12th December 2014 Start date: September 2015 Duration: 3 or 4 years, depending on qualifications Eligibility: Candidates must be nationals of the UK or other EU country This project will pursue the hypothesis that chronic loss of dopamine causes the fragmentation of striatal processing of cortical input. The striatum supports neural populations coding for different actions, forming the basis for action selection. Animal model data has suggested that chronic dopamine loss causes the fragmentation of these populations, mixing together previously well-defined populations (Cho et al 2002 J Comp Neurol). To test this hypothesis, the candidate will develop our existing large-scale model of the striatal network (Humphries et al, 2010, PLoS Comput Biol) and combine it with a newly-developed model of dopamine-dependent plasticity at cortical synapses onto striatal neurons (Gurney et al, PLoS Biol, in press). The project will then test aspects of the hypothesis: Does fragmentation occur via changes to network excitability or to plastic change to network inputs? Does fragmentation cause the breakdown of action selection and therefore the observed motor symptoms? And why is 80% loss of dopamine necessary for this to occur? For more details of the project please visit: http://www.ls.manchester.ac.uk/phdprogrammes/projectsavailable/project/?id=… For details of the application and interview process see: http://www.mhs.manchester.ac.uk/postgraduate/mhs-graduate-school/mrcdtp/stu… Informal enquiries: mark.humphries(a)manchester.ac.uk<mailto:mark.humphries@manchester.ac.uk> Dr Mark Humphries MRC Senior non-Clinical Research Fellow AV Hill Building Faculty of Life Sciences University of Manchester http://www.systemsneurophysiologylab.ls.manchester.ac.uk/

Nov. 3, 2014

Assistant or Associate Professor, Data Analytics

by Guerra, Erick J.

Assistant or Associate Professor, Health or Biomedical Data Analytics Rowan University is a comprehensive state-designated research institution with approximately 15,000 students. Its main campus is located in Glassboro, N.J., 20-miles southeast of Philadelphia, and it has a branch campus and medical school in Camden and a second medical school in nearby Stratford. Rowan is the second university in the country to offer M.D. and D.O. medical-degree granting programs. The School of Osteopathic Medicine has a faculty practice plan that serves approximately 200,000 patients in Southern New Jersey. The institution is also home to the South Jersey Technology Park, which fosters the translation of applied research into commercial products and processes. Its business incubator also supports that mission. The University boasts seven colleges–Rohrer College of Business and colleges of Communication and Creative Arts, Education, Engineering, Humanities and Social Sciences, Performing Arts, and Science and Mathematics. The Department of Biomedical & Translational Sciences in the School of Biomedical Science & Health Professions invites applications for a tenure-track Associate or Assistant Professor position in the field of Health or Biomedical Data Analytics starting September 1, 2015. A Ph.D. in Data Analytics or related field is required. The successful candidate will assume a leadership role in the newly established Data Analytics programand is expected to develop an externally-funded research program in Health or Biomedical Data Analytics that involves undergraduate and/or graduate (MS) students. Applications are encouraged from candidates with a successful record of research in one of the following areas: medical informatics, genomic or proteomic data mining, systems biology, translational bioinformatics. Candidates with a background in industry settings are also encouraged to apply. The Department of Biomedical & Translational Sciences is the home for new interdisciplinary biomedical initiatives and welcomes its first cohort of students pursuing a BS in Translational Biomedical Sciences in Fall 2014. Many faculty-members in the Department of Biomedical & Translational Sciences have joint appointments with the College of Science and Mathematics, and there are opportunities to collaborate with both the allopathic and osteopathic medical schools at Rowan University. The College of Science & Mathematics currently offers related BS degrees in Biological Sciences, Biochemistry, Biophysics, and Bioinformatics, in addition to MS degrees in Pharmaceutical Science and Bioinformatics. The Master of Science in Data Analytics with a specialization in Health Data Analytics is housed in the College of Science & Mathematics with stakeholders spanning several colleges and schools at Rowan University. The successful candidate will develop and implement new interdisciplinary curricula in both Data Analytics and Translational Biomedical Sciences. Teaching responsibilities will include courses in Translational Biomedical Sciences, graduate courses in Data Analytics, and potentially other discipline specific courses falling under these programs. The application must be submitted through our online application system (http://rowanuniversity.hodesiq.com/jobs/assistant-associate-professor-healt…) as a single file (PDF format) containing a curriculum vitae, a detailed description of research interests and plans, a statement of teaching philosophy, and graduate transcripts (copies acceptable). Three letters of recommendation should be sent via email directly to Ms. Shelly Klink (klink(a)rowan.edu<mailto:klink@rowan.edu>). The application deadline is November 15, 2014. Rowan University values diversity and is committed to equal opportunity in employment. Candidates must demonstrate authorization to work in the United States. All positions are contingent upon budget appropriations.

Nov. 1, 2014

Call for Papers - Neural Networks special issue on Big Data

by Asim Roy

Apologies for cross posting. Special Issue on Neural Network Learning in Big Data [Special Issue on Neural Network Learning in Big Data] Neural Networks Special Issue: Neural Network Learning in Big Data Big data is much more than storage of and access to data. Analytics plays an important role in making sense of that data and exploiting its value. But learning from big data has become a significant challenge and requires development of new types of algorithms. Most machine learning algorithms encounter theoretical challenges in scaling up to big data. Plus there are challenges of high dimensionality, velocity and variety for all types of machine learning algorithms. The neural network field has historically focused on algorithms that learn in an online, incremental mode without requiring in-memory access to huge amounts of data. The brain is arguably the best and most elegant big data processor and is the inspiration for neural network learning methods. Neural network type of learning is not only ideal for streaming data (as in the Industrial Internet or the Internet of Things), but could also be used for stored big data. For stored big data, neural network algorithms can learn from all of the data instead of from samples of the data. And the same is true for streaming data where not all of the data is actually stored. In general, online, incremental learning algorithms are less vulnerable to size of the data. Neural network algorithms, in particular, can take advantage of massively parallel (brain-like) computations, which use very simple processors, that other machine learning technologies cannot. Specialized neuromorphic hardware, originally meant for large-scale brain simulations, is becoming available to implement these algorithms in a massively parallel fashion. Neural network algorithms, therefore, can deliver very fast and efficient real-time learning through the use of hardware and this could be particularly useful for streaming data in the Industrial Internet. Neural network technologies thus can become significant components of big data analytics platforms and this special issue will begin that journey with big data. For this special issue of Neural Networks, we invite papers that address many of the challenges of learning from big data. In particular, we are interested in papers on efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of online learning to solve real-world big data problems (e.g. health care, transportation, and electric power and energy management). RECOMMENDED TOPICS: Topics of interest include, but are not limited to: 1. Autonomous, online, incremental learning - theory, algorithms and applications in big data 2. High dimensional data, feature selection, feature transformation - theory, algorithms and applications for big data 3. Scalable neural network algorithms for big data 4. Neural network learning algorithms for high-velocity streaming data 5. Deep neural network learning 6. Neuromorphic hardware for scalable neural network learning 7. Big data analytics using neural networks in healthcare/medical applications 8. Big data analytics using neural networks in electric power and energy systems 9. Big data analytics using neural networks in large sensor networks 10.Big data and neural network learning in computational biology and bioinformatics SUBMISSION PROCEDURE: Prospective authors should visit http://ees.elsevier.com/neunet/ for information on paper submission. During the submission process, there will be steps to designate the submission to this special issue. However, please indicate on the first page of the manuscript that the manuscript is intended for the Special Issue: Neural Network Learning in Big Data. Manuscripts will be peer reviewed according to Neural Networks guidelines. Manuscript submission due: December 15, 2014 First review completed: March 1, 2015 Revised manuscript due: April 1, 2015 Second review completed, final decisions to authors: April 15, 2015 Final manuscript due: April 30, 2015 GUEST EDITORS: Asim Roy, Arizona State University, USA (asim.roy(a)asu.edu<mailto:asim.roy@asu.edu>) (lead guest editor) Kumar Venayagamoorthy, Clemson University, USA (gkumar(a)ieee.org<mailto:gvenaya@clemson.edu>) Nikola Kasabov, Auckland University of Technology, New Zealand (nkasabov(a)aut.ac.nz<mailto:nkasabov@aut.ac.nz>) Irwin King, Chinese University of Hong Kong, China (irwinking(a)gmail.com<mailto:irwinking@gmail.com>)

Nov. 1, 2014