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computational neuroscience positions at Sandia National Laboratories
by Chance, Frances S. Nov. 5, 2014

Nov. 5, 2014

Colleagues, We are seeking computational neuroscientists to join our interdisciplinary research program in understanding cognitive function at the neural circuit level. Example research topics of interest include understanding neural circuit function, neural basis of perception, decision-making, learning and memory, and the development and validation of large-scale high-fidelity neural simulations. Multiple positions for varying levels of experience are available. To apply, visit http://www.sandia.gov/careers/ and click "View All Jobs". Type the indicated Job ID into the Keywords box to find the appropriate posting. For questions please contact Frances Chance (fschanc(a)sandia.gov<mailto:fschanc@sandia.gov>), Brad Aimone (jbaimon(a)sandia.gov<mailto:jbaimon@sandia.gov>), or Glory Emmanuel (gremman(a)sandia.gov<mailto:gremman@sandia.gov>). Applications received by December 19, 2014 will receive full consideration. -- Technical Staff (Job ID 647552): The successful applicant for our technical staff position should have a strong background in neuroscience (as evidenced by a Ph. D. in neuroscience, cognitive science, machine learning, or a related field), and experience with current neuroscience research methods and data analysis. The successful applicant will be expected to contribute to ongoing projects as well as to develop new lines of research and pursue new funding opportunities. Experience should be commensurate with a beginning assistant professor or higher. Postdoctoral Position in Computational Neuroscience (Job ID 647564): An advanced degree (Ph. D. or equivalent) in computational neuroscience or a related area is required for this position, as is a strong record of academic performance and publications. Other required qualifications include competence in C, C++, Matlab, Python, or a related programming language, and excellent written- and oral-communication skills. Postdoctoral Position in Machine Learning/Computer Science (Job ID 647563): An advanced degree (Ph. D. or equivalent) in mathematics or computer science or related area is required for this position; with a preference for those with experience in examining neural computation or developing machine learning approaches based on neural inspiration. Those with focus on mathematics research areas pertinent to neural systems, such as studies of large dynamical systems, high-dimensional statistics, and dimensionality reduction, are particularly encouraged to apply. A strong record of academic performance and publications is required. Other required qualifications include competence in C, C++, Matlab, Python, or a related programming language, and excellent written- and oral-communication skills.

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Last Mile: ADAPTIVE 2015 || March 22 - 27, 2015 - Nice, France
by Cristina Pascual Nov. 4, 2014

Nov. 4, 2014

INVITATION: ================= Please consider to contribute to and/or forward to the appropriate groups the following opportunity to submit and publish original scientific results to ADAPTIVE 2015. The submission deadline is November 18, 2014. Authors of selected papers will be invited to submit extended article versions to one of the IARIA Journals: http://www.iariajournals.org ================= ============== ADAPTIVE 2015 | Call for Papers =============== CALL FOR PAPERS, TUTORIALS, PANELS ADAPTIVE 2015, The Seventh International Conference on Adaptive and Self-Adaptive Systems and Applications March 22 - 27, 2015 - Nice, France General page: http://www.iaria.org/conferences2015/ADAPTIVE15.html Call for Papers: http://www.iaria.org/conferences2015/CfPADAPTIVE15.html Submission page: http://www.iaria.org/conferences2015/SubmitADAPTIVE15.html Contributions: - regular papers [in the proceedings, digital library] - short papers (work in progress) [in the proceedings, digital library] - ideas: two pages [in the proceedings, digital library] - extended abstracts: two pages [in the proceedings, digital library] - posters: two pages [in the proceedings, digital library] - posters: slide only [slide-deck posted at www.iaria.org] - presentations: slide only [slide-deck posted at www.iaria.org] - demos: two pages [posted at www.iaria.org] - doctoral forum submissions: [in the proceedings, digital library] Proposals for: - mini-symposia: see http://www.iaria.org/symposium.html - workshops: see http://www.iaria.org/workshop.html - tutorials: [slide-deck posted on www.iaria.org] - panels: [slide-deck posted on www.iaria.org] Submission deadline: November 18, 2014 Sponsored by IARIA, www.iaria.org Extended versions of selected papers will be published in IARIA Journals: http://www.iariajournals.org Print proceedings will be available via Curran Associates, Inc.: http://www.proceedings.com/9769.html Articles will be archived in the free access ThinkMind Digital Library: http://www.thinkmind.org The topics suggested by the conference can be discussed in term of concepts, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas. All tracks are open to both research and industry contributions, in terms of Regular papers, Posters, Work in progress, Technical/marketing/business presentations, Demos, Tutorials, and Panels. Before submission, please check and comply with the editorial rules: http://www.iaria.org/editorialrules.html ADAPTIVE 2015 Topics (topics and submission details: see CfP on the site) Fundamentals and design of adaptive systems Fundamentals on adaptive and self-adapting systems; Frameworks and architectures for adaptive supporting platforms; Architectures for adaptive applications; Specification of adaptive behavior; Specification of adaptive structures and topologies; Design and implementation of adaptive components; Composition of adaptive behaviors and structures; Adaptive individual and collective behavior and structures; Adaptive deterministic and non-deterministic behavior; Semantic modeling Adaptive entities Adaptive environments; Adaptive hardware configurations; Adaptive software applications; Adaptive protocols; Adaptive algorithms; Adaptive and interactive interfaces; Adaptive filters; Adaptive clock speeds; Adaptive schedulers; Adaptive load balancers Adaptive mechanisms Adaptive real-time strategies; Learning-based adaptive strategies; Adaptive mechanisms (trees, ant algorithms, fuzzy-logic, etc. ); Agent based-adaptive mechanisms; Policy- based adaptation; Feedback-based adaptive systems; Context-aware adaptation; User-aware adaptation Adaptive applications Adaptive artificial intelligence in computer wargames; Intelligent complex adaptive systems; Adaptive virtualization; Adaptive social networks; Adaptive entertainment applications; Adaptive disaster recovery systems; Anticipative adaptive systems; Fault-tolerant adaptive systems; Adaptive ad hoc networks; Application domains, e,g., building (AEC projects, simulation models, facility management), tourism, etc.; Adaptive economic applications (finance, stock exchange, regulatory decisions, etc.); Adaptive vehicular traffic Adaptivity in robot systems Adaptive robot behavior; self-organization and emergence; flexible and reconfigurable robot structures; adaptation in collective and swarm robotics Self-adaptation Theory of self-adaptation and control; Self-adaptive networks, systems, and applications; Self-adaptive services; Self-adaptive behavior and topology/structure; Specification of self-adaptive behavioral control Self-adaptation applications Self-monitoring and self-management; Self-configuration, self-healing, self-management; Self-defense, self-protection, self-diagnosis; Self-adaptation of overlay networks; Self-adaptation in ad hoc sensor networks; Context-aware self-adaptation of networks and services Computational Trust for Self-Adaptive Systems Trust models for adaptive systems; Trust models for self-organizing and autonomics systems; Agent-based trust models for cooperation; Privacy and Security in self-organizing and adaptive systems; Human and social factors involved in trust and adaptation; Applications of trust and security on adaptive systems, with a focus on web-based applications, social networks, social search; Trust models for self-organizing systems of information (wikis, forums, blogs.); Trust and personalisation for enabling adaptation; Models for learning trust and the evolution of trust; Decentralised trust models; Case studies; Theoretical trust models for self-adaptation Metrics for adaptive and self-adaptive systems Stability and convergence; Optimal (self-) adaptation; Accuracy validation of (self-) adaptation; Real-time (self-) adaptive performance Committee: http://www.iaria.org/conferences2015/ComADAPTIVE15.html ================================================

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CFP: Special Issue on Neurobiologically Inspired Robotics: Enhanced Autonomy Through Neuromorphic Cognition
by Jeff Krichmar Nov. 4, 2014

Nov. 4, 2014

Dear Comp-Neuro, I hope some of you will consider submitting to this special issue of Neural Networks. Neurobiologically inspired robotics goes by many names: brain-based devices, cognitive robots, neurorobots, and neuromorphic robots, to name a few. The field has grown into an exciting area of research and engineering. The common goal is twofold: Firstly, developing a system that demonstrates some level of cognitive ability can lead to a better understanding of the neural machinery that realizes cognitive function. The often used phrase, “understanding through building”, implies that one can get a deep understanding of a system by constructing physical artifacts that can operate in the real-world. In building and studying neurobiologically inspired robots, scientists must address theories of neuroscience that couple brain, body, and behavior. Secondly, the deep theoretical understanding of cognition, neurobiology and behavior obtained by constructing physical systems, could lead to a system that demonstrates capabilities commonly found in the animal kingdom, but rarely found in artificial systems, most notably their adaptive and flexible autonomous behavior. There have already been some successes that meet these goals. For example, navigation models based on the hippocampus are now deployed on robots that autonomously explore their environment. Machine image processing systems based on visual cortex have been used in a number of unsupervised recognition and perception applications. Robots designed to address impairments due to disorders such as Alzheimer’s disease, autism spectrum disorder, and attentional deficit disorders, are being used as therapeutic and diagnostic tools without the need for constant caretaker supervision. Despite these successes, the field is still in its infancy and basic research is needed. In particular, we are interested in papers that describe: 1) How models of cognitive functions, such as attention, decision-making, learning and memory, perception, and social cognition can be constructed on physical robots. 2) How the neuromorphic devices, which are designed to run neural algorithms with low-power, can advance the construction of autonomous robotics. 3) How the theoretical and engineering lessons learned from constructing neurobiologically inspired robots can transfer to autonomous robots carrying out practical applications. This Special Issue invites papers that address the three broad topics described above. Topics of interest • Adaptive behavior • Active sensing • Artificial empathy • Cortical computing • Developmental robotics • Embodied Cognition • Neuromorphic Engineering • On-line learning and memory systems • Prediction and planning • Socially assistive robotics Guest Editors Jeffrey Krichmar, University of California, Irvine Minoru Asada, Osaka University Jorg Conradt, Technische Universitat München Important Dates Submission due: 1 Feb 2015 Acceptance notification: 1 Aug 2015 Expected publication: 1 Nov 2015 Submission instructions Each paper for submission should be formatted according to the style and length limit of Neural Networks. Please refer complete Author Guidelines at http://www.elsevier.com/journals/neural-networks/0893-6080/guide-for-authors. Note that published papers and those currently under review by other journals or conferences are prohibited. A separate cover letter should be submitted that includes the paper title, the list of all authors and their affiliations, and information of the contact author. Each paper will be reviewed rigorously, and possibly in two rounds, i.e., minor/major revisions will undergo another round of review. Prospective authors are invited to submit their papers directly via the online submission system at http://ees.elsevier.com/neunet/. To ensure that all manuscripts are correctly included into the special issue described, it is important that all authors select “SI: Neurobiological Robotics” when they reach the "Article Type" step in the submission process. Jeff Krichmar Department of Cognitive Sciences 2328 Social & Behavioral Sciences Gateway University of California, Irvine Irvine, CA 92697-5100 jkrichma(a)uci.edu http://www.socsci.uci.edu/~jkrichma

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**Abstract submission deadline THIS FRIDAY 11/07** MURI Winter School Jan. 2015
by Santamaria, Cookie Nov. 4, 2014

Nov. 4, 2014

UCSD and the University of Chicago present: MURI WINTER SCHOOL 2015<http://biocircuits.ucsd.edu/special/winterschool2015> Dynamics of Multifunction Brain Networks Organizers: Henry Abarbanel (UCSD), Timothy Gentner (UCSD), and Daniel Margoliash (Univ. of Chicago) 15th Floor, Building 1, Village West<http://maps.ucsd.edu/mapping/viewer/default.htm?mkey=1&lc=open&km=open&rc=c…>, UC San Diego January 7-9, 2015 Registration deadline: Friday, December 5, 2014 Abstract submission deadline: Friday, November 7, 2014 Lecturers: Tim Gardner<http://people.bu.edu/timothyg/Home.html> (Boston University) – “High density neural recordings in singing birds: methods and observations” Michael Long<http://longlab.med.nyu.edu/> (New York University) – “How does the brain generate behavioral sequences?” Gabriel Mindlin<http://www.lsd.df.uba.ar/> (University of Buenos Aires) – “The biomechanics of birdsong production” and “Low dimensional dynamics systems behind birdsong production” Richard Mooney<http://www.mooneylab.com/> (Duke University) – “Neurobiology of vocal learning” and “Forward mechanisms for hearing and learning” Marc Schmidt<http://www.bio.upenn.edu/people/marc-schmidt> (University of Pennsylvania) – “Brainstem and forebrain contributions to the generation of learned motor” Ofer Tchernichovski<http://ofer.sci.ccny.cuny.edu/> (Hunter College) – “Vocal learning beyond imitation: from cultural evolution to brain dopamine” and “How a song is learned: mechanisms of template matching” ________________________________________________________________________________ This is the third annual Winter School sponsored by the U.S. Office of Naval Research as part of its UCSD/Chicago MURI program in Dynamics of Multifunction Brain Networks. This year’s School presents a series of pedagogical and research oriented talks on the use of birdsong as a model system where theory and experiment can meet productively. It includes a series of pedagogical presentations and a poster session. The School is intended for all researchers, including advanced graduate students, interested in the general area of understanding functional nervous systems. Active participation during the School's lectures is strongly encouraged. All attendees are invited to submit abstracts for poster presentations. Selected contributed posters will be highlighted in an oral session. Registration is required for attendance, but there is no fee for attending. A limited number of travel awards are available for exceptionally qualified graduate students and postdocs, who should apply and have their supervisor send a letter of reference, via email, to muri.winter.school(a)gmail.com<mailto:muri.winter.school@gmail.com>. Please visit us online to register and for details regarding the schedule and poster abstract submission: http://biocircuits.ucsd.edu/special/winterschool2015

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PhD student position in theoretical/ computational neuroscience available in Mannheim, Germany
by Seeger, Simone Nov. 4, 2014

Nov. 4, 2014

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/>

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CfP : TWELFTH INTERNATIONAL CONFERENCE ON MULTIPLE CLASSIFIER SYSTEMS
by Dr. Schwenker Nov. 3, 2014

Nov. 3, 2014

---- 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

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PhD in “Fragmented neural processing in Parkinson's disease” at University of Manchester, Faculty of Life Sciences
by Mark Humphries Nov. 3, 2014

Nov. 3, 2014

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/

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Assistant or Associate Professor, Data Analytics
by Guerra, Erick J. Nov. 1, 2014

Nov. 1, 2014

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.

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Call for Papers - Neural Networks special issue on Big Data
by Asim Roy Nov. 1, 2014

Nov. 1, 2014

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>)

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Postdoctoral Position in Neuroscience (BRAIN Initiative project): In vivo imaging of cortical dynamics
by Patrick Kanold Oct. 31, 2014

Oct. 31, 2014

Dear all, A postdoctoral position utilizing in vivo 2-photon imaging is available in the laboratory of Dr. Patrick Kanold at the University of Maryland. The laboratory studies the function, development, and plasticity of the cerebral cortex (http://www.kanold.org) We are seeking highly motivated individuals that want to join exciting collaborative project, together with Dr. Dietmar Plenz (NIMH, http://www.nimh.nih.gov/labs-at-nimh/principal-investigators/dietmar-plenz.…) and Dr. Wolfgang Losert (UMd Physics), funded by the NIH BRAIN initiative. We use in vivo 2-photon imaging and stimulation technology that allows rapid imaging and stimulation in multiple focal planes and aim to develop new analysis techniques based on dynamic systems and graph theoretic measures to extract network dynamics at the single neuron and population level. We aim to identify the influence of single neurons relative to the synergistic influence of specific groups of neurons (the crowd) on network dynamics and ultimately behavior of the animal (see http://www.clfs.umd.edu/biology/kanold/Research.html) The ideal candidate will have a PhD in neuroscience, engineering, or related discipline, a background in systems neuroscience, and an ability to work in a large multidisciplinary team. Special considerations are given to applicants with training in imaging, in vivo physiology, computational approaches, and programming skills (esp. MATLAB). Qualified individuals should send a copy of curriculum vitae and names of three references to pkanold(a)umd.edu.

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