- Comp-neuro - lists.cnsorg.org

Postdoctoral position available in the Niv Lab at Princeton

by Yael Niv

A postdoctoral position is available in the lab of Dr. Yael Niv in the Princeton Neuroscience Institute and the Department of Psychology at Princeton University (http://www.princeton.edu/~nivlab) for a collaborative project with the lab of Geoffrey Schoenbaum at NIDA. Research in the Niv lab focuses on computational modeling of learning and decision making at the systems level (reinforcement learning, Bayesian inference), and model-driven behavioral and functional imaging experiments of human learning and decision making. Research in the Schoenbaum lab utilizes rodent models to study learning and decision making, as well as addiction. We seek an exceptionally talented candidate who is intensely interested in understanding decision making and addiction, has proven experience in empirical investigations of learning and decision making (and corresponding task design), has expertise in computer programming and modeling, and has a keen interest in reinforcement learning and normative models of behavior. Background in studies of addiction, functional imaging and/or rodent work is an advantage. Anticipated start date is January 2015 though there is considerable flexibility. This is a one-year position with the possibility of renewal, pending satisfactory performance. Essential Qualifications: PhD in psychology, neuroscience or equivalent. Proven experience with computer programming. Preferred Qualifications: The ideal candidate will have robust experience with fMRI (event related designs and model-based analysis techniques) or with studies of rodent decision making (Pavlovian and instrumental conditioning), will be proficient in programming (Matlab or equivalent), and will have experience with computational modeling (machine learning, reinforcement learning, Bayesian models). To apply, please visit the website https://jobs.princeton.edu (requisition # 1400567) and create an online application. Applicants must submit a cover letter stating background and research interests and citations of at least two representative publications, a CV, and contact information for at least two references. Princeton University is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. This position is subject to the University’s background check policy.

Sept. 10, 2014

Joint Neuroscience-Math tenure-track faculty position at UT Austin

by Ila Fiete

Dear Colleagues, UT Austin is initiating a multi-faculty (cluster) search for tenure-track faculty in computational and theoretical neuroscience, to build on existing efforts here in that direction. I request that you pass on word of this search to exceptional senior postdocs who bridge math and neuroscience and are ready to start junior faculty positions. Attached below is the text of the search advertisement, and details on how to apply. Best regards, Ila --------------- The Departments of Neuroscience and Mathematics at the University of Texas at Austin are seeking a leader in the field of mathematical neuroscience or mathematics applied to neuroscience, for appointment as a tenure-track assistant professor. Areas of interest include, but are not limited to, the intersection of neuroscience with 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 in the field to create a Computational and Theoretical Neuroscience center. The successful candidate will orchestrate an outstanding program of independent research and contribute to an environment of diversity in higher education through research, teaching, and service. She or he will have joint membership in both departments and teach one course in each department. 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. Qualified applicants with a Ph.D. or equivalent and two or more years of post-graduate experience will be considered for appointment. Applications will be reviewed starting September 5, 2014, until the position is filled. Please submit applications through the MathJobs website: https://www.mathjobs.org/jobs/jobs/6059 <https://www.mathjobs.org/jobs>. A complete application will include: 1. Statement of research interests. 2. Curriculum vita. 3. Letters of reference from at least 3 individuals. 4. 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>). -- ________________________________ Ila Fiete Associate Professor Center for Learning and Memory The University of Texas at Austin Phone: 512.232.8439

Sept. 10, 2014

Job Posting: Neuroscience Image Processing Scientist at Northwestern University

by Xue Wang

The Department of Radiology at Northwestern University Feinberg School of Medicine is seeking a full-time Neuroscience Image Processing Scientist. Under the direction of the Medical Director of the Neurological Testing Center and the Director of Neuroimaging Research, the Neuroscience Image Processing Scientist will be responsible for coordinating imaging for use in epilepsy surgery and other functional neurosurgical procedures. Assignments will require close interaction with neuroradiology and neuroimaging collaborators to develop and implement more advanced functional imaging techniques. The primary effort will be focused on the development and implementation of clinical tools; however, there is dedicated time for independent research. Northwestern has very active clinical and research programs in Neurology and Neuroimaging. Imaging resources include 10 clinical and 4 dedicated research high field MRI systems, a PET/CT system, high density EEG and TMS. There is a very active and rapidly expanding Neurosurgery group with strong collaborations with BrainLab, and a supportive clinical environment for research. Candidates should have an MD or PhD in neuroscience, medicine, physics, psychology or a related field, and excellent communication skills. Experience with analysis of structural, diffusion, fMRI including resting state, MEG, PET, SPECT, and EEG data is required. The ability to co-register multi-modality diagnostic (EEG, MEG) studies with SISCOM, MRI, fMRI, DTI and PET studies is a must. Experience in planning stereo EEG electrode placement, surgical guidance, and proficiency with PRISM software, CURRY Neuroimaging Suite, and BrainLab surgical navigation systems, is desired. The position is funded for an initial two-year time period, with a high likelihood of long term employment. Salary is based on the NIH scale and benefits are highly competitive. Position is available on July 1, 2014, and applications will be accepted until the position is filled. Applicants should send curriculum vitae, references and cover letter to: Todd B. Parrish, Ph.D. Department of Radiology 737 N. Michigan Ave, 16th Floor Chicago, IL 60611 toddp(a)northwestern.edu Northwestern University is an Equal Opportunity, Affirmative Action Employer of all protected classes, including veterans and individuals with disabilities. Women and minorities are encouraged to apply. Hiring is contingent upon eligibility to work in the United States

Sept. 9, 2014

2014 SFN Meeting Workshop: NSG Portal for parallel simulations

by Ted Carnevale

What: Using the Neuroscience Gateway Portal for Parallel Simulations A Satellite Symposium at the 2014 Society for Neuroscience Meeting Where: Location to be announced in downtown Washington, DC When: 9 AM - Noon on Saturday, November 15, 2014 Speakers to include: A. Majumdar, S. Sivagnanam, and T. Carnevale Registration deadline: Friday, October 31, 2014 Do you have a large scale modeling project that exceeds the speed or capacity of your local hardware? Have you tried to use high performance computing (HPC) resources at your own institution, but found the process too difficult because of administrative or technical barriers? If yes, this workshop is for you. In a single morning session, you will learn how to use the Neuroscience Gateway Portal (NSG) http://www.nsgportal.org/ which is designed for neuroscientists who need to use HPC resources for large modeling projects. It simplifies every aspect of the process, from getting allocations of free CPU time to uploading your model, launching and monitoring jobs, and downloading results. The NSG already has several parallel simulators installed, including Brian, NEST, NEURON, PGENESIS and PyNN. Space is limited, so sign up quickly. See http://www.neuron.yale.edu/neuron/static/courses/nsg2014/nsg2014.html for further details and the registration form. Support: The NSG project is supported by a grant from the National Science Foundation. NSG provides its users free allocations of CPU time on HPC resources under the auspices of XSEDE (Extreme Science and Engineering Discovery Environment), which is also supported by NSF.

Sept. 9, 2014

PhD position in Neurocomputational modeling of language processing

by Hartmut Fitz

A PhD position is available at the Max-Planck Institute for Psycholinguistics in Nijmegen, the Netherlands, within the 'Language in Interaction' consortium. The PhD candidate will participate in a team effort to understand the neurobiology underlying language processing by building computer models for sentence processing based on recurrent networks of spiking neurons. The goal is to develop a model with processing memory based on vector representations of words which are incrementally interpreted in terms of thematic roles ("who did what to whom"). A core objective will be to investigate the computational role of different neuronal models, connectivity types, neurobiologically motivated adaptation mechanisms, and different methods for calibrating the model's read-out units. Closing date: 30 September 2014 For more information see: https://www.languageininteraction.nl/jobs/phd-positions.html https://www.languageininteraction.nl/jobs/id-2nd-phd-call-general.html

Sept. 8, 2014

Postdoc opportunity at Stanford in Causal Brain Network Analysis

by Wei Wu

Dear fellow researchers, The Etkin Lab at Stanford University is currently seeking to fill a postdoc position for causal brain network analysis. Please find below the job requirement and contact info. Thank you for your attention! Postdoc opportunity at Stanford in Causal Network Analysis – from Concurrent TMS/fMRI to Transdiagnostic Imaging of Psychiatric Disorders The Laboratory of Amit Etkin, MD PhD at Stanford University is currently accepting applications for a postdoctoral research fellowship focused on causal network analyses using fMRI in healthy individuals and patients with a range of psychiatric disorders. The focus of this position is on application of network analysis tools (e.g. graph theory, network-based statistics, etc) to fMRI data, including both causal circuit mapping experiments with concurrent transcranial magnetic stimulation (TMS) and fMRI, as well as resting-state data from patients across the mood, anxiety and stress disorders represented, and across the lifespan. The successful applicant will have a PhD in Cognitive Neuroscience, Neurophysiology, Psychology, Computer Science, Statistics or related fields. Experience with analysis of fMRI data or with causal network analysis tools is required. Additional programming experience is a plus. Duties will also include manuscript preparation, presentation of findings at conferences, management of research assistants and contribution to the preparation of grants. Laboratory and Stanford resources include research-dedicated 3T and 7T MRI scanners, concurrent TMS/fMRI setups and concurrent TMS/EEG setups. Salary commensurate with experience. More information about our ongoing studies can be found at: http://etkinlab.stanford.edu. To apply, please send a curriculum vitae, a statement describing research interests and relevant background and three letters of recommendations, as well as relevant reprints/preprints of research articles to: Amit Etkin, MD, PhD Department of Psychiatry and Behavioral Sciences Stanford University amitetkin(a)stanford.edu

Sept. 5, 2014

New software for neuronal model fitting

by Szabolcs Káli

Dear Colleagues, We are pleased to announce the availability of the first public (beta) version of Optimizer, an interactive software tool for fitting the parameters of neuronal models. Optimizer was developed in Python, and has the following main features: - efficiently fits the free parameters of arbitrary neural (or other) models using advanced nonlinear optimization algorithms - all aspects of the optimization problem (including the model, its free parameters, the target data, the simulation and recording protocol, the error function) and the fitting process itself can be conveniently controlled from a graphical user interface (GUI) - full internal support for models implemented in the Neuron simulator (while other models can be also optimized using a "black box" approach) - choice of 7 different (tunable) optimization algorithms, including a customized evolutionary (genetic) algorithm, differential evolution, simulated annealing, the basinhopping algorithm, random search, and two local search methods - choice of 11 different error functions for the comparison of simulated and experimental (target) data, including generic ones as well as several feature-based ones which are appropriate for the comparison of membrane potential traces containing action potentials; error functions can be combined with arbitrary weights - the software is easily extensible in various ways to support novel scenarios A paper describing the software, several use cases, and comparisons with other model fitting tools, has been recently published in Frontiers in Neuroinformatics (the abstract and citation details are included at the end of this message): http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00063/abstract The software can be downloaded using git from https://github.com/vellamike/optimizer where you can also find instructions for installing the software and its dependencies. The current version of Optimizer has been verified to work under several recent Linux distributions. Alternatively, a compressed archive containing the software and various examples can be downloaded from https://github.com/vellamike/optimizer/releases/tag/v2.0-beta The documentation of the software is available at http://optimizer.readthedocs.org/en/latest/ Finally, we wish to encourage everyone interested in fitting neuronal models to try the software and provide feedback. If you find that your favorite optimization algorithm or error function is not currently included in Optimizer, it can probably be added with relatively little effort, especially given an existing implementation of the algorithm or function itself, and we would be very interested in collaborating on such an extension. We are also interested in learning about the model fitting problems encountered by potential users; we would be happy to advise users on whether and how Optimizer may be used to solve particular problems; in addition, future development of the software will be guided by the feedback that we receive. Best regards, Szabolcs Káli and Péter Friedrich Friedrich P, Vella M, Gulyás AI, Freund TF and Káli S (2014) A flexible, interactive software tool for fitting the parameters of neuronal models. Front. Neuroinform . 8 :63. doi: 10.3389/fninf.2014.00063 Abstract: The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

Sept. 5, 2014

Job/Fellowship Opportunity: Big Data IT Management and Automation in NeuroMorpho.Org

by Rubén Armañanzas

George Mason University, Krasnow Institute for Advanced Study in Fairfax, VA (USA), invites applications for a full-time doctoral student graduate research assistant position available immediately in the Center for Neural Informatics, Structures, and Plasticity (krasnow1.gmu.edu/cn3) headed by Prof. Giorgio Ascoli. To be considered for this position, applicants must apply to the PhD program in Bioengineering at George Mason University (http://volgenau.gmu.edu/doctoral-degrees) following the instructions below. The successful applicant will join a collaborative multidisciplinary team at the cutting edge of the development and dissemination of digital tools and resources for neuroscience. The Ascoli Lab is funded by the National Institutes of Health, the National Science Foundation, the Office of Naval Research, the Air Force Office of Scientific Research, the Intelligence Advanced Research Projects Activity, and Keck. This position is particularly suited for computationally minded researchers seeking exposure to the burgeoning field of big data in neuroinformatics. The candidate will join the NeuroMorpho.Org project -- a free, public, centralized repository of neuronal digital reconstructions. NeuroMorpho.Org has firmly established itself as a powerful resource for the neuroscience community, with millions of downloads and data usage in over 200 publications by other labs since its 2006 public release. Its version 5.7 contains over 148,000 internal data files pertaining to more than 11,000 neuronal reconstructions contributed by 144 laboratories worldwide from 24 species and 95 brain regions. The pre-doctoral fellow will work within the CN3 research group and will be tasked with a variety of activities including design of new Information Technology (IT) solutions to enhance the performance of existing software and hardware infrastructure. Due to the growing pace of the field, the recipient will be also responsible for tuning and implementing the next generation support for Big Data handling of digital neuronal reconstructions. Desired qualifications include: 1. proficiency in C/C++, Java and Python; 2. experience in Unix/Linux server administration; MySQL design and administration; web technologies and API web services; content management systems; and 3. a BS/MS in Computer Science, Information/Systems/Software Engineering or a similar field. Knowledge in task analysis automation and interest in neuroscience are a plus. The position includes a $22,000/year 12 months-stipend (20 hours/week during academic year, 40 hours/week during summer) in addition to health insurance and up to 18 credits per year of tuition coverage in the Volgenau school of Engineering. To apply, please follow the PhD application instructions (http://volgenau.gmu.edu/how-to-apply) Include the wording "/Neuroinformatics fellowship in Prof. Giorgio Ascoli's lab/" in both your research/interest statement and cover letter. After submitting the application material, please also forward a copy of your resume, research/interest statement, and cover letter directly to ascoli(a)gmu.edu <mailto:ascoli@gmu.edu> with subject "Bioengineering PhD candidate". Beyond the frontiers of neuronal types

Sept. 4, 2014

NEURAL COMPUTATION - October 1, 2014

by Terry Sejnowski

Neural Computation - Contents -- Volume 26, Number 10 - October 1, 2014 Available online for download now: http://www.mitpressjournals.org/toc/neco/26/10 ----- Articles Efficient Sensory Encoding and Bayesian Inference With Heterogeneous Neural Populations Deep Ganguli, Eero P Simoncelli Dimensionality of Object Representations in Monkey Inferotemporal Cortex Sidney Lehky, Roozbeh Kiani, Hossein Esteky, and Keiji Tanaka Letters Hierarchical Control Using Networks Trained With Higher-Level Forward Models Gregory Duncan Wayne, Larry Abbott Spine Head Calcium as a Measure of Summed Postsynaptic Activity for Driving Synaptic Plasticity Bruce Graham, Ausra Saudargiene, and Stuart Cobb Design of Charge-Balanced Time-Optimal Stimuli for Spiking Neuron Oscillators Isuru Dasanayake, Jr-Shin Li Influence of External Inputs and Asymmetry of Connections on Information-Geometric Measures Involving Up to Ten Neuronal Interactions Yimin Nie, Jean-Marc Fellous, and Masami Tatsuno Fragility in Dynamic Networks: Application to Neural Networks in the Epileptic Cortex Duluxan Sritharan, Sridevi Vedula Sarma On Anti-periodic Solutions for Cohen-Grossberg Shunting Inhibitory Neural Networks With Time-varying Delays and Impulses Changjin Xu., Qiming Zhang Learning Rates of Lq Coefficient Regularization Learning With Gaussian Kernel Shaobo Lin, Jinshan Zeng, Jian Fang, and Zongben Xu ------------ 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 ------------

Sept. 4, 2014

Postdoctoral position at the University of Pittsburgh

by Brent Doiron

Applications are invited for a Simons foundation funded postdoctoral position(s) in the Mathematics and Neuroscience departments of the University of Pittsburgh. The project will involve the collection, analysis, and modeling of large-scale multi-unit recordings from cortex, with an emphasis on understanding how visual attention modulates neural response. Of specific interest is elucidating the neural mechanisms that underlie the control of neural variability, as well as its consequences on neural function. Successful candidates will either be members of the working group of Dr. Brent Doiron and will be in tight collaboration with the Dr. Marlene Cohen, or vice versa, depending on the fit of the applicant. Theoretical candidates should expect to be members of the Doiron group, while candidates with an experimental focus would be in the Cohen group. Please visit www.math.pitt.edu/~bdoiron and www.cohenlab.com for a list of recent work from the Doiron and Cohen groups. The successful candidate(s) will be integrated into the Center for the Neural Basis of Cognition (http://www.cnbc.cmu.edu) a large and vibrant community of neuroscientists spanning many departments across the University of Pittsburgh and Carnegie Mellon University. The position is funded for several years, with an initial one-year appointment and an expectation of extension to at least three years, given satisfactory performance. The salary is competitive, and the start date is negotiable (January 2015 is optimal). Applicants with a PhD in Mathematics, Applied Mathematics, Neuroscience, Statistics, Biology, Psychology or a related discipline, and ideally with experience in computing, computational neuroscience, statistical analysis, or in vivo recording should send a CV including a list of publications, a research statement, and reference contact information to bdoiron(a)pitt.edu The University of Pittsburgh is an Affirmative Action, Equal Opportunity Employer, and women and members of minority groups under-represented in academia are especially encouraged to apply. -- Associate Professor of Mathematics Center for the Neural Basis of Cognition University of Pittsburgh 301 Thackeray Hall Pittsburgh, PA, 15260 www.math.pitt.edu/~bdoiron

Sept. 3, 2014