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NEURON course at SFN 2016 meeting
by Ted Carnevale July 18, 2016

July 18, 2016

Using NEURON to Model Cells and Networks Friday, Nov. 11 from 9 AM to 5 PM at a site in downtown San Diego This course provides a practical introduction to building and using models of neurons and networks with NEURON. Covered topics include basic concepts, workflow for building and using models, speeding up simulations with parallel hardware from multicore personal computers to massively parallel supercomputers, using Python with NEURON, and modeling reactive diffusion with the RxD class. The registration deadline for this course is Friday Oct. 28, but you should sign up early because space is limited. See http://www.neuron.yale.edu/neuron/static/courses/sd2016/sd2016.html for more information and a link to the registration form. --Ted

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Subject: Postdoctoral position in computational neuroscience
by Szabolcs Káli July 15, 2016

July 15, 2016

Applications are invited for a postdoctoral research position in the Laboratory of Cerebral Cortex Research at the Institute of Experimental Medicine of the Hungarian Academy of Sciences. The institute, located in the center of Budapest, is dedicated to biomedical research in the field of neuroscience, hosts a variety of world-class research groups, and was awarded the title “Centre of Excellence” by the European Commission. The laboratory, led by Prof. Tamás Freund, employs a multidisciplinary approach to investigate the generation of normal and pathological patterns of network activity in the hippocampus, the roles of subcortical inputs in cortical information processing, and the molecular mechanisms of synaptic transmission. The successful candidate will work with Dr. Szabolcs Káli on developing detailed biophysical models of hippocampal neurons and circuits in the context of a large-scale initiative for collaborative work in computational neuroscience. The aim of the project is (1) to further the understanding of the dynamics and computational capabilities of cortical cells and networks, and (2) to create a generic framework and corresponding tools for the reproducible, collaborative development, simulation, and validation of data-driven neural models. The successful applicant is expected to contribute to both aspects of the project, developing morphologically and biophysically detailed models of different types of hippocampal neurons, and constructing anatomically and physiologically faithful models of hippocampal circuits. The work will take advantage of the neuroinformatics, brain simulation, and high-performance computing infrastructure developed by the European Human Brain Project, and the holder of this post is expected to make significant contributions to testing an integrative platform for model development, representation, optimization, and validation. He/she will work closely with experimentalists within the group who provide anatomical and physiological data, and is expected to coordinate model and tool development with collaborators at leading European institutions. The job also offers ample opportunities to develop and maintain contacts with members of the wider (experimental and modeling) community. We are looking for an individual holding a PhD in computational neuroscience (or related discipline), with a keen interest in applying theoretical and simulation tools to understanding neural information processing. The position requires an ability to work independently, but also as a member of a large international research team. The applicant should be able to communicate effectively in English, both in speech and in writing. We also expect the successful candidate to have experience in at least some of the following areas: - developing cellular or network models using neural simulation software (such as Neuron) - analysis of anatomical and/or electrophysiological data sets - programming (in Python, MATLAB, etc.) and collaborative software development - advanced parameter optimization methods (such as evolutionary algorithms) The position is available from September 1, 2016 (the exact starting date is flexible) until March 31, 2018 in the first instance, with the possibility of renewal. The salary is according to local scales for postdoctoral positions in EU-funded projects, and depends on relevant experience. Review of applications will start on August 10, but applications are accepted until the position is filled. Applications should contain a CV, a brief letter of interest, and two references, and should be sent to Szabolcs Káli (kali(a)koki.hu) Informal inquiries about the post are also welcome. -------------------------------------------------------- Szabolcs Káli Laboratory of Cerebral Cortex Research Institute of Experimental Medicine Hungarian Academy of Sciences H-1083 Budapest Szigony utca 43. Phone: +36-1-2109413 Fax: +36-1-2109412 E-mail: kali.szabolcs(a)koki.mta.hu www.koki.mta.hu

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Call for participation "From Single Neurons to Brain Complexity and Human Behavior" Research Topic (Frontiers)
by PRWTOPAPA FWTEINH July 14, 2016

July 14, 2016

Dear potential authors on the comp-neuro mailing list, [Apologies for cross-posting]In collaboration with Frontiers in Computational Neuroscience, we are organizing a Research Topic titled "From Single Neurons to Brain Complexity and Human Behavior", hosted by Constantinos Siettos, Jens Starke, Konstantinos Michmizos, Foteini Protopapa, Nikolaos Smyrnis. As host editor, I would like to encourage you to contribute to this topic. Please find more information below: Web site: http://frontiersin.org/Computa tional_Neuroscience/researchto pics/From_Single_Neurons_to_ Brain_Complexity_and_Human_ Behavior/5116Deadlines for this Research Topic are: Oct 01, 2016 (Abstract) and Feb 01, 2017 (Manuscript)Description: During brain data acquisition, from the neural to the behavioral scale, the targeted brain function is typically secluded from the influence of other brain functions. Such an approach results in data acquired at a single spatial and temporal scale of the brain and limits the analysis and understanding of the intrinsically multiscale and parallel brain functions. This calls for a paradigm shift from examining the brain as a monolithic structure to examining –and interacting with– the brain holistically. The proposed Research Topic aims to attract papers that will form a new computational infrastructure resource by combining novel integrative methods in Computational Neuroscience. Specifically, the research focus will be on: 1) The development of new methods and algorithms that can deal with multiscale dynamics and integrate data from synchronized behavioral and neural activity; and 2) The improved understanding of the neural dynamics in behavioral states. The understanding of brain functionality across its scales will have an unprecedented health, social and financial impact. The proposed Research Topic will host advanced mathematical modeling efforts and computational approaches that seek to understand how the interplay among cellular, anatomical and neuronal connectome empowers complex brain functions such as vision, attention, memory, learning and emotion. Tools include, but are not limited to, linear and nonlinear time series analysis of single unit recordings, EEG, MEG, fMRI and PET as well as multiscale modeling and analysis of neuronal systems dynamics, causal connectivity analysis (Granger-based, Phase Synchronization, Mutual Information, Transfer Entropy etc), manifold and machine learning methodologies for handling data complexity (PCA, ICA, ISOMAP, Diffusion Maps) etc. We seek to present research efforts that bridge the bottom-up and top-down approaches. A bottom-up research includes modelling the dynamics of neurons and/or networks of neurons to approximate the complex emergent behavior whereas a top-down research would try to reveal how macroscale phenomena observed in integrative EEG, MEG or fMRI signals can be used to infer about the brain organization and ultimately explain the underlying functional mechanisms. Specific areas of interest include but are not limited to: the reconstruction of functional networks that govern the information flow of the brain signals, the identification of distinct spatio-temporal patterns that can suggest the mechanisms of specific higher cognitive functions, and the modeling and analysis of resting-state activity. We will try to cover studies targeting the healthy brain as well as studies targeting neurologic and psychiatric disorders in adults (such as epilepsy, Parkinson’s disease Schizophrenia and other Psychotic disorders) and/or in children and adolescents (such as child epilepsy and autistic spectrum disorders). Let us know if you have any questions about this Research Topic. We look forward to hearing from you soon. With best regards, Constantinos Siettos (ksiet(a)mail.ntua.gr) Associate Professor at National Technical University of Athens (NTUA), Greece.and Foteini Protopapa, Ph.D. ( fwteini104(a)gmail.com ) Research Associate at EPIPSI, Greece.

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4 PhD / PostDoc positions in the Institute of Neural Information Processing, Ulm University, Germany
by Daniel Braun July 13, 2016

July 13, 2016

In the *Institute of Neural Information Processing* of the Faculty of Engineering, Computer Science and Psychology of Ulm University there will be *4 PhD / PostDoc positions in the field of Learning Systems* from October 2016 in the newly established research group on “Learning Systems”. The group is interested in the mathematical foundations of intelligent behavior in cognitive systems, including both biological and artificial systems, in particular the information-processing principles underlying learning and decision-making. This requires a combined theoretical and experimental approach. In our theoretical line of work we use methods from machine learning, statistics, physics, information theory and decision theory to investigate the effects of limited resources on learning and decision-making (“bounded rationality”). In our experimental line of work we investigate sensorimotor behavior in humans by using virtual reality technology and robotic interfaces to study learning and decision-making quantitatively under controlled conditions. This interdisciplinary approach unites aspects from both basic and applied research. On a basic level we aim at a quantitative understanding of embodied intelligence, in particular the emergence of cognition from sensorimotor interactions by processes of abstraction and structure learning. On an applied level we are interested in both autonomous robotic systems and human-machine-interactions, as sensorimotor learning and decision-making in artificial systems constitute a cutting-edge technical challenge. The research group is supported by the ERC-project BRISC (Bounded Rationality in Sensorimotor Coordination) https://erc.europa.eu/projects-and-results/erc-funded-projects/BRISC funded by the excellence programme of the European Research Council, in which we aim at a novel information-theoretic understanding of sensorimotor interactions in psychological and technical systems. Previous publications of the group can be found under http://www.kyb.tuebingen.mpg.de/research/rg/braun.html Ulm University with its more than 10,000 students is Baden-Württemberg’s youngest university. It offers attractive career prospects in a highly innovative research, teaching and work environment at the same time facilitating the reconciliation of work and family life. Embedded within the novel international “Cognitive Systems”-Master’s course, the research group “Learning Systems” is part of the university’s priority programme on human-machine-interaction. Beyond the university, there are plenty of opportunities to cooperate within the SFB Transregio 62 and with international partners in Great Britain and the United States. The applicants should have a strong mathematical background, hold a Diploma or Master’s degree in natural science, mathematics, economics, psychology, computer science, engineering or similar and have a keen interest in artificial and natural intelligence. The positions are paid according to German tariff TV-L E13. Exceptional candidates with a Bachelor’s degree can also be considered, albeit with an individual pay scale regulation. The University of Ulm is an equal opportunity employer: Handicapped individuals are strongly encouraged to apply, and so are women in areas in which they are underrepresented. The positions will be filled as soon as possible. Candidates should send a CV, a brief statement of interest and a copy of their highest degree to Prof. Dr. Dr. Daniel Alexander Braun daniel.braun(a)uni-ulm.de . -- Prof. Dr. Dr. Daniel Alexander Braun Institute of Neural Information Processing Faculty of Engineering, Computer Science and Psychology University of Ulm James-Franck-Ring D-89081 Ulm Germany phone: +49 (0)731 50 - 24151 <%2B49%20%280%29731%2050%20-%2024158> fax: +49 (0)731 50 - 24156 email: daniel.braun(a)uni-ulm.de

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POSTDOCTORAL POSITION IN COMPUTATIONAL NEUROSCIENCE AT BROWN UNIVERSITY
by Wilson Truccolo July 12, 2016

July 12, 2016

Applications for an NIH-funded post-doctoral position are invited for the Laboratory of Dr. Wilson Truccolo in the Department of Neuroscience at Brown University, Providence, RI, USA. The positions are for the development of data-driven stochastic models of multiscale neural dynamics in human epilepsy. Data consist of simultaneous intracortical microelectrode array recordings of ensembles of single neurons and local field potentials, as well as ECoGs, during seizure and non-seizure states in individuals with pharmacologically resistant epilepsy. The project involves a collaboration between the labs of Dr. Truccolo and Dr. Sydney Cash (Massachusetts General Hospital, Harvard Medical School, Boston). The ideal candidate will have a strong background in computational/theoretical neuroscience, modeling of stochastic neural dynamics and experiments involving closed-loop electrical stimulation. Related recent publications can be found on the Truccolo lab website ( http://www.truccololab.com/ <http://dl.dropbox.com/u/983891/Truccolo_home.htm>). Inquiries should be addressed to Dr. Truccolo (wilson_truccolo(a)brown.edu) Applications including a CV, statement of research interests, and the names and full contact details of three referees should be sent to wilson_truccolo(a)brown.edu. -- Wilson Truccolo, Ph.D. Pablo J. Salame '88 Goldman Sachs Assistant Professor of Computational Neuroscience Department of Neuroscience & Institute for Brain Science, Brown University Lab website: http://www.truccololab.com/

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Ph.D. positions in computational neuroscience at ESI
by Martin Vinck July 9, 2016

July 9, 2016

The laboratory of Dr. Martin Vinck at the *Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society *is seeking candidates for Ph.D. positions. Students will work on two classes of problems. First, we aim to solve various methodological / neurostatistical problems for offline and on-line, closed-loop analysis of multivariate neuroscience datasets. Second, we are developing models of visual cortex in order to test specific hypotheses about the relationship between cortical dynamics and computation. PhD students will work together with experimental neuroscientists and apply methods and models to experimental datasets from primate and rodent animal models, including electrophysiological and calcium imaging data. We are primarily looking for students with training in a quantitative discipline (physics, engineering, computer science etc.). Interested candidates are invited to send their application materials in electronic form (PDF format) to martin.vinck(a)esi-frankfurt.de Applications should contain a letter of intent, a detailed curriculum vitae, lists of university courses completed with marks obtained, and the names of at least two scientists who can give references. The advertisement will be valid until positions are filled. Dr. Martin Vinck martin.vinck(a)esi-frankfurt.de Research Group Leader Ernst Struengmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society Deutschordenstr. 46, D-60528 Frankfurt am Main, Germany

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PhD/postdoc positions at Ernst Struengmann Institute for Neuroscience
by Martin Vinck July 9, 2016

July 9, 2016

Our laboratory at the Ernst Struengmann Institute for Neuroscience in Cooperation with Max Planck Society is seeking candidates for Postdoc and Ph.D. positions. Projects involve the application of parallel, large-scale electrophysiological recording techniques of single units and EEG in combination with closed-loop optogenetic manipulation methods to investigate mechanisms of information encoding and transmission in mice brains. We are particularly interested in the way in which the brain efficiently encodes information, and the way in which sensory predictions established in higher areas influence sensory representations in earlier sensory areas. We are furthermore interested in investigating the role of GABAergic interneurons in selective attention and neuronal oscillations. We address these questions using laminar recordings in combination with optogenetics in mice, as well as through the development of novel tools for the quantitative analysis of large-scale electrophysiological data. Within the ESI, tight collaborations exist with the lab of Prof. Dr. Pascal Fries. Because we are searching for members that can strengthen an interdisciplinary team, we are encouraging students/researchers with a background either in biological/medical sciences or in physics/engineering/mathematics/etc. to apply. The ideal PhD or postdoc candidate has previous experience and strengths either in 1) performing hands-on experimental work and electrophysiological recordings or 2) hardware engineering and quantitative analysis of large-scale data. We are searching for applicants with good communication skills and a high commitment and curiosity about neuroscientific questions. Interested candidates are invited to send their application materials in electronic form (PDF format) to martin.vinck(a)esi-frankfurt.de Applications should contain a letter of intent, a detailed curriculum vitae, lists of university courses completed with marks obtained (only for PhD candidates), and the names of at least two scientists who can give references. The advertisement will be valid until positions are filled. Dr. Martin Vinck martin.vinck(a)esi-frankfurt.de Research Group Leader Ernst Struengmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society Deutschordenstr. 46, D-60528 Frankfurt am Main, Germany

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Tenured Junior Research/Teaching Faculty Position in Computational Neuroscience at Ecole Normale Superieure in Paris, France
by Boris Gutkin July 9, 2016

July 9, 2016

Tenured Junior Research/Teaching Faculty Position in Computational Neuroscience at Ecole Normale Superieure in Paris, France A tenured junior faculty position is expected to open in the Department of Cognitive Studies (DEC) at the ENS-Paris. The position profile is broad, focusing on computational neuroscience. The recruited candidate will join the Group for Neural Theory, which is part of the Laboratory for Cognitive Neuroscience,DEC, ENS Paris. The candidate is expected to have a strong background in theoretical neuroscience and/or computational neuroscience. The candidate is expected to develop an active research program and integrate into the GNT. Teaching in neuroscience and computational neuroscience shall be done at the ENS within the cognitive sciences masters program. The Laboratory for Cognitive Neuroscience provides opportunities to develop a collaborative research program in computational neuroscience of decision making. Further collaborative opportunities exist within the department and local laboratories involved in electrophysiology and/or psychophysics. The next cycle of recruitment for a Junior Faculty position in computational neuroscience shall be announced in the spring of 2017. In order to apply for the position candidates need to pre-qualify with the National University Council. The deadline for the pre-qualification is in October 2016. Help with putting together the application shall be given to potential candidates who should contact mc.comp.gnt(a)gmail.com

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Special Issue of Applied Soft Computing on Non-iterative Approaches in Learning
by Teng Teck Hou July 8, 2016

July 8, 2016

Special Issue of Applied Soft Computing (Elsevier) (Impact Factor: 2.8) Submission is open now Special Issue on Non-iterative Approaches in Learning (Includes comparisons with iterative methods) Call for Papers http://www.journals.elsevier.com/applied-soft-computing/call-for-papers/spec ial-issue-on-non-iterative-approaches-in-learning-includ/ Optimization, which plays a central role in learning, has received considerable attention from academics, researchers, and domain workers. Many optimization problems in machine learning are solved by iterative methods which generate a sequence of improving approximated solutions with some termination criteria. These methods usually suffer from low convergence rate and are sensitive to parameter settings (such as learning rate/step size, maximum number of iterations). On the other hand, non-iterative solutions, which are usually presented in closed-form manner, are in general computationally faster than iterative solutions. However, comparative studies with iterative methods are also welcome. The main focus of this special issue is to present the recent advances in non-iterative solutions in learning. Original contributions and surveys are welcome. The special issue aims to promote non-iterative concepts in the field of learning. Even though non-iterative methods have attracted much attention in recent years, there exists a performance gap when compared with older methods and other competing paradigms. This special issue aims to bridge this gap. Besides the dissemination of the latest research results on non-iterative algorithms, it is also expected that this special issue will cover some industrial applications, present some new ideas and identify directions for future studies. The topics of the special issue include, but are not limited to: * Methods with and without randomization * Regression, classification and time series * Kernel methods such as kernel ridge regression, kernel adaptive filters, etc. * Feedforward, recurrent, multilayer, deep and other structures. * Ensemble learning * Moore-Penrose pseudo inverse, SVD and other solution procedures. * Non-iterative methods for large-scale problems with and without kernels * Theoretical analysis of non-iterative methods * Comparative studies with competing iterative methods * Applications of non-iterative solutions in domains such as power systems, biomedical, finance, signal processing, big data and all other areas Submission format and Guidelines Papers will be evaluated based on their originality, presentation, relevance and contribution to the development of non-iterative methods, as well as their suitability and the quality in terms of both technical contribution and writing. The submitted papers must be written in good English and describe original research which has not been published nor is currently under review by other journals or conferences. If used, the previously published conference papers should be clearly identified by the authors (at the submission stage) and an explanation should be provided how such papers have been extended to be considered for this special issue. Guest Editors will make an initial determination on the suitability and scope of all submissions. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review and the authors will be promptly informed of such cases. Author guidelines for preparation of manuscript can be found at http://www.journals.elsevier.com/applied-soft-computing/ Manuscripts should be submitted online at: http://ees.elsevier.com/asoc/ Applied Soft Computing Journal is well indexed. Its impact factors are 2.8 (2 years) and 3.2 (5 years). Important dates Manuscript submission: 15th Aug 2016 Revised version submission: 31st Jan 2017 Acceptance notification: 31st March 2017 Expected Publication: Mid 2017 Guest Editors Dr P N Suganthan, Nanyang Technological University, Singapore. epnsugan(a)ntu.edu.sg Prof. Sushmita Mitra, Indian Statistical Institute, India. sushmita(a)isical.ac.in Dr Ivan Tyukin, Department of Mathematics, University of Leicester, UK. I.Tyukin(a)le.ac.uk

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IEEE CIDM 2016
by Friedhelm Schwenker July 6, 2016

July 6, 2016

CALL FOR PAPERS Paper Submission Deadline: July 18, 2016 IEEE CIDM 2016 IEEE Symposium on Computational Intelligence and Data Mining December 6-9, 2016, Athens, Greece SSCI 2016:http://ssci2016.cs.surrey.ac.uk/CIDM.htm CIDM 2016:http://neuro.informatik.uni-ulm.de/CIDM2016/ IEEE CIDM 2016 organized by the IEEE Computational Intelligence Society Data Mining Technical Committee is part (one of 30 symposia) of the IEEE Symposium Series of Computational Intelligence (IEEE SSCI 2016). IEEE CIDM 2016 will bring together researchers and practitioners from around the world to discuss the latest advances in the field of computational intelligence applied to data mining and will act as a major forum for the presentation of recent results in theory, algorithms, systems and applications. Topics related to all aspects of data mining, such as theories, algorithms, systems and applications, particularly those based on computational intelligence technologies, are welcome; these include, but are not limited to: • Neural networks for data mining • Evolutionary algorithms for data mining • Fuzzy sets for data mining • Data mining with soft computing • Foundations of data mining • Mining with big data • Classification, Clustering, Regression • Association • Feature learning and feature engineering • Machine learning algorithms • Multimedia data mining • Text mining • Link and graph mining • Social media mining • Collaborative filtering • Crowd sourcing • Personalization • Security, privacy and social impact of data mining • Data mining applications Important Dates • Paper submission: July 18, 2016 • Notification to authors: September 12, 2016 • Final submission: October 10, 2016 • Early registration: October 10, 2016 Co-Chairs Carlotta Domeniconi, Georg Mason University, USA Friedhelm Schwenker, Ulm University, Germany Zhi-Hua Zhou, Nanjing University, China -- PD Dr. Friedhelm Schwenker University of Ulm Institute of Neural Information Processing D-89069 Ulm, Germany phone: +49-731-50-24159 fax: +49-731-50-24156 email: friedhelm.schwenker(a)uni-ulm.de www: http://www.uni-ulm.de/in/neuroinformatik/mitarbeiter/f-schwenker.html

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