- Comp-neuro - lists.cnsorg.org

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 8, 2016

July 8, 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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Postdoctoral position in Computational Neuroscience at IIT (Italy)
by Stefano Panzeri July 5, 2016

July 5, 2016

(apologies for cross posting) We are seeking candidates for a postdoctoral position in computational neuroscience in the Neural Computation Laboratory at the IIT https://www.iit.it/research/lines/neural-computation directed by Stefano Panzeri and located in Rovereto (Trento), Italy. This laboratory aims to crack the neural code using neural network modeling and computational analyses of neurophysiological experiments. The laboratory offers a range of strongly interdisciplinary expertise in computational neuroscience, including information theory and other types of advanced single-trial neural analyses, advanced spectral analysis, and biophysically plausible neural network modeling. The successful candidate will develop and use tools for the analysis of large-scale simultaneous recordings of the spiking activity of cells in the cortex to understand how populations of neurons carry information. The successful candidate would have the opportunity to benefit from an extensive set of existing collaborations with leading neurophysiological laboratories, including those of Nikos Logothetis at Max Planck Tubingen (collaborating on oscillations, sensory information coding and neuromodulation), Tommaso Fellin at IIT (collaborating on Optogenetics, Neurophysiology and Neural coding), Mathew Diamond at SISSA (collaborating on the behavioral impact of information carried by spike timing), and Christopher Harvey at Harvard Medical School (collaborating on neural population dynamics and decision making). We are willing to consider candidates interested in any of the aspects of neural coding of interest to the laboratory. Candidates must hold a PhD in a numerate scientific discipline. They must be highly motivated and creative individuals who want to work in a dynamic, multi-disciplinary research environment and be willing to interact with both experimental and theoretical neuroscientists. For reference to recent work of the lab please see Safaai, H., et al (2015) Modelling the effect of Locus Coeruleous firing on single trial cortical state dynamics and sensory processing. Proc. Natl. Acad. Sci. USA 112(41): 12834–12839 Zuo Y, et al (2015) Spike timing and spike rate make complementary contributions to perceptual decisions in rat S1 and S2 cortex. Current Biology 25: 357-363 Besserve M, Lowe S, Logothetis NK, Scholkopf B, Panzeri S (2015) Shifts of gamma phase across primary visual cortical sites reflect dynamic stimulus- modulated information transfer. PLoS Biology 13(9): e1002257. doi:10.1371/journal.pbio.1002257 Panzeri, S, et al (2015) Neural population coding: combining insights from microscopic and mass signals, Trends in Cognitive Sciences 19: 162-172. Interested candidates should contact Dr. Stefano Panzeri ( stefano.panzeri(a)iit.it <https://mail.iit.it/owa/redir.aspx?C=oajclq8rPE6FcMCJ-lI1Hsu55NFH69II9YBluB…>), emailing their CV, a brief statement of research interests, and names and email addresses of 2 or 3 referees.

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Associate Professor Position in Data Sciences (Big Data) at the Norwegian University of Life Sciences
by Hans Ekkehard Plesser July 5, 2016

July 5, 2016

Dear Colleagues! The Department of Mathematical Sciences and Technology (IMT) at the Norwegian University of Life Sciences (NMBU) has a permanent, full-time position available for an Associate Professor in Data Sciences (Big Data). The position is part of the University's programme for the recruitment of top researchers, which aims to recruit young researchers with great potential for NMBU. The position will include: • one PhD Position (3 years) • NOK 0.25 million in operating funds Application deadline is 1 October 2016. For details, please see https://www.jobbnorge.no/en/available-jobs/job/127472/associate-professor-i… Best, Hans Ekkehard Plesser -- Dr. Hans Ekkehard Plesser, Associate Professor Section Head Dept. of Mathematical Sciences and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway Phone +47 6723 1560 Email hans.ekkehard.plesser(a)nmbu.no Home http://arken.umb.no/~plesse

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2 open PhD positions at TU Dresden, Germany
by Stefan Kiebel July 5, 2016

July 5, 2016

The Department of Psychology at the TU Dresden (Chair of Neuroimaging, PI: Stefan Kiebel) is looking for two PhD students. One of the PhD student positions is in experimental cognitive neuroscience, and one is in computational neuroscience. Both PhD positions will be working at the interface between computational and experimental neuroimaging research in cognitive control and goal-directed action sequences. Detailed information about both positions can be found at: http://tiny.cc/8ii4by Both positions are part of the DFG-funded CRC 940 'volition and cognitive control' (http://www.sfb940.de/en/home.html) The CRC was established in 2012 and will be funded for the next four years. The CRC aims at identifying the cognitive and neuronal mechanism, which underlie the volitional control of goal-directed actions as well as its dysfunctions in selected mental disorders. PhD position 1: The candidate will work as a member of a team in the CRC940-Project A9 on computational modelling of goal-directed actions and explaining dynamics in cognitive control in terms of variations in meta-control parameters. In this project, neuroimaging (fMRI and EEG) experiments will be used to test hypotheses derived using advanced Bayesian models of goal-reaching over multiple trials. http://tiny.cc/8ii4by (PhD position Cognitive Neuroscience) PhD position 2: The candidate will work as a member of a team in the CRC940-Project Z2 on advanced computational modelling of functional magnetic resonance imaging and electroencephalography data. In this project, advanced modelling techniques will be developed and extended to enable testing novel experimental hypotheses using fMRI and EEG. http://tiny.cc/8ii4by (PhD position Computational Neuroscience) --> The application deadline for both positions is the 14th of July. -- Prof. Dr. Stefan Kiebel Technische Universität Dresden School of Science Department of Psychology 01062 Dresden, Germany Tel.: +49 (351) 463-43145 Fax: +49 (351) 463-43147

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Research Engineer position - Axion/Columbia
by Robert Butera July 1, 2016

July 1, 2016

Passing this on for a colleague - contact the email address in the job ad for more information. RESEARCH ENGINEER Axion BioSystems, Inc. is a life science company located in Atlanta, GA. We design, develop and manufacture instrumentation used for the precise monitoring and control of excitable cells. Axion’s products are used by pharmaceutical and biotech companies, governmental agencies, and universities for drug safety, drug discovery, and disease modeling assays. We are currently seeking an energetic scientist to lead Axion’s efforts in a collaboration with the Institute for Genomic Medicine (IGM) at Columbia University. The objective of the collaboration is to interrogate the genetic mechanisms of epilepsy with multiwell microelectode array (MEA) technology. The newly developed IGM is an integral part of Columbia University’s Precision Medicine Initiative. As part of this Initiative, the IGM performs comprehensive genomic analyses on more than 10,000 patient genomes per year in order to identify new therapeutic targets of neurodevelopmental disorders including epilepsy. In order to understand how the implicated mutation affects disease, the laboratories of David Goldstein and Michael Boland use both primary neurons from mouse models of epilepsy and neurons derived from human induced pluripotent stem cells to study the effects of epilepsy gene mutations on neurodevelopment and neural network behavior. The selected individual will contribute analytical and electrophysiological expertise to complement a highly skilled team of molecular biologists. JOB FUNCTIONS: • Execution of neurophysiological experiments with the multiwell MEA platform. The ability to interpret or debug experiments in real-time is important, such as distinguishing signal from noise or artifacts. • Analysis of large electrophysiological data sets, primarily from the multiwell MEA platform, but also including complementary in vivo data. • Develop experimental hypotheses and identify expected results. Must confirm results through independent analysis and investigate any discrepancies that appear, while understanding the strengths and limitations of model systems (i.e., cellular vs. animal). • Drive innovation of the multiwell MEA assay, leading to new experimental methods and analytical techniques. • Work well in a fast-paced, collaborative environment at Columbia University in New York City, with an ability to communicate well with the Axion team in Atlanta. REQUIREMENTS: • PhD in neuroscience or neuroengineering. A recent post-doc experience is a plus. • Hands-on research experience with electrophysiology techniques. Specific experience with MEA technology is desired. • Biostatistical and data analysis experience. • Experience with software analysis tools such as MATLAB or MiniTAB. • Attention to detail with good documentation and communication skills. • U.S Citizenship or permanent residency required. USEFUL KNOWLEDGE: The following are not required, but will enhance an applicant’s candidacy. • Hands on experience in general biological protocols including knowledge of primary cell dissociation, cell culture protocols, CRISPR, gene editing, and basic molecular techniques. • Experience with screening technologies, such as high-throughput patch clamp or high content imaging. • Knowledge of pharmacology and neurotoxicity research. CONTACT INFORMATION: If you are interested in this position, please send your resume and cover letter to careers(a)axionbio.com

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CFP: Advances in Biologically Inspired Reservoir Computing
by John Butcher July 1, 2016

July 1, 2016

Dear all, We are seeking papers for a special issue on Reservoir Computing, paper submission deadline is 31st September 2016. For more details see below and http://ispac.diet.uniroma1.it/cognitive-computation-special-issue/ Many thanks *Scope and motivation* *Reservoir computing is a family of techniques for training and analyzing recurrent neural networks, wherein the recurrent portion of the network is assigned before the training process, typically via stochastic assignment of its weights. The non-linear reservoir acts as a high-dimensional kernel space, which generates complex dynamics characterized by sharp transitions between ordered and chaotic regimes. The behavior of this model emulates the functioning of many biological (complex) systems, among which the brain.* *Driven by the conceptual simplicity of the reservoir and by links with neuroscience, computer science and systems’ theory, researchers have achieved remarkable breakthroughs, both in theory and in practice. These include dynamical models for explaining the working behavior of reservoirs, unsupervised strategies for the adaptation of the network, and the design of unconventional computing architectures for its execution.* *The recent upsurge of interest in fully adaptable recurrent networks, far from shifting the attention from the field, has brought renewed interest in reservoir computing models. In our era of extreme computational power and sophisticated problems, it is essential to understand the limits and the potentialities of simple (both deterministic and random) collections of processing units. For this reason, many fundamental questions remain open, including the design of optimal task-dependent reservoirs in a stable fashion, novel investigations on the memory and power capabilities of reservoir devices, and their applicability in an ever-increasing range of domains.* *In light of this, the aim of this special issue is to provide a unified platform for bringing forth and advancing the state-of-the-art in reservoir computing approaches. Researchers are invited to submit innovative works on the theory and implementation of this family of techniques, in order to provide an up-to-date overview on the field.* *Topics* *The topics of interest to be covered by this Special Issue include, but are not limited to:* *-Theoretical analyses on the computational power of reservoir computing.* *-Deep reservoir models.* *-Techniques for the automatic adaptation of the reservoir and the readout.* *-Supervised, unsupervised and semi-supervised training criteria.* *-Non-conventional substrates for the implementations of reservoirs.* *-Parallel and distributed algorithms for reservoir computing.* *-Comparisons between reservoir computing and standard (deep) neural networks.* *-Reservoir computing for reinforcement learning problems.* *-Fundamental links between reservoir computing and neuroscientific findings.* *-Investigation of reservoir dynamic in a phase space of reduced dimensionality.* *-Applicative papers in all areas (including robotics, industrial control, etc.) are welcome, as well as outstanding surveys on specific aspects of the field.* -- Dr John Butcher Neuroscience Research Associate School of Life Sciences Huxley Building Keele University UK ST5 5BG http://www.keele.ac.uk/lifesci/people/researchassistants/johnbutcher/

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