February 2016 - Comp-neuro - lists.cnsorg.org

Neuroimaging Data Analyst Position at the University of Illinois at Chicago (Summer/Fall 2016)
by Sylvia Morelli Feb. 25, 2016

Feb. 25, 2016

Drs. Sylvia Morelli and K. Luan Phan are considering applications for a neuroimaging data analyst to work jointly in the Empathy and Social Connection Lab (http://www.morellilab.com/ <http://www.morellilab.com/>) and the Mood and Anxiety Disorders Research Program (http://www.psych.uic.edu/research/mood-and-anxiety-disorders-research-progr… <http://www.psych.uic.edu/research/mood-and-anxiety-disorders-research-progr…>) at University of Illinois at Chicago. The data analyst will have the opportunity to analyze several data sets related to social cognitive affective neuroscience (e.g., empathy, social networks, reward learning, decision-making, emotional processing and regulation, fear learning and extinction) in healthy and psychiatric populations (e.g., anxiety and depression), as well as pharmacotherapy and psychotherapy treatment response. The analyst will implement the latest approaches developing in the field of imaging neuroscience — including but not limited to pattern classification, prospective prediction, machine learning, effective connectivity, connectomics/graph theory, computational modeling – and train others (faculty, postdocs, graduate students) on conventional and new analysis techniques. The position will involve analyzing data across multiple modalities including self-report, behavior, psychophysiology (SCR, startle), EEG/ERP, DTI, structural MRI and functional MRI – at single-unit and integrated levels. The data analyst will work amongst diverse investigators across disciplines (e.g., neuroscience, psychology, psychiatry). Successful applicants will have a Masters- or PhD-level degree in neuroscience, psychology, computer science, statistics/biostatistics or mathematics and/or a record of extensive experience in analyzing structural and functional MRI data. Applicants should also be fluent in SPM (or AFNI/BrainVoyager), statistical packages (e.g., Matlab and R), and programming languages (e.g., Unix). Excellent communication (verbal and written) and organizational skills are required. The position will be fully funded for at least 2 years with the potential for a longer-term position. We will review applications beginning March 1, until the position is filled. Applicants should send a CV, a statement describing their qualifications for the position and relevant experience, and the names of 2-3 references that may be contacted for a recommendation to Dr. Morelli (smorelli(a)uic.edu <mailto:smorelli@uic.edu>) and Dr. Phan (klphan(a)psych.uic.edu <mailto:klphan@psych.uic.edu>).

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2 Postdoc positions in modeling dendritic integration and synaptic plasticity
by Walter Senn Feb. 25, 2016

Feb. 25, 2016

Dear Colleagues, dear all, We are inviting applications for 2 postdoc positions (for at least 2 years, earliest start April 1, 2016) in computational neuroscience at the University of Bern, Switzerland. Our research is devoted to models of synaptic plasticity and dendritic integration that serve as a computational paradigm for learning in neuronal networks. We have recently suggested to consider “Learning by the dendritic prediction of somatic spiking” (Neuron 2014) that assigns an intrinsic computational task to neurons and dendrites. This concept will be extended by including dendritic nonlinearities and considering networks of such neuronal prediction elements. The 2 positions are: 1) Postdoc for modeling dendritic integration. We have shown how the error-backpropagation algorithm translates to a dendritic tree that displays NMDA-spikes in individual dendritic branches (PLoS Comp Biol 2016). Along these ideas a biophysical model of dendritic integration will be worked out that makes also contact to Bayesian integration. 2) Postdoc for modeling synaptic plasticity and learning in networks. The neurons as intrinsic prediction elements will be connected to form a network that mimics various learning tasks and that is particularly suited for imitation learning. You will profit from generous travel funding and from collaborations in- and outside the Human Brain Project, and you will also enjoy the beautiful city and surrounding of Switzerland’s capital. Ideal candidates have a strong background in computational neuroscience and machine learning. Please send CV, letter of motivation, and publication list by March 11 to Walter Senn (senn(a)pyl.unibe.ch) and Sabine Herzog (herzog(a)pyl.unibe.ch) With best wishes Walter Senn PS: For recent work of our group see http://www.physio.unibe.ch/~senn/neuroscience.aspx

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Call for Applications: Paul J. Salem Postdoctoral Scholars in Computational Brain Science – Brown Institute for Brain Science
by Michael J Frank Feb. 25, 2016

Feb. 25, 2016

The Brown Institute for Brain Science Initiative for Computation in Brain and Mind <http://compneuro.clps.brown.edu> is seeking applicants for the Paul J. Salem Postdoctoral Scholarships in Brain Science. The postdoctoral scholars will lead an exciting new project at the interface between machine learning and neuroscience. In particular, we are looking for computational neuroscientists interested in the application and development of novel machine learning methods for the analysis of neural recordings. Candidates are expected to have a solid background in modern machine learning and signal processing as well as demonstrated interest in experimental neuroscience. In addition, to conducting primary research with large neural data sets, candidates will be involved in the mentoring of students, and the organization of workshops. Candidates are expected to interact closely with the Neurotechnology group within the Brain Institute. The initial appointment is for 12 months with the potential to renew. The salary is competitive. The start date is negotiable although funding is available now. Research group: *The Initiative for Computation in Brain and Mind (ICBM;* <http://compneuro.clps.brown.edu>http://compneuro.clps.brown.edu) which began Fall 2013 within the Brown Institute for Brain Science, creates an exciting intellectual environment that fosters synergistic collaborations across departments. Groups affiliated with the initiative work on two core levels of computation. The first level focuses on theoretical neuroscience, including computational perception, control over action and learning, and fundamental questions in neuronal networks (synaptic plasticity, circuits, networks, oscillations). The second level focuses on applications and neurotechnology, including brain-machine interfaces, advanced neural data analysis, computer vision, computational psychiatry, and robotics. ICBM has 16 core computational faculty (http://compneuro.clps.brown.edu/people/) spanning six departments, and many more faculty who incorporate computation for theory development, analysis, or both. Computational neuroscience tools at Brown have been applied in projects including brain-machine control of robotic arms in paralyzed humans; models of visual systems in biological organisms and their innovative application for classifying animal behavioral patterns; predicting and quantifying effects of genetics, disease, medications, and brain stimulation on motor and cognitive function; identification of the source of neural rhythms and their roles in sensorimotor function; development of fundamental theories of brain plasticity, and learning; state-of-the art models of machine learning and reinforcement learning in computer science. The Brown Institute for Brain Science (BIBS) advances multidisciplinary research, technology development, and training in the brain sciences. BIBS unites more than 100 faculty from departments across Brown, spanning basic and clinical departments, and physical and biological sciences. BIBS creates opportunities and essential support to advance interdisciplinary research efforts among these groups including training funds to support interdisciplinary scholarship and research. Requirements: Candidates must have received their PhDs within 3 years of the application deadline, and will work under the supervision of faculty members at Brown University who are affiliated with the Initiative for Computation in Brain and Mind. They must have a strong background in computational neuroscience and machine learning, with a track record of relevant publications at top venues (such as NIPS or ICML). Excellent programming skills are required (C/C++/Matlab/Python). Application: Please send your applications by email to cbm-info(a)brown.edu. Please include a brief statement of interests, a curriculum vitae, a list of publications and 2-3 letters of reference (addressed directly to cbm-info(a)brown.edu) There is no deadline for the application but applicants are encouraged to apply as soon as possible as the position will be filled as soon as a suitable applicant is found. ------------------------------ Michael J Frank, PhD, Professor Laboratory for Neural Computation and Cognition Brown University http://ski.clps.brown.edu (401)-863-6872

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[IEEE ICCI*CC'16:] RE: IEEE ICCI*CC'16 Update
by Yingxu Wang Feb. 24, 2016

Feb. 24, 2016

Dear Colleague, The 15th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC'16) will be held at Stanford University, CA, USA during Aug. 22-23, 2016 (http://www.ucalgary.ca/icci_cc/iccicc-16) The IEEE ICCI*CC series is a flagship conference of its field sponsored by IEEE Computer, Computational Intelligence, and SMC Societies. It forms a transdisciplinary field that studies the internal information processing mechanisms of the brain, the underlying abstract intelligence (aI) theories and denotational mathematics, and their engineering applications in cognitive computing, computational intelligence, and cognitive systems. Paper submission is via Easy Chair at: https://www.easychair.org/conferences/?conf=iccicc2016. Submission deadline is approaching on Feb. 29, 2016. Special session papers may get a grace period by coordination of the session chairs. The conference will be featured by a number of keynote speeches that highlight the cutting-edge theories and novel technologies for cognitive systems and computational intelligence inspired by the brain such as: a) Natural Language Understanding vs. Natural Language Precisiation Prof. Lotfi A. Zadeh, UC Berkeley, USA b) Hebbian Learning and the LMS Algorithm Prof. Bernard Widrow, Stanford \university, USA c) Deep Learning and Deep Thinking by Cognitive Robots and Computational Intelligent Systems Prof. Yingxu Wang, University of Calgary, Canada You are welcome to submit a paper to IEEE ICCI*CC'16 or to organize a special session related to the theme of the conference. The Proceedings of ICCI*CC'16 will be published by IEEE CS Press and indexed by EI, DBLP, and IEEE eXplore. Selected papers from the proceedings will be published in leading international journals and IEEE transactions. We look forward to having an opportunity to meet you at IEEE ICCI*CC'16. Best regards, Prof. Yingxu Wang IEEE ICCI*CC'16 Co-Chair _______________________________________________ This message was sent to all subscribers of ieeeicci13-L To unsubscribe, see instructions at: http://www.ucalgary.ca/it/email/mailman E-mail: ieeeicci13-L(a)mailman.ucalgary.ca Homepage: http://mailman.ucalgary.ca/mailman/listinfo/ieeeicci13-l

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1st Call for Abstracts: the 8th Int'l Workshop on Guided Self-Organization (GSO-2016, Cancun, Mexico, 7 July 2016)
by Joseph Lizier Feb. 24, 2016

Feb. 24, 2016

Dear All, This is an announcement and Call for Abstracts for the Eighth International Workshop on Guided Self-Organization (GSO-2016) to be held on July 7, 2016 at ALIFE XV in Cancun, Mexico: http://guided-self.org/gso8/ The GSO Workshop will bring together invited experts and researchers in self-organizing systems, with particular emphasis on the information- and network-theoretic foundations of GSO and the information dynamics of complex systems. The following topics are of special interest: information-theoretic measures of complexity, nonlinear dynamics, complex networks, information-driven self-organization (IDSO), applications of GSO to complex networks, distributed computation, machine learning, swarm intelligence, bio-inspired systems, computational neuroscience, systems biology, cooperative and modular robotics, etc. Submissions to the workshop are extended abstracts (one page). If interested in participating, please send an extended abstract (by May 29, 2016) by email to: prokopenko.mikhail(a)gmail.com The notifications are expected by June 10, 2016. Authors of accepted submissions will present the content to the workshop. We expect that selected papers of GSO-2016 will be published in a special issue (research topic) of Frontiers of Robotics and AI, or Artificial Life journals. Please consult above website for more information. Best wishes, Mikhail Prokopenko Carlos Gershenson Daniel Polani <apologies for possible cross-posting>

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Postdoc in Bordeaux, France. Application deadline 01/04/2016.
by Nicolas P. Rougier Feb. 24, 2016

Feb. 24, 2016

Postdoc in Bordeaux, France at the Neurodegenerative Diseases Institute <http://www.imn-bordeaux.org/en/> Application deadline 01/04/2016. Details & application at INRIA website <http://www.inria.fr/en/institute/recruitment/offers/post-doctoral-research-…>. Actor–critic models of the basal ganglia We developed a model of the basal ganglia (Piron et al., 2016) that introduces an action selection mechanism that is based upon the competition between a positive feedback through the direct pathway and a negative feedback through the hyperdirect pathway. The model also exploits the parallel organization of circuits between the basal ganglia and the cortex using segregated loops: one loop allows to choose the cue and one loop allows to make the actual motor selection. Learning occurs between the cognitive cortex and the cognitive striatum using a simple reinforcement learning where the values of the different cues are updated after each decision. As in most computational models of the basal ganglia, this model relies on an actor-critic architecture where the dopamine signal is used to encode the temporal difference prediction error signal in the critic (Joel et al., 2002; Khamassi et al., 2005). However, this algorithm is not very elaborated and its implementation is not biologically plausible since values are stored outside the model. The objectives of this postdoc is thus to review and to re-implement (Python) main actor-critic models of the literature in order to compare them on a common set of decision tasks (two-arm bandit task for example) in terms of biological plausibility and performances. Special attention will be given to the "Primary Value and Learned Value Pavlovian Learning Algorithm" model (O'Reilly, 2007) and the AGREL model (Roefselma et al. 2005). From these replications (that will be published in ReScience <http://rescience.github.io/>), the most plausible and compatible mechanisms will be implemented in our own model of the basal ganglia in order to replace the current reinforcement learning algorithm (Guthrie et al., 2013, Piron et al. 2016). Profiles PhD in computational neuroscience or computer science (machine learning) with strong experience in Python & the scientific Python stack (numpy, scipy, matplotlib, git/github). References • Joel, D., Niv, Y., & Ruppin, E. (2002). Actor–critic models of the basal ganglia: New anatomical and computational perspectives. Neural Networks, 15(4-6), 535–547. • Khamassi, M., Lachèze, L., Girard, B., Berthoz, A., Guillot, A. (2005). Actor-critic models of reinforcement learning in the basal ganglia: From natural to artificial rats. Adaptive Behavior, 13 (2). • Guthrie, M., Leblois, A., Garenne, A., & Boraud, T. (2013). Interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study. Journal of Neurophysiology, 109(12). • C. Piron, D. Kase, M. Topalidou, M. Goillandeau, H. Orignac, T. N'Guyen, N.P. Rougier, T. Boraud, The globus pallidus pars interna in goal oriented and habitual behavior. Resolving an old standing paradox, Movement Disorders, (2016), to appear. • O'Reilly, R. C., Frank, M. J., Hazy, T. E., & Watz, B. (2007). PVLV: The Primary Value and Learned Value Pavlovian Learning Algorithm. Behavioral Neuroscience, 121(1). • Roelfsema, P.R., van Ooyen A. (2005). Attention-gated reinforcement learning of internal representations for classification. Neural Computation. Nicolas P. Rougier

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Postdoc and PhD positions and further opportunities in the Bernstein group “Neural network dynamics and computation” (Raoul-Martin Memmesheimer)
by Memmesheimer, Raoul M. Feb. 23, 2016

Feb. 23, 2016

Postdoc and PhD positions and further opportunities in the Bernstein group “Neural network dynamics and computation” (Raoul-Martin Memmesheimer) There are fully funded positions available at the PhD and at the postdoctoral researcher level in my future group at FIAS Frankfurt, with an envisaged starting date of July 2016. At an earlier career stage, you can write your Bachelor’s or Master’s thesis, or do a short-term internship to get to know research in theoretical neuroscience/neurophysics. Depending on the project and the need, financial support is available. Optimal backgrounds of applicants include theoretical neuroscience and/or theoretical physics, computer science, mathematics, quantitative biology. Under the overarching topic “Bridging the scales: Spiking in artificial and biological neural computation”, we aim to develop spiking networks for the solution of artificial intelligence problems and we aim to understand healthy and pathological spike patterns. Your theoretical work in the group may delineate principles underlying effective computations with spiking neurons, investigate the origins of pathological dynamics in the hippocampus, or explore the impact of coupling nonlinearities found in this brain region. Some of the planned projects are directly guided by recent experimental results and aim at explanations that have been requested by experimentally working collaborative partners. Others have a higher level of abstraction and explore rather principal questions. For the different projects, collaborative partnerships have been arranged with experimentally working scientists at Heidelberg University (group of A. Draguhn) and Bonn University (group of H. Beck), and with theoretically working groups at Columbia University (group of L. Abbott), Radboud Unversity (group of H.J. Kappen) and FIAS Frankfurt (group of Jochen Triesch). Of course I am also happy to discuss your own ideas for your future research in the group. If you are interested in more information, see my website (www.memmesheimer.net) contact me via email (rm3354(a)cumc.columbia.edu) at CoSyne 2016, or, if you are in the neighborhood, drop by at my office at Columbia University. -- Raoul-Martin Memmesheimer Center for Theoretical Neuroscience, Columbia University, New York, USA Web: www.memmesheimer.net Phone: +1 (646) 774 7329 Email: rm3354(a)cumc.columbia.edu<mailto:rm3354@cumc.columbia.edu>

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2016 Summer workshop on the dynamic brain: applications open
by Adrienne Fairhall Feb. 23, 2016

Feb. 23, 2016

View online Announcing the Summer Workshop on the Dynamic Brain, co-hosted by the Allen Institute for Brain Science and the Computational Neuroscience Program at the University of Washington and directed by Drs. Christof Koch and Adrienne Fairhall. This intensive, two-week, projects-based, interdisciplinary course aims to give advanced students in neuroscience, biology, physics, engineering and computer science a rapid introduction to the current state of understanding of the neurobiology of sensory processing, including anatomy, physiology and neural coding. The workshop will include data analyses, Python and other software boot-camps, and lectures, taught by faculty from the University of Washington and the Allen Institute for Brain Science, on topics focused on the mammalian cortex and closely associated satellite structures. Subscribe | Unsubscribe | Feedback | Privacy Policy Copyright ©2016. Allen Institute for Brain Science. All Rights Reserved.

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Post-doctoral Research Fellow - Computational Modelling of Deep brain Stimulation for Parkinson's disease
by Olga Grant Feb. 23, 2016

Feb. 23, 2016

Post-doctoral Research Fellow - Computational Modelling of Deep brain Stimulation for Parkinson's disease Highly motivated candidates are invited to join an exciting new project to develop multiscale models of the neuromuscular system for closed-loop deep brain stimulation (DBS). We are seeking a post-doctoral research fellow to develop models of the electric field around the implanted electrode during deep brain stimulation and its interaction with surrounding neurons. Despite the success of DBS, the mechanisms by which it works are not yet understood. This project aims to address this question and to design closed-loop control strategies for DBS through a combination of computational modelling and experimentation. The project represents an exciting opportunity for enthusiastic candidates to work on a new multidisciplinary research project funded by the European Research Council (ERC). The Neuromuscular Systems and Neural Engineering Lab is a multidisciplinary research group led by Prof. Madeleine Lowery in the School of Electrical and Electronic Engineering at University College Dublin. Our research involves applying engineering principles, in particular mathematical modelling, signal analysis and experimentation, to understand how the nervous system controls muscle in healthy and diseased states. Through this research we aim to improve our understanding of the neuromuscular system to address fundamental questions in the control of human movement and to develop improved therapeutic and rehabilitation strategies. Candidates should hold a PhD in Biomedical Engineering, Electrical/Electronic Engineering or a related discipline, with prior experience in electromagnetic finite element analysis and/or computational neural modelling. Excellent communication, analytical and programming skills are required. Full details of the position and the application process are available through the UCD Human Resources website ( <https://www.ucd.ie/hr/jobvacancies/> https://www.ucd.ie/hr/jobvacancies/) . Informal enquiries may be made to Prof. Madeleine Lowery UCD School of electrical Electronic & Communications Engineering University College Dublin Belfield Dublin 4 Ireland madeleine.lowery(a)ucd.ie Tel. +353 1 716 1911 ____________________________________ Dr Olga M Grant Research Project Manager School of Electrical & Electronic Engineering Tel +353 (1) 7161778

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Postdoctoral position in computational neuroscience (sensory processing & decision-making)
by Ralf Haefner Feb. 23, 2016

Feb. 23, 2016

A postdoctoral position is available in the computational neuroscience group of Ralf Haefner in the department for Brain & Cognitive Sciences at the University of Rochester (NY). The focus of our group is on understanding the neural basis of perceptual decision-making. Based on the assumption that the brain performs probabilistic inference using an internal model of the world, we ask questions such as: What is the nature of the internal model? How is it learnt? How are posterior beliefs represented by spiking responses of populations of neurons? What algorithm does the brain use to compute its beliefs? How do cognitive priors influence sensory processing? What is the role of attention in a probabilistic inference framework? What is the role of feedback connections to sensory areas more generally? In order to answer these questions, we perform studies ranging from purely theoretical at the interface of neuroscience and machine learning, to neural data analysis, to psychophysics. Our collaborations with electrophysiology labs inside our department (Greg DeAngelis, Jude Mitchell) and externally (e.g. Born lab at Harvard Medical School, Cumming lab at NIH) allow for the testing of theoretical models using state-of-the-art cortical population recordings in behaving animals and, if desired, the opportunity to record your own data. We also collaborate with the theoretical groups of Jakob Macke (Caesar Institute, Bonn) and Stefano Panzeri (IIT in Trieste). For past work indicative of future directions see... Haefner et al. NatNeuro 2013: http://www.nature.com/neuro/journal/v16/n2/full/nn.3309.html Smolyanskaya et al. Neuron 2015: http://www.cell.com/neuron/abstract/S0896-6273(15)00561-9 Haefner et al. Neuron 2016 (accepted): http://arxiv.org/abs/1409.0257 …and our presentations at the upcoming COSYNE 2016: Poster II-17 (Main Meeting, Fri): Chicharro et. al at Poster III-1 (Main Meeting, Sat): Lange et al. at Talk in “choice-related feedback” Workshop (Tue), Haefner et al. We’re looking for a motivated candidate with strong mathematical and programming skills. The ideal candidate would also be familiar with (or quick to pick up) machine learning techniques, especially relating to inference in probabilistic models, and/or any relevant neuroscience background. The successful candidate with join a highly collaborative department with a strong tradition in probabilistic modeling across a wide range from sensory processing to linguistics (https://www.bcs.rochester.edu/research/index.html <https://www.bcs.rochester.edu/research/index.html> ). The city of Rochester is located on the shore of Lake Ontario in upstate New York on the edge of the finger lakes region, and has a high quality of life (http://en.wikipedia.org/wiki/Rochester,_New_York <http://en.wikipedia.org/wiki/Rochester,_New_York> ). Start date and duration are flexible. Please send applications including a brief research statement, a CV and names of 2-3 references by email. I'll be at COSYNE for both conference and workshops and available to meet there in person. Ralf Haefner Assistant Professor Brain & Cognitive Sciences University of Rochester (NY) rhaefner(a)bcs.rochester.edu <mailto:rhaefner@bcs.rochester.edu>

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