- Comp-neuro - lists.cnsorg.org

Columbia+Janelia postdoc positions: zebrafish computational neuroscience and machine learning
by Liam Paninski 28 Jul '14

28 Jul '14

Colleagues, Will you please circulate this postdoc ad amongst your students or anyone else who might be interested: http://grossmancenter.columbia.edu/zebrafish.html? Thank you. Liam Paninski ------------------------------------------------------------------------------------------ Postdoctoral position in large-scale computational neuroscience A full-time postdoctoral position is available immediately, jointly between Columbia University's Grossman Center for the Statistics of Mind and HHMI's Janelia Research Campus. The aim is to understand whole-brain activity at the cellular level using large-scale computational methods. The position is co-supervised by Liam Paninski, John Cunningham and Larry Abbott (at Columbia) and Jeremy Freeman and Misha Ahrens (at Janelia Research Campus) and is centered around understanding the dynamics of all neurons in the brain of behaving larval zebrafish. We use light-sheet imaging to record simultaneously from most of the ~10^5 neurons during behavior, and use large-scale cluster computing and machine learning, statistics, and modeling to gain insight into how brain-wide network dynamics underlies behavior. The position will be based at Columbia University and HHMI Janelia Research Campus, and we will encourage applicants to spend time at both campuses. Requirements: Qualifications include primarily a strong research portfolio in computational neuroscience, statistical neuroscience, machine learning, or a related field. Backgrounds in analyzing large neural datasets, modeling complex neural systems, and in cutting-edge machine learning will be particularly valuable. These positions are highly quantitative and highly interdisciplinary; applicants should have a PhD in Electrical Engineering, Statistics, Machine Learning, Physics, Applied Mathematics, or Computational Neuroscience. Appointment: The initial appointments will be for one year, and are renewable. Salaries will be set based on experience and skills. Applicants should send an email to zebrafish.computation(a)gmail.com providing: 1. a one-page description of past research experience 2. a one-page description of future research interests and goals 3. a resume of educational and research experience, including publications 4. names of three people who could provide letters of reference All materials should be in pdf or plain text. Applications will be reviewed as they arrive until the positions are filled; interested candidates are encouraged to express their interest early. Due to demand, we may not be able to reply to all applications.

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ONLY FOR STUDENTS - 2 weeks mini computational neuroscience freelance project for students from home
by Robert Schindle 28 Jul '14

28 Jul '14

Hello All, We are currently working on a project in the field of computational neuroscience which requires some help with analyzing data and documenting the research within the next 2 weeks. We are looking for someone with good c++ and Python skills. You will be compensated accordingly. If you are interested please contact us immediately so that we can discuss other details and start the work immediately. Thanks a lot, Robert Schindle.

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Postdoctoral Position- Arizona
by Jean-Marc 26 Jul '14

26 Jul '14

The Computational and Experimental Neuroscience Laboratory is looking to fill a position for a Postdoctoral Research Fellow. The research project is geared towards understanding the role of awake hippocampal replay in spatial navigation. The techniques used include high density electrophysiology in the behaving rat. The project is multidisciplinary and involves collaboration with a robotics team (University of Florida, Tampa) and a computational neuroscience team (INSERM, Lyon, France). Good communication skills are necessary. This position is appropriate for a computational neuroscientist with some experimental background interested in branching out to experimental neuroscience. Applicants with a subset of the following skill set are strongly encouraged to apply: - Scientific expertise or interests in hippocampus and/or prefrontal-cortex - Electrophysiology (in vivo chronic behaving or in vitro) - Behavioral Neuroscience (rat training/testing) - General laboratory skills (e.g. histology, surgery) - Matlab programming and spike-data analyses Applicant should send a cover letter, CV and the names of at least 2 references to Dr. Jean-Marc Fellous (fellous(a)email.arizona.edu) The search is ongoing and is projected to be completed by October 30th, 2014. Information about the laboratory can be found at: http://amygdala.psychdept.arizona.edu/lab.html The CENL and the University of Arizona are committed to maintaining a diverse and inclusive work environment and women and minorities are encouraged to apply. The University of Arizona is an equal opportunity, affirmative action employer.

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Postdoctoral positions: Memory, motor, & learning dynamics
by Mark Goldman 25 Jul '14

25 Jul '14

Two theoretical postdoctoral positions and 1-2 collaborative experiment-theory positions are available in the laboratory of Dr. Mark Goldman at the University of California at Davis.The lab works on a broad range of problems in computational neuroscience ranging from neural coding to dynamics and plasticity of single neurons and networks. Immediate funding is available for a range of projects related to working memory, neural integration, motor learning, and synaptic plasticity as described below.The postdoctoral candidate also would have flexibility to work on a range of issues of his or her choosing.Candidates are expected to have strong training in an analytically rigorous discipline such as theoretical neuroscience, physics, mathematics, computer science, or engineering.The postdoctoral candidate will have ample opportunity to interact within the vibrant computational and systems neuroscience communities at UC Davis and the greater San Francisco Bay Area. Candidates should send a CV, brief statement of previous research and future research interests, and contact information for three references to: Mark Goldman, msgoldman(a)ucdavis.edu. Specific positions with funding include: 1) *Theoretical positions:Dynamics of memory and motor-related neural activity* /Challenging the attractor picture of working memory/.In the traditional attractor picture of working memory, memory storage results from positive feedback processes that lead to the formation of self-sustained attractors.In one project, we are exploring how functionally feedforward, rather than feedback, network architectures can generate flexible codes for storing memories and producing a broad range of input-output transformations.In a second project, we are utilizing methods from engineering control theory to show how balanced cortical networks can utilize negative feedback to stabilize persistent patterns of neural activity. /Multi-scale modeling of neural integration./The oculomotor neural integrator, which transforms eye-velocity encoding motor commands into eye-position encoding commands, is a model system for understanding the mathematical integration of inputs and the maintenance of memory-storing activity.We seek to determine the respective roles of cellular and circuit mechanisms of memory storage in this system.Multi-scale models, from ion channels to behavior, will be generated based upon electrophysiological and optical imaging recordings and optogenetic manipulations performed in our experimental collaborators' laboratories. /Context-dependent memory storage./Recent experiments suggest that the oculomotor neural integrator functions in a context-dependent manner, producing spatially distinct activity patterns depending upon whether eye movements are being made in the context of rapid changes in gaze (saccades) versus smooth tracking of moving objects.We seek to determine the circuit mechanisms underlying this context-dependent activity and to propose general frameworks, applicable to both oculomotor and cortical memory systems, for how multiple context-dependent inputs can be separately stored in a single network. /Role of the granule cell layer in cerebellar motor learning./The eye movement system provides a highly tractable setting for studying motor learning because it is well-characterized experimentally and has fewer degrees of freedom than more complicated movement systems.In collaboration with whole-circuit optical imaging and optogenetic perturbation experiments in the Aksay laboratory at Cornell Medical University and genetic manipulations, electrophysiological recording, and optogenetic perturbations in Jennifer Raymond's laboratory at Stanford University, we are modeling the neural dynamics and coding of cerebellar granule neurons and their relation to Purkinje cell firing and the plasticity of eye movement behaviors. 2) *Experimental positions:* *Development, dynamics, and plasticity of neural networks: *In collaboration with Kim McAllister (kmcallister(a)ucdavis.edu <mailto:kmcallister@ucdavis.edu>), we are seeking to understand the learning rules underlying development and learning in neural networks. We will use cutting-edge technology in patterned substrates, optogenetics, and uncaging in a novel long-term imaging assay for synapse dynamics that allows recording and single synapse or single cell manipulation of neuronal and network activity. These cultured networks will be used to directly test central tenets of Hebbian, spike-timing dependent, and homeostatic learning over development. The applicant will perform these imaging experiments as well as apply theoretical models to better understand and predict our results. *Dynamics of memory and motor-related neural activity:*There are also possibilities for joint experimental-theoretical work with Emre Aksay's laboratory at Weill Medical College of Cornell University (in New York City) on either oculomotor memory storage or cerebellar motor learning, using /in vivo/ two-photon optical recording and stimulation to dissect circuit function and plasticity.Interested candidates should contact Dr. Aksay at ema2004(a)med.cornell.edu <mailto:ema2004@med.cornell.edu>.

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PhD Position in Brain-Inspired Computing (Theory)
by Mihai A. Petrovici 25 Jul '14

25 Jul '14

The Kirchhoff-Institut für Physik at the Ruprecht-Karls-Universität Heidelberg (Germany) has an immediate opening for a * PhD Position in Brain-Inspired Computing (Theory)* The position is located in the Electronic Vision(s) Lab of Prof. Karlheinz Meier. The group develops large-scale hardware for brain-inspired computing and investigates novel theoretical paradigms of neural computation. The Heidelberg group works on the development of brain-inspired electronic circuits, so-called neuromorphic systems. These systems implement physical models of neurons and synapses in-silico, with fully configurable parameters and connectivity. Throughout a series of international projects, the hardware systems have evolved from single chips to wafer-scale devices with hundreds of thousands of neurons (Schemmel et al., 2010) and are planned to be further scaled up in the framework of the EU Human Brain project (HBP). The configurability of these systems fosters their use as general-purpose emulation devices for neuroscientific research (Pfeil et al., 2013). Their intrinsic parallelism, low power consumption and high acceleration sets them apart from conventional computing architectures. The heterogeneity of neuromorphic substrates requires novel theoretical approaches (Petrovici et al., 2014). The Heidelberg group is also involved in theoretical research and modeling of spike-based computational principles. In particular, the use of stochasticity for Bayesian inference is a major focus. These models provide useful architectures for machine learning, while at the same time advancing the study of possible computational paradigms in the mammalian neocortex (Petrovici et al., 2013). The successful candidate will join the theory and modeling section of the group in developing new stochastic models that exploit the heterogeneity of the hardware substrate, thereby benefitting from its significant speed advantage over conventional simulators. ------------------------------------------ Candidates are expected to hold a Masters degree or equivalent in Physics, Mathematics, Computer Science or a related discipline. Knowledge in neuroscience is not required but would be an advantage. The candidates should have a deep interest in theoretical and computational neuroscience, as well as excellent mathematical abilities. An important part of their work will consist in acquiring new skills from various fields of neuroscience, as required by the interdisciplinary nature of the research topic. The successful candidates will use complex simulation software and are therefore required to have very good programming skills. ------------------------------------------ Applications should addressed to Prof. Karlheinz Meier (meierk(a)kip.uni-heidelberg.de) and Mihai Petrovici (mpedro(a)kip.uni-heidelberg.de) including the following information in a single pdf-file : - Statement of research - CV - Two names (with email addresses) of suggested referees - Copies of university degrees and additional certificates - URLs of master /diploma) thesis and related publications

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CNS*14 Tutorial on Neuronal Model Parameter Search Techniques
by Cengiz Günay 24 Jul '14

24 Jul '14

Dear colleagues, You are invited to attend our shortly upcoming full-day tutorial session at CNS*2014 in Quebec City. *Where:* Organization for Computational Neurosciences (OCNS) Conference, Quebec City Conference Center, Room 2105 *When:* Saturday July 26, 2014 between 9am - 4:30pm *T9: Neuronal Model Parameter Search Techniques* Parameter tuning of model neurons to mimic biologically realistic activity is a non‐trivial task. Multiple models may exhibit similar dynamics that match experimental data – i.e., there is no single “correct” model. To address this issue, the ensemble modeling technique proposes to represent properties of living neurons with a set of neuronal models. Several approaches to ensemble modeling have been proposed over the years, but the two most prevalent parameter tuning methods are systematic “brute‐force” searches [1, 2] and various evolutionary algorithms‐based techniques [3, 4, 5, 6]. Both approaches relay on traversing a very large parameter space (with thousands to millions of model instances), but utilize diametrically different ways to accomplish that. In both cases, however, entire collections of biologically realistic models are generated, whose neural activity characteristics can then be cataloged and studied using a database [1, 2]. The tutorial covers “tips and tricks,” as well as various pitfalls in all stages of model construction, large‐scale simulations on high performance computing clusters [S2], database construction and analysis of neural data, along with a discussion about the strengths and weaknesses of the two parameter search techniques. We will review software implementations for each technique: PANDORA Matlab Toolbox [7][S1] for the brute force method and NeRvolver (i.e., evolver of nerve cells) for evolutionary algorithms. PANDORA was used in recent projects for tuning models of rat globus pallidus neurons [2][M1], lobster pyloric network calcium sensors [8][M2], leech heart interneurons [9][M3,S3] and hippocampal O‐LM interneurons (Skinner Lab, TWRI/UHN and Univ. Toronto). NeRvolver is a prototype of a computational intelligence‐based system for automated construction, tuning, and analysis of neuronal models, which is currently under development in the Computational Intelligence and Bio (logical) informatics Laboratory at Delaware State University [10]. Through the utilization of computational intelligence methods (i.e., Multi‐Objective Evolutionary Algorithms and Fuzzy Logic), the NeRvolver system generates classification rules describing biological phenomena discovered during the process of model creation or tuning. Thus in addition to producing neuronal models, NeRvolver provides–via such rules–insights into the functioning of the biological neurons being modeled. In the tutorial, we will present basic functionalities of the system and demonstrate how to analyze the results returned by the software. We will allocate enough time for Q&A and if participants bring a laptop pre‐loaded with Matlab, they can follow some of our examples. *Lecturers/Organizers:* Cengiz Günay, Anca Doloc-Mihu (Emory University, USA) Vladislav Sekulić (University of Toronto, Canada), Tomasz G. Smolinski (Delaware State University, USA) *References * [1] Astrid A. Prinz, Cyrus P. Billimoria, and Eve Marder. Alternative to hand‐tuning conductance‐based models: Construction and analysis of databases of model neurons. J Neurophysiol, 90:3998–4015, 2003. [2] Cengiz Günay, Jeremy R. Edgerton, and Dieter Jaeger. Channel density distributions explain spiking variability in the globus pallidus: A combined physiology and computer simulation database approach. J. Neurosci., 28(30):7476–91, July 2008. [3] Pablo Achard and Erik De Schutter. Complex parameter landscape for a complex neuron model. PLoS Comput Biol, 2(7):794–804, Jul 2006. [4] Tomasz G. Smolinski and Astrid A. Prinz. Computational intelligence in modeling of biological neurons: A case study of an invertebrate pacemaker neuron. In Proceedings of the International Joint Conference on Neural Networks, pages 2964–2970, Atlanta, GA, 2009. [5] Tomasz G. Smolinski and Astrid A. Prinz. Multi‐objective evolutionary algorithms for model neuron parameter value selection matching biological behavior under different simulation scenarios. BMC Neuroscience, 10(Suppl 1):P260, 2009. [6] Damon G. Lamb and Ronald L. Calabrese. Correlated conductance parameters in leech heart motor neurons contribute to motor pattern formation. PLoS One, 8(11):e79267, 2013. [7] Cengiz Günay, Jeremy R. Edgerton, Su Li, Thomas Sangrey, Astrid A. Prinz, and Dieter Jaeger. Database analysis of simulated and recorded electrophysiological datasets with PANDORA’s Toolbox. Neuroinformatics, 7(2):93–111, 2009. [8] Cengiz Günay and Astrid A. Prinz. Model calcium sensors for network homeostasis: Sensor and readout parameter analysis from a database of model neuronal networks. J Neurosci, 30:1686–1698, Feb 2010. NIHMS176368,PMC2851246. [9] Anca Doloc‐Mihu and Ronald L. Calabrese. A database of computational models of a half‐center oscillator for analyzing how neuronal parameters influence network activity. J Biol Phys, 37(3):263–283, Jun 2011. [10] Emlyne Forren, Myles Johnson‐Gray, Parth Patel, and Tomasz G. Smolinski. Nervolver: a computational intelligence‐based system for automated construction, tuning, and analysis of neuronal models. BMC Neuroscience, 13(Suppl 1):P36, 2012. -Cengiz -- Cengiz Gunay Postdoctoral Fellow, Dept. of Biology Visiting Faculty, Dept. of Math & CS Emory University cgunay(a)emory.edu http://www.biology.emory.edu/research/Prinz/Cengiz/

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Announcement: GENESIS 2.4 beta tutorial at CNS
by Hugo Cornelis 23 Jul '14

23 Jul '14

This is a public announcement of the GENESIS-2.4 tutorial at the Computational Neuroscience meeting (CNS) in Quebec City and the official public beta release of GENESIS 2.4. This new release includes support for spike-timing dependent plasticity in large networks of multicompartmental cells, bug fixes, comes with new python utilities and tutorial materials. The "Ultimate GENESIS Tutorial Distribution", included in this release, contains updated course materials for the tutorial Constructing biologically realistic neuron and network models with GENESIS. The tutorial brings together material that is available as separate downloads from the GENESIS web site and through the GENESIS Users Group. It includes several simulator-independent tutorials on biologically realistic neural modeling, as well as both tutorial and research simulations that have been implemented with GENESIS. The latest version of this GENESIS tutorial distribution will be available at http://genesis-sim.org/GENESIS/UGTD.html. The GENESIS tutorial at the CNS meeting is introductory and aimed at people who are new to or have only elementary knowledge about the GENESIS-2 simulator, as well as those who have used GENESIS in the past and would like to learn of new developments in cortical network modeling with GENESIS. * introduction and overview ** Why are we doing this? ** resources ** installation and configuration overview * single neuron modeling and visualization * debugging model and script * fast simulations with hsolve * network modeling * spike-timing dependent plasticity During the last hour of the tutorial, I will show in detail how to technically debug models, scripts and source code, at various levels of implementation. The latest news can always be found at http://genesis-sim.org/GENESIS/ -- Hugo -- Hugo Cornelis Ph.D. GENESIS-3 -- lead architect http://www.genesis-sim.org/ Neurospaces Project Architect http://www.neurospaces.org/

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Brian tutorial at CNS meeting in Québec City
by Marcel Stimberg 23 Jul '14

23 Jul '14

**Brian tutorial at CNS meeting in Québec City** Everyone interested in simulating spiking neural networks is invited to join us for a tutorial on the "Brian simulator": 9am-4.30pm, Saturday, 26th July 2014 in Québec City The tutorial is part of the OCNS meeting's tutorials (http://www.cnsorg.org/cns-2014-tutorials) and takes place in room 2101 of the Québec City Conference Center. It will demonstrate the use of Brian 2, a major update of the Brian simulator which further improves its flexibility and adds exciting new code generation abilities. We will provide an introduction to Brian in the morning (no prior knowledge of Brian necessary) and discuss more advanced topics (including the new code generation features) in the afternoon. We recommend participants to install the current version of Brian 2 beforehand; installation instructions, the schedule and additional material can be found here: http://briansimulator.org/brian-tutorial-at-cns-2014/ Best regards, Marcel Stimberg

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Faculty Position in Computational Neuroscience at the University of California, Irvine
by Jeff Krichmar 22 Jul '14

22 Jul '14

Dear Computational Neuroscientists, The following job position may be of interest to many of you. ************************** University of California, Irvine Faculty Position in Computational Neuroscience The Department of Cognitive Sciences (www.cogsci.uci.edu) at the University of California, Irvine (UCI) invites applications for a faculty position at the Associate or Full Professor level. We are especially interested in candidates who use mathematical, computational, or robotics approaches to study the neural basis of cognition in any of these areas: (1) vision, hearing, and attention; (2) memory and decision-making; (3) learning and development; (4) language. Applicants whose research relates to human behavior are preferred. A strong record of publications and extramural funding is essential. Exceptional candidates at the Assistant Professor level will also be considered. The online application includes: A cover letter, CV, research and teaching statements, 3 recent publications, and contact information for 3-5 referees. Interested candidates can apply for the position at: https://recruit.ap.uci.edu/apply/JPF02452. To ensure full consideration, please complete the application by November 15, 2014. The University of California, Irvine is an Equal Opportunity/Affirmative Action Employer committed to excellence through diversity. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age, protected veteran status, or other protected categories covered by the UC nondiscrimination policy. ************************** Best regards, Jeff Krichmar Department of Cognitive Sciences 2328 Social & Behavioral Sciences Gateway University of California, Irvine Irvine, CA 92697-5100 jkrichma(a)uci.edu http://www.socsci.uci.edu/~jkrichma

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GENESIS 2.4 beta release and modeling tutorials
by Dave Beeman 22 Jul '14

22 Jul '14

There is now a stable public beta release of GENESIS 2.4 available on the web site for the GENESIS neural simulator. Although there have been development releases of GENESIS 3, this is the first new release of GENESIS 2 since the March 2006 release of GENESIS 2.3. This new release was made necessary by the many improvements and user contributions over the past few years, and the incorporation of new objects for efficiently modeling synaptic plasticity in large networks of realistic multicompartmental cells. The final release will be in November 2014. There is also a new July 2014 version of the "Ultimate GENESIS Tutorial Distribution", a complete self-paced GENESIS modeling course, combined with GENESIS binary and source distributions and installation instructions. It provides an easy way to get everything by downloading a single package. This was prepared as course materials for the tutorial "Constructing biologically realistic neuron and network models with GENESIS" to be given on July 26th at CNS 2014. These may be downloaded from the GENESIS 2 home page at http://genesis-sim.org/GENESIS/ Those who wish to follow or contribute to the development of the final release can get the latest versions at the github repository: https://github.com/dbeeman/genesis-2.4beta-files Dave Beeman ------------------------------------------------------------ Prof. David Beeman, GENESIS Users Group Dept. of Electrical, Computer, and Energy Engineering University of Colorado, Boulder, CO 80309 email: genesis(a)genesis-sim.org - dbeeman(a)colorado.edu GENESIS 2 WWW site: http://genesis-sim.org/GENESIS/ ------------------------------------------------------------

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