July 2014 - Comp-neuro - lists.cnsorg.org

Post-doc openings for theoretical neuroscience at UCSF and UCB

by Friedrich Sommer

Post-doctoral fellow in computational/theoretical neuroscience The Chang Lab at UCSF and the Sommer Lab at UCB are jointly seeking two (2) post-doctoral fellows in computational/theoretical neuroscience. Our groups are developing and applying novel computational methods to understand population neural data. The positions have the specific focus on understanding the dynamics of neural population data collected directly from the cortical surface of humans during the perception and production of speech at multiple temporal resolutions. These positions are fully funded as part a collaborative effort between University of California San Francisco, University of California Berkeley, and Lawrence-Berkeley National Laboratory. This Bay Area collaboration combines UCSF's strong clinical and experimental neuroscience experience with in vivo cortical electrophysiology, UCB's expertise in theoretical neuroscience, statistical learning and data analysis, and LBNL's supercomputing and applied mathematics together with electronics and microfabrication. Successful applicants will have access to the full range of facilities and expertise uniquely allowed by this collaboration. Applicants should have a Ph.D. (or equivalent degree) in a quantitative field such as computer science, electrical engineering, physics, or applied mathematics/statistics with a strong publication record. Expertise in theoretical/computational neuroscience or the analysis/modeling of complex dynamical systems is recommended; experience in analyzing the dynamics of population neural recordings is preferred. Applicants must be able to work in a collaborative, multi-disciplinary environment and exhibit good communication skills. Experience with scientific programing languages (e.g. C/C++, python, Matlab) is required. Expertise in speech/language and/or high-performance computing is not required, but is beneficial. To apply, please submit a CV and three letters of reference to: changed(a)neurosurgery.ucsf.edu fsommer(a)berkeley.edu ----------------------------------------------------------------------------------------------------------------- Friedrich T. Sommer, Ph.D, Adjunct Professor Redwood Center for Theoretical Neuroscience & Helen Wills Neuroscience Institute UC Berkeley 575A Evans Hall, MC #3198 Berkeley, CA 94720-3198 http://redwood.berkeley.edu/wiki/Fritz_Sommer -----------------------------------------------------------------------------------------------------------------

July 18, 2014

Neurocomputing special issue CFP

by Zhijun Yang

Dear Colleagues, May we draw your attention to a Neurocomputing special issue CFP: Advances on Biological Rhythmic Pattern Generation: Experiments, Algorithms and Application. The full announcement can be found as an internet link below. http://www.journals.elsevier.com/neurocomputing/call-for-papers/special-iss… Instructions for submission: • The submission website for Neurocomputing is located at: http://ees.elsevier.com/neucom/default.asp • To ensure that all relevant manuscripts are correctly identified for inclusion into this special issue, it is important that authors select SI: Creating Biology Rhythm when reaching the “Article Type” step in the submission process. We look forward to receiving your contributions to what promises to be an exciting special issue. Best regards, Zhijun Yang -- Zhijun Yang, BEng, MSc, PhD, Lecturer in Design Engineering Department of Design Engineering and Mathematics School of Science and Technology Middlesex University, London NW4 4BT, UK Voice: +44 (0)7882681333 --------------------------------------------------------------------------- Please note that Middlesex University's preferred way of receiving all correspondence is via email in line with our Environmental Policy. All incoming post to Middlesex University is opened and scanned by our digital document handler, CDS, and then emailed to the recipient. If you do not want your correspondence to Middlesex University processed in this way please email the recipient directly. Parcels, couriered items and recorded delivery items will not be opened or scanned by CDS. There are items which are "exceptions" which will be opened by CDS but will not be scanned a full list of these can be obtained by contacting the University.

July 17, 2014

Postdoc position in neural inspired computation

by Aimone, James Bradley

A postdoctoral research position is available for research on neural algorithm development for a neural-inspired computing hardware project within the cognitive modeling group at Sandia National Laboratories. The successful applicant will join a broad interdisciplinary team that includes computational neuroscientists, computer scientists, and electrical engineers focused on developing methods for hardware acceleration of neural algorithms. He or she will be expected to leverage research from the broader neuroscience research community to develop computational approaches for addressing real-world problems. Previous experience both in neuroscience research and in data analytics / machine learning domains is ideal. For more information, please contact Dr. Brad Aimone (technical contact, jbaimon(a)sandia.gov<mailto:jbaimon@sandia.gov> ) or Dr. John Wagner (department manager, jswagne(a)sandia.gov<mailto:jswagne@sandia.gov>). For the full posting, necessary qualifications and application information, go to careers.sandia.gov and search for posting ID 646640 -------------------------- James Bradley Aimone, Ph.D. Senior Member of Technical Staff Cognitive Modeling Department Sandia National Laboratories Phone: (505) 284-3147 Fax: (505) 844-4728

July 17, 2014

Research Specialist position at Princeton University

by Francisco Pereira

A Research Specialist position is available in Matthew Botvinick's lab in the Princeton Neuroscience Institute, in collaboration with Francisco Pereira at Siemens Corporate Technology. This is a position within a team participating in the IARPA Knowledge Representation in Neural Systems program. The goal of the program is to develop models of how the brain represents conceptual knowledge, and how that knowledge is used when carrying out tasks like reading a sentence. These models will be tested using brain imaging data. The main role in this position is as a programmer writing the code needed to build such models, while also being involved in experiment design and data analysis. Examples of tasks you might be asked to do: - prepare text corpora for use in developing models - implement evaluation tasks that the models developed will be benchmarked with - collect material from online resources and behavioral experiments - prepare stimuli for brain imaging experiments or model building - help in designing and carrying out brain imaging and behavioral experiments - analyze behavioral and brain imaging experimental data It is an unusual position in that you will be gaining experience in both machine learning and cognitive neuroscience. In addition to the core research goals, we will also be delivering a concrete system to the funding agency, and hence this will be a fast-paced project. You will be working directly with the PIs and also graduate students and research scientists in the team. Essential qualifications are experience developing software in MATLAB or Python (Perl is also useful), as well as an undergraduate degree in computer science, biomedical engineering, cognitive neuroscience or a related field. Preferred qualifications are knowledge in natural language processing, machine learning, and collection, preprocessing and analysis of brain imaging data (e.g. SPM, FSL or AFNI). The final candidate will be required to pass a background check successfully. This is a 1 year position, starting as soon as possible, with an additional year possible contingent on funding availability and performance. To apply, please go to https://jobs.princeton.edu/applicants/jsp/shared/Welcome_css.jsp and use “Search Open Positions” with requisition number 1400452. Please email francisco.pereira(a)gmail.com with any pre-application inquiries.

July 16, 2014

1st Call for Abstracts for the 7th Int'l Workshop on Guided Self-Organisation (Freiburg, Dec 16-18, 2014)

by Joschka Boedecker

<apologies for possible cross-posting> Dear colleagues, This is an announcement and Call for Abstracts for the Seventh International Workshop on Guided Self-Organisation (GSO-2014) to be held from December 16-18, 2014 in Freiburg, Germany. http://ml.informatik.uni-freiburg.de/events/gso14/index The workshop will bring together researchers from a richly diverse background who share interest in understanding and designing self-organising systems. Of particular interest are well-founded, but general methods for characterizing such systems in a principled way with the view of ultimately allowing them to be guided toward prespecified goals. Information theory, nonlinear dynamics and graph theory are core to many of these methods, and quantifying complexity and its sources is a common theme. This year, the workshop is organised in collaboration with the BrainLinks-BrainTools cluster of excellence at University of Freiburg ( https://www.brainlinks-braintools.uni-freiburg.de/ ), and one focus theme will be guided self-organisation in neural systems. Submissions to the workshop are extended abstracts (one-two pages). If interested in participating, send an extended abstract (by September 7th, 2014) by email to: jboedeck at informatik dot uni-freiburg dot de The notifications are expected by September 21st, 2014. Authors of accepted submissions will present the content to the workshop (scheduled 30 minutes presentation). It is expected that post-workshop publication of selected papers will follow in a special journal issue (as has been the case for previous GSO workshops). Please consult the website above and also http://guided-self.org/ for more information. Best wishes, Joschka Boedecker Mikhail Prokopenko Martin Riedmiller

July 16, 2014

Re: [Comp-neuro] Two Postdoctoral Positions in Computational Modeling of Primate Motor Control

by Yaroslav Molkov

Two postdoctoral/research associate positions in computational neuroscience for the duration of up to three years are available in the externally funded project focusing on multiscale computational modeling of motor control mechanisms, involving interactions between basal ganglia, thalamus, cerebellum, cortex, and spinal cord in connection with realistic simulation of pathological changes in motor control during Huntington’s disease. This collaborative project is performed jointly by the groups of Drs. Ilya Rybak (Dept. of Neurobiology and Anatomy, Drexel University, Philadelphia, PA, USA) and Yaroslav Molkov (Dept. of Mathematical Science, Indiana University – Purdue University, Indianapolis, IN, USA). Applicants must have a PhD degree in Neuroscience, Applied Mathematics, Physics, or a related discipline and have excellent C++ programming skills. Previous experience in computational neuronal modeling is a plus. Interested candidates are encouraged to contact Dr. Rybak (rybak(a)drexel.edu) or Dr. Molkov (ymolkov(a)iupui.edu) by email. To apply, please send an email along with (1) curriculum vitae; (2) a cover letter describing previous research experience and interests; (3) the names and contact information of three references. Drs. Rybak and Molkov will be available to answer questions and discuss this job opportunity with potential candidates at the CNS’2014 meeting in Quebec City, Canada (July 26-31). ================================= Ilya A. Rybak, Ph.D. Professor Department of Neurobiology and Anatomy Drexel University College of Medicine 2900 Queen Lane Philadelphia, PA 19129, USA Tel.: (215) 991-8596 FAX: (215) 843-9082 E-mail: rybak(a)drexel.edu Yaroslav Molkov, Ph.D. Assistant Professor Indiana University - Purdue University Indianapolis Department of Mathematical Sciences 402 N. Blackford St, LD 270, Indianapolis, IN 46202 Office: LD 224U Phone: (317) 274-6934 Fax: (317) 274-3460 Email: ymolkov(a)iupui.edu

July 15, 2014

Linking Brain Models and Data: An Invitation from James Bonaiuto and Michael A. Arbib

by James Bonaiuto

The USC Brain Project has just released a new version of their neuroinformatics tool, the Brain Operation DataBase (BODB) v4.0. BODB is a tool for modelers and experimentalists to document computational models of the brain and their linkages to summaries of experimental data. Models can be compared in terms of these relations, and experimental data summaries can be visualized in a variety of ways. Groups of users can build collaboratory workspaces which allow them to share groups of models and data and work together to develop new models. This latest version of BODB includes many new features: - Improved search functionality: users can now search federated databases such as CoCoMac (macaque neural connectivity) and Brede (human brain imaging) directly as well as use boolean combinations of search terms - Users can now maintain a list of their favorite entries outside any workspace - Model and BOP diagrams: users can generate clickable and zoomable graphs showing the relationships between brain operating principles (BOPs) and between models and summaries of empirical data (SEDs) - ERP SED visualization: users can view the locations of ERP components on a standard electrode placement system - Improved workspaces: workspaces now include an activity stream, discussion board, and bookmarks. Workspaces administrators can send invitations to other users to join the workspace. - ModelDB import: users can search ModelDB for models and import them to BODB for documentation of their linkage to SEDs - New Documentation System: the new system is far more extensive, is linked from pages on BODB, and users can now search the documentation - Web API: it is now possible to return BODB entries in JSON aand XML format through a simple URL protocol An overview of our general methodology for the neuroinformatics of brain modeling can be found at http://www.ncbi.nlm.nih.gov/pubmed/24234915. It is just one paper from a special issue of Neuroinformatics (Volume 12, Number 1, January 2014) on “Action, Language and Neuroinformatics” (Michael Arbib, Guest Editor). The Manual for BODB can be found at http://bodb.usc.edu/media/BODB_Manual.pdf. We invite you to register for a free account on BODB and add your own models and experimental data: http://bodb.usc.edu ------------------------------------------------------ James Bonaiuto, PhD Research Associate Sobell Department of Motor Neuroscience and Movement Disorders UCL Institute of Neurology 33 Queen Square, London WC1N3BG, United Kingdom

July 15, 2014

2014 SFN Meeting Workshop: NSG Portal for parallel simulations

by Ted Carnevale

What: Using the Neuroscience Gateway Portal for Parallel Simulations A Satellite Symposium at the 2014 Society for Neuroscience Meeting Where: Lodation to be announced in downtown Washington, DC When: 9 AM - Noon on Saturday, November 15, 2014 Speakers to include: A. Majumdar, S. Sivagnanam, and T. Carnevale Registration deadline: Friday, October 31, 2014 This workshop is intended for neuroscientists who are already using, or would like to use, the Neuroscience Gateway Portal (NSG http://www.nsgportal.org/) for parallel simulations. The NSG portal eliminates most administrative and technical barriers that face neuroscientists who need to use high performance computing resources for large modeling projects. Its web-based interface simplifies the tasks of uploading models, specifying job parameters, monitoring job status, and storing and retrieving output data. Simulators currently installed include Brian, MOOSE, GENESIS3, NEST, NEURON, and PyNN. The workshop will combine didactic presentations by NSG's developers, discussions with experienced users, and hands on instruction in how to use the portal. For more information and the online registration form see http://www.neuron.yale.edu/neuron/static/courses/nsg2014/nsg2014.html --Ted

July 14, 2014

NEURON course at 2014 SFN Meeting

by Ted Carnevale

Using NEURON to Model Cells and Networks Satellite Symposium, Society for Neuroscience Meeting 9 AM - 5 PM on Friday, November 14, 2014 Speakers include Ted Carnevale, Bill Lytton, and Gordon Shepherd This course emphasizes practical issues that are key to the most productive use of NEURON, an advanced simulation environment for realistic modeling of biological neurons and neural circuits. Through lectures and live computer demonstrations, we will address topics that include the following: Partial list of topics that will be covered: * Efficient design and implementation of models of neurons and networks. * Constructing and managing models with NEURON's GUI, hoc, and Python. * Using the built-in variable-order variable-timestep integrator for improved speed and accuracy. * Parallelizing models of cells and networks to take advantage of multicore PCs and Macs, workstation clusters, and parallel supercomputers. * Expanding NEURON's repertoire of biophysical mechanisms, including an introduction to using the new RxD feature to implement models that involve reactive diffusion. * Databases for empirically-based modeling. Each registrant will receive a comprehensive set of notes. Registration is limited to 25 individuals on a first-come, first-serve basis. The registration deadline is Friday, October 31, 2014. NO on-site registration will be accepted. For more information and the online registration form, see http://www.neuron.yale.edu/neuron/static/courses/dc2014/dc2014.html --Ted

July 14, 2014

PhD open positions on event-driven robotics

by Chiara Bartolozzi

There are few PhD positions with scholarship for developing event-driven sensors and computation for the humanoid robot iCub at the Italian Institute of Technology. The call, application form and tips on the documentation to send are at the following link: http://www.iit.it/en/openings/phd-calls/2030-phd-course-topic-information.h… 1. Event-driven vision sensors for humanoid robots Tutors: Chiara Bartolozzi Department: iCub Facility (Istituto Italiano di Tecnologia) http://www.iit.it/en/research/departments/icub-facility.html Description: Carrying out real-world tasks robustly and efficiently is one of the major challenges of robotics. Biology clearly outperforms artificial computing and robotic systems in terms of appropriateness of the behavioural response, robustness to interference and noise, adaptation to ever changing environmental conditions, or energy efficiency. All these properties arise from the characteristics of the radically different style of sensing and computation used by the biological brain. In conventional robots, sensory information is available in a sequence of static frames and high dynamics can be sensed only by increasing the sampling rate. Unfortunately the available bandwidth limits the amount of information that can be transmitted forcing a compromise between resolution and speed. The goal of the proposed theme is the development of bio-inspired event-driven artificial vision sensors that will be mounted and validated on the humanoid robot iCub. The goal is to convey to the robot the most informative signal that it can use to robustly interact with the world. The research will focus on the design of mixed-mode analog/digital circuits for photo-transduction and focal-plane processing. Different architectures and circuits will be considered for the pixel design, evaluating the trade-off between complexity (and fill-in factor) and sensor pre-processing capabilities. Additionally, ad hoc digital asynchronous logic circuits and interfaces based on the "Address Event Representation" protocol will be developed for optimally interfacing the sensor with the robot. Requirements: degree in Electronic Engineering, Physics (or equivalent) and background in Analog and/or Digital Circuit Design, possibly on FPGA programming (VHDL, Verilog). High motivation to work on a robotic platform. Reference: C. Posch, D. Matolin, and R. Wohlgenannt, "A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS," IEEE J Solid-State Circuits, vol. 46, no. 1, pp. 259-275, Jan. 2011 Contacts: chiara.bartolozzi(a)iit.it<mailto:chiara.bartolozzi@iit.it> 2. Building the neuromorphic iCub Tutors: Chiara Bartolozzi Department: iCub Facility (Istituto Italiano di Tecnologia) http://www.iit.it/en/research/departments/icub-facility.html Description: Carrying out real-world tasks robustly and efficiently is one of the major challenges of robotics. Biology clearly outperforms artificial computing and robotic systems in terms of appropriateness of the behavioural response, robustness to interference and noise, adaptation to ever changing environmental conditions, or energy efficiency. All these properties arise from the characteristics of the radically different style of sensing and computation used by the biological brain. In conventional robots, sensory information is available in a sequence of static snapshots and high dynamics can be sensed only by increasing the sampling rate. Unfortunately the available bandwidth limits the amount of information that can be transmitted forcing a compromise between resolution and speed. The goal of the proposed theme is the integration of event-driven sensors and computing platforms on the humanoid robot iCub. The goal is to convey to the robot the most informative signal that it can use to robustly interact with the world and provide adequate computing platforms such as the SpiNNaker system. The research will focus on the development of the infrastructure for optimally interfacing event-driven hardware modules based on the Address Event Representation protocol on the iCub. Requirements: degree in Electronic Engineering, Physics (or equivalent) and background on FPGA programming (VHDL, Verilog). High motivation to work on a robotic platform. Reference: C. Bartolozzi, F. Rea, C. Clercq, M. Hofstätter, D. B. Fasnacht, G. Indiveri, and G. Metta, "Embedded neuromorphic vision for humanoid robots," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011, pp. 129-135 Contacts: chiara.bartolozzi(a)iit.it<mailto:chiara.bartolozzi@iit.it> 3. Event-driven vision for the iCub Tutors: Chiara Bartolozzi, Lorenzo Natale Department: iCub Facility (Istituto Italiano di Tecnologia) http://www.iit.it/en/research/departments/icub-facility.html Description: Interacting with a dynamical environment is one of the major challenges of robotics. Biology clearly outperforms robotic systems when acting in real scenarios in terms of appropriateness of the behavioural response, robustness to interference and noise, adaptation to ever changing environmental conditions, and energy efficiency. All these properties arise from the characteristics of the radically different style of sensing and computation used by the biological brain. In conventional robots, sensory information is available in a sequence of static snapshots and high dynamics can be sensed only by increasing the sampling rate. However the available bandwidth limits the amount of information that can be transmitted forcing a compromise between resolution and speed. Event-driven vision sensors transmit information as soon as a change occurs in their visual field, achieving incredibly high temporal resolution, coupled with extremely low data rate and automatic segmentation of significant events. The proposed theme aims at the exploitation of highly dynamical information from event-driven sensors for robust interaction of the humanoid robot iCub with moving objects. The goal is to develop new techniques for the recognition and prediction of trajectories of moving targets including humans and objects and plan for precise reaching movements, by exploiting the high temporal resolution and compressive signal encoding of event-driven vision sensors mounted on the iCub. The research will focus on the development of the infrastructure for handling event-driven sensory data and of event-driven vision algorithms for motion estimation as well as algorithms for trajectory prediction. Real time behaviour will be achieved by using compressive event driven computation and on-board processing on FPGA. Requirements: degree in Computer Science or Engineering (or equivalent) and background in Computer Vision and/or Machine Learning. High motivation to work on a robotic platform and good computer and FPGA programming skills Reference: Benosman, R.; Clercq, C.; Lagorce, X.; Sio-Hoi Ieng; Bartolozzi, C., "Event-Based Visual Flow," Neural Networks and Learning Systems, IEEE Transactions on , vol.25, no.2, pp.407,417, Feb. 2014, doi: 10.1109/TNNLS.2013.2273537 Contacts: chiara.bartolozzi(a)iit.it<mailto:chiara.bartolozzi@iit.it> , lorenzo.natale(a)iit.it<mailto:lorenzo.natale@iit.it> 4. New techniques for vision-assisted speech processing for the iCub Tutors: Chiara Bartolozzi, Leonardo Badino Department: iCub Facility (Istituto Italiano di Tecnologia) http://www.iit.it/en/research/departments/icub-facility.html Description: Robust speech detection in realistic environments for human robot interaction is still a challenging task. Vision is currently used for improving speech recognition in noisy acoustic environments, however, the temporal content of the visual information is severely limited by the temporal discretization of the frame-based acquisition. Event-driven vision sensors transmit information as soon as a change occurs in their visual field, achieving incredibly high temporal resolution, coupled with extremely low data rate and automatic segmentation of significant events. The goal of the proposed theme is the exploitation of highly dynamical information from event-driven vision sensors for robust speech processing for the humanoid robot iCub. The goal is to extract visual features and cues from the stream of visual events that are related to speech production landmarks and exploit them for further improving speech recognition. The research will focus on the development of the infrastructure for handling event-driven sensory data and of novel algorithms for extraction of visual features from event-driven cameras. Requirements: degree in Computer Science or Engineering (or equivalent) and background in Auditory Processing and/or Machine Learning. High motivation to work on a robotic platform and good computer and FPGA programming skills. Reference: G. Potamianos, C. Neti, G. Gravier, A. Garg, and A.W.Senior in Proceedings of the IEEE Vol. 91 No. 9 September 2003 pp. 1306-1326 Benosman, R.; Clercq, C.; Lagorce, X.; Sio-Hoi Ieng; Bartolozzi, C., "Event-Based Visual Flow," Neural Networks and Learning Systems, IEEE Transactions on , vol.25, no.2, pp.407,417, Feb. 2014, doi: 10.1109/TNNLS.2013.2273537 Contacts: chiara.bartolozzi(a)iit.it<mailto:chiara.bartolozzi@iit.it> , leonardo.badino(a)iit.it<mailto:leonardo.badino@iit.it> Chiara Bartolozzi Researcher Istituto Italiano di Tecnologia Via Morego, 30 16163 Genoa, Italy Ph: +39 010 7178-1474

July 14, 2014