- Comp-neuro - lists.cnsorg.org

job opening in neural dynamics and control
by Obermayer, Klaus, Prof. Dr. Sept. 28, 2018

Sept. 28, 2018

Dear moderator, can post our job announcement below on the neuroinformatics mailing list? Thank you very much! Best regards Klaus Obermayer ------------------------------------------------------------------------------------------------------------------------------------------------ Neural Information Processing Group @ Technische Universität Berlin Research Assistant (PhD Position) in Computational Neuroscience Brain network models: Dynamics and Control Salary grade E13 (TV-L Berliner Hochschulen) Duration: max. 4 years Starting date: January 1st, 2019 Working field: The successful candidate is supposed to work on the analysis and simulation of calibrated whole-brain networks using neuronal mass models and on the design and evaluation of novel control schemes for the stabilization of and the switching between their dynamical states. She/he will be part of the Collaborative Research Center "Control of self-organizing nonlinear systems: Theoretical methods and concepts of application" (https://www.itp.tu-berlin.de/sfb910/ sonderforschungsbereich_910/sonderforschungsbereich_910/) as well as the Bernstein Center for Computational Neuroscience Berlin (https://www.bccn-berlin.de/) The candidate is also expected to interact with clinical scientists who are interested in the non-invasive manipulation of the whole-brain dynamics using transcranial current stimulation. Requirements: Successfully completed university degree (Master, Diplom, or equivalent) in Computational Neuroscience, Computer Science, Mathematics, Physics, or related fields; in-depth knowledge in non-linear dynamics and stochastic processes; very good programming and English language skills. How to apply: Please send your application with the usual documents (CV, motivation letter, transcripts of records, certificates, and the names of two persons who can provide recommendation letters) to Prof. Dr. Obermayer, FG Neuronale Informationsverarbeitung, Sekr. MAR 5-6, Marchstr. 23, 10587 Berlin, Germany, or by e-mail to klaus.obermayer(a)tu-berlin.de. For information about the research group see https://www.ni.tu-berlin.de/ Please send copies only. Original documents will not be returned. Aplication deadline: 26. 10. 2018 Reference number: IV-570/18

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Two PhD positions in computational and cognitive neuroscience
by Alireza Soltani Sept. 28, 2018

Sept. 28, 2018

Two PhD positions are available in the Computational and Cognitive Neuroscience Lab <http://ccnl.dartmouth.edu/> of Alireza Soltani at Dartmouth College. This NIH-funded project aims to uncover circuit-level mechanisms underlying adaptive learning. The project involves computational modeling at different levels (mean-field to spiking networks) as well as analyses of behavioral, electrophysiological, and calcium imaging data in rats during dynamic learning and decision-making tasks. Applicants should have a strong quantitative background (e.g. physics, math, statistics, computational neuroscience, computer science, etc), good programming skills, genuine interest in understanding the brain, and a collaborative attitude. A background in neuroscience is desirable, but not essential. Interested applicants should submit an inquiry to Alireza Soltani (soltani(a)dartmouth.edu <mailto:soltani@darmouth.edu>), and submit their applications to Psychological and Brain Sciences Program <https://pbs.dartmouth.edu/graduate/graduate-admissions>at Dartmouth College. Alireza Soltani, Ph.D. Assistant Professor Department of Psychological and Brain Sciences Dartmouth College, NH Alireza.Soltani(a)Dartmouth.edu <mailto:Alireza.Soltani@Dartmouth.edu> http://ccnl.dartmouth.edu <http://ccnl.dartmouth.edu/> Phone: 603-646-2998 Office: Moore 356

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New ebook: Neural Information Theory
by James V Stone Sept. 27, 2018

Sept. 27, 2018

This book (published June 2018) is now available in Kindle and pdf formats: Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency by James V Stone. Reviews, synopsis, and table of contents can be found here: http://sebtelpress.com "To understand life, one has to understand not just the flow of energy, but also the flow of information." William Bialek, 2012. regards, Dr James V Stone Honorary Reader, Sheffield University, UK. Web: sebtelpress.com Twitter: @jgvfwstone

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EEG time-varying networks - paper & code
by Williams, Nitin J Sept. 27, 2018

Sept. 27, 2018

APOLOGIES FOR CROSS-POSTING: Dear all, I am just writing to let you know of our recent paper on a Markov Model-based method to analyse time-varying networks in EEG task-related data: https://doi.org/10.3389/fncom.2018.00076 The method combines sparse-MVAR (Multi Variate Auto Regressive) modelling to estimate networks, with Markov modelling to characterise network changes. We propose this method as complementing existing methods to analyse time-varying networks in EEG/MEG task-related data. MATLAB code for the method can be downloaded from: https://github.com/nitinwilliams/eeg_meg_analysis/tree/master/sMVAR_MM_tool… Any comments/questions on the method/code are of course welcome. Regards, Nitin

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Workshop announcement: The HBP CodeJam #9 Workshop
by Andrew Davison Sept. 27, 2018

Sept. 27, 2018

The HBP CodeJam #9 Workshop - Early-bird registration ends 30. Sept.! Grand Hotel Et Des Palmes, Palermo, Italy. Nov. 26, 2018 at 09:00 – Nov. 28, 2018 at 18:00 The goal of the CodeJam workshops is to catalyze open-source, collaborative software development in computational and systems neuroscience and neuroinformatics, by bringing together researchers, students and engineers to share ideas, present their work, and write code together. The general format of the workshops is to dedicate the mornings to invited and contributed talks, leaving the afternoons free for discussions, tutorials and code sprints. The theme of this 9th installment is "co-design": scientists, students and engineers from different disciplines collaborating to build research infrastructure for computation-based neuroscience, and to use this infrastructure to model brain circuits, behaviour and learning, develop novel bio-inspired computing systems, and help discover new drugs for neurological disorders. Topics of CodeJam#9: • Large-scale brain modelling: towards a mouse whole brain model • Cortical and subcortical microcircuit models: circuit and model workflows, simulation and analysing tools, computing resources and user interaction • Infrastructure and open data: Beyond traditional HPC, electrophysiology data formats and analysis • Visio-motor integration and hierarchical navigation planning: Integration with HBP Infrastructure (Neurorobotics platform) • Biological deep learning: Plasticity implementation on neuromorphic systems • Modelling for drug discovery and medicine Topics might change slightly, we are still finalizing the programme. Confirmed speakers (not yet complete): • Matteo Cantarelli • Oliver Schmid • Markus Diesmann • Stine Vennemo • Håkon Mørk • Armandi Romani • Genrich Ivaska • Shailesh Appukuttan • Bernd Schuller • Michael Denker • Alper Yegenoglu • Lorenzo Vannucci More information and registration details at https://neuralensemble.org/meetings/CodeJam9/ Registration fee: Early-bird registration ends on 30 September 2018 Programme Committee Luca L. Bologna (CNR), Andrew Davison (CNRS), Anna Kreshuk (U. Heidelberg), Eric Müller (U. Heidelberg), Jeff Muller (EPFL), Bernd Schuller (FZ Jülich) Organisers Luca L. Bologna (CNR), Alessia Bonafede (CNR), Martina Schmalholz (U. Heidelberg)

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Postdoctoral position in memory, sequence replay, and sleep
by Kamran Diba Sept. 26, 2018

Sept. 26, 2018

<https://sites.lsa.umich.edu/diba-lab/>The Diba Lab <https://sites.lsa.umich.edu/diba-lab/> at the University of Michigan Medical School is currently recruiting a postdoctoral candidate for a project recently funded by two NIMH (R01 and R21) grants. The project involves a collaboration with Dr. Ted Abel’s lab at the University of Iowa to investigate the Molecular, Cellular and Circuit Effects of Sleep Deprivation on Hippocampal Function. The successful candidate will perform and analyze large-scale neuronal recordings from freely-behaving, sleeping and sleep-deprived animals, along with chemogenetic manipulations, immunohistochemistry and biochemical assays. Data analysis of oscillatory and sequential neuronal firing patterns, using MATLAB, python, and other tools, is critical for this research and the prospective candidate should demonstrate quantitative and programming skills. The salary will be based on the National Research Service Award (NRSA) payscale <https://grants.nih.gov/grants/guide/notice-files/NOT-OD-18-175.html>. Our lab is housed in the Department of Anesthesiology and the Center for Consciousness Science <https://consciousness.med.umich.edu/> at Michigan Medicine. We are affiliated with the Department of Biomedical Engineering <http://bme.umich.edu/explore-research/> and the Neuroscience Graduate Program <http://neuroscience.med.umich.edu/program-0>. Collectively these programs and the University of Michigan at large provide a highly exciting and stimulating scientific environment for computational and systems neuroscience. The city of Ann Arbor is also consistently ranked <https://annarborusa.org/insights/communities/facts-rankings/> among the top places in the U.S. to live (with ready access to cultural events, sports, parks and natural beauty. Applicants should contact Kamran Diba: kdiba(a)umich.edu with a detailed C.V. or resume and a description of research background, skills, and interests. ------------- A top-ranked public university, the University of Michigan has a tradition of excellence in research, learning and teaching, sports and the arts, and more. The neuroscience community at the University of Michigan encompasses over 154 participating faculty from among 29 departments from the School of Medicine (including the Department of Anesthesiology), College of Literature Science and Arts, College of Engineering, School of Dentistry, School of Kinesiology and School of Public Health. As the longest standing neuroscience Ph.D. program in the United States the Neuroscience Graduate Program at U-Michigan has built an exceptional graduate program that features collaborative interactions among faculty and integration across individual disciplines. The program is composed of 70 to 80 graduate students (currently, 52% women, 30% under-represented minorities) and has more than 130 successful and accomplished alumni that work in academic research, industrial research and development, academic medicine and biotechnology. The NGP sponsors many activities that foster development of an active and cohesive neuroscience community on campus (e.g. Weekly Neuroscience Research Seminars, Annual Retreat, Spring Symposium, and Community Outreach).

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CFP: The OMG-Empathy Prediction Challenge
by Pablo Barros Sept. 26, 2018

Sept. 26, 2018

CALL FOR PARTICIPATION The One-Minute Gradual-Empathy Prediction (OMG-Empathy) Competition held in partnership with the IEEE International Conference on Automatic Face and Gesture Recognition 2019 in Lille, France. https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/omg_empathy.html I. Aim and Scope The ability to perceive, understand and respond to social interactions in a human-like manner is one of the most desired capabilities in artificial agents, particularly social robots. These skills are highly complex and require a focus on several different aspects of research, including affective understanding. An agent which is able to recognize, understand and, most importantly, adapt to different human affective behaviors can increase its own social capabilities by being able to interact and communicate in a natural way. Emotional expression perception and categorization are extremely popular in the affective computing community. However, the inclusion of emotions in the decision-making process of an agent is not considered in most of the research in this field. To treat emotion expressions as the final goal, although necessary, reduces the usability of such solutions in more complex scenarios. To create a general affective model to be used as a modulator for learning different cognitive tasks, such as modeling intrinsic motivation, creativity, dialog processing, grounded learning, and human-level communication, only emotion perception cannot be the pivotal focus. The integration of perception with intrinsic concepts of emotional understanding, such as a dynamic and evolving mood and affective memory, is required to model the necessary complexity of an interaction and realize adaptability in an agent's social behavior. Such models are most necessary for the development of real-world social systems, which would communicate and interact with humans in a natural way on a day-to-day basis. This could become the next goal for research on Human-Robot Interaction (HRI) and could be an essential part of the next generation of social robots. For this challenge, we designed, collected and annotated a novel corpus based on human-human interaction. This novel corpus builds on top of the experience we gathered while organizing the OMG-Emotion Recognition Challenge, making use of state-of-the-art frameworks for data collection and annotation. The One-Minute Gradual Empathy datasets (OMG-Empathy) contain multi-modal recordings of different individuals discussing predefined topics. One of them, the actor, shares a story about themselves while the other, the listener, reacts to it emotionally. We annotated each interaction based on the listener's own assessment of how they felt while the interaction was taking place. We encourage the participants to propose state-of-the-art solutions not only based on deep, recurrent and self-organizing neural networks but also traditional methods for feature representation and data processing. We also enforce that the use of contextual information, as well as personalized solutions for empathy assessment, will be extremely important for the development of competitive solutions. II. Competition Tracks We let available for the challenge a pre-defined set of training, validation and testing samples. We separate our samples based on each story: 4 stories for training, 1 for validation and 3 for testing. Each story sample is composed of 10 videos with interactions, one for each listener. Although using the same training, validation and testing data split, we propose two tracks which will measure different aspects of the self-assessed empathy: The Personalized Empathy track, where each team must predict the empathy of a specific person. We will evaluate the ability of proposed models to learn the empathic behavior of each of the subjects over a newly perceived story. We encourage the teams to develop models which take into consideration the individual behavior of each subject in the training data. The Generalized Empathy track, where the teams must predict the general behavior of all the participants over each story. We will measure the performance of the proposed models to learn a general empathic measure for each of the stories individually. We encourage the proposed models to take into consideration the aggregated behavior of all the participants for each story and to generalize this behavior in a newly perceived story. The training and validation samples will be given to the participants at the beginning of the challenge together with all the associated labels. The test set will be given to the participants without the associated labels. The team`s predictions on the test set will be used to calculate the final metrics of the challenge. III. How to Participate To participate to the challenge, please send us an email to barros @ informatik.uni-hamburg.de with the title "OMG-Empathy Team Registration". This e-mail must contain the following information: Team Name Team Members Affiliation Participating tracks We split the corpus into three subsets: training, validation and testing. The participants will receive the training and validation sets, together with the associated annotations once they subscribe to the challenge. The subscription will be done via e-mail. Each participant team must consist of 1 to 5 participants and must agree to use the data only for scientific purposes. Each team can choose to take part in one or both the tracks. After the training period is over, the testing set will be released without the associated annotations. Each team must submit, via e-mail, their final predictions as a .csv file for each video on the test set. Together with the final submission, each team must send a short 2-4 pages paper describing their solution published on Arxiv and the link for a github page to their solution. If a team fails to submit any of these items, their submission will be invalidated. Each team can submit 3 complete submissions for each track. IV. Important Dates 25th of September 2018 - Opening of the Challenge - Team registrations begin 1st of October 2018 - Training/validation data and annotation available 1st of December 2018 - Test data release 3rd of December 2018 - Final submission (Results and code) 5th of December 2018 - Final submission (Paper) 7th of December 2018 - Announcement of the winners V. Organization Pablo Barros, University of Hamburg, Germany Nikhil Churamani, University of Cambridge, United Kingdom Angelica Lim, Simon Fraser University, Canada Stefan Wermter, Hamburg University, Germany -- Dr.rer.nat. Pablo Barros Postdoctoral Research Associate - Crossmodal Learning Project (CML) Knowledge Technology Department of Informatics University of Hamburg Vogt-Koelln-Str. 30 22527 Hamburg, Germany Phone: +49 40 42883 2535 Fax: +49 40 42883 2515 barros at informatik.uni-hamburg.de https://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.html https://www.inf.uni-hamburg.de/en/inst/ab/wtm/

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Postdoc in computational neuroscience/mathematical biology
by Rubchinsky, Leonid L Sept. 26, 2018

Sept. 26, 2018

NSF-supported postdoctoral position in mathematical biology/computational neuroscience in my group in the Department of Mathematical Sciences at Indiana University-Purdue University Indianapolis (IUPUI). This position involves research in the mechanisms and properties of synchronization in neural and other living systems including collaboration with experimentalists. The position offers excellent interdisciplinary training possibilities in computational neuroscience and mathematical biology. Applicants should have (or expected to get in a near future) Ph.D. in mathematics, physics, neuroscience, biomedical engineering, computer science, or another related field. Applicants should have strong quantitative skills in dynamical modeling and data analysis and a strong interest in life sciences applications. Experience in nonlinear dynamics, computational neuroscience, and mathematical biology is a plus. Please apply via mathjobs.org at https://www.mathjobs.org/jobs/jobs/12619. Screening of applications will continue until position is filled. Please address informal inquires to Leonid Rubchinsky via e-mail: lrubchin(a)iupui.edu Leonid *********************** Leonid Rubchinsky, PhD Associate Professor Department of Mathematical Sciences, Indiana University - Purdue University, Indianapolis Stark Neurosciences Research Institute, Indiana University School of Medicine 402 N. Blackford St Indianapolis, IN 46202-3216 lrubchin(a)iupui.edu http://www.math.iupui.edu/~leo 317-274-9745 317-274-3460 (fax) ***********************

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Software Developer / Research Associate
by Stephanie Jones Sept. 24, 2018

Sept. 24, 2018

Applications are invited for a research software developer / research associate position in the lab of Dr. Stephanie Jones at Brown University in Providence, RI. The level of the hire depends on experience. Our group combines human electrophysiological recordings and computational neural modeling techniques to study the mechanisms and meaning of brain dynamics in health and disease. We collaborate extensively with animal electrophysiologists and clinicians to develop data constrained models that are translationally relevant. The software developer / research associate will work with a team of researchers at Brown, Mass General Hospital, and Yale, to help develop a new software tool for circuit level investigation of human magneto- and electro-encephalography (MEG/EEG) signals: Human Neocortical Neurosolver, HNN (https://hnn.brown.edu) <https://hnn.brown.edu)>. The goal of this software is to provide the community with a user friendly tool to develop and test hypotheses on the circuit origin of their data. The hired candidate will help support and expand HNN’s capabilities, including software development and working with researchers to apply HNN to specific neuroscience questions. Duties will also include development of tutorials, online resources, and participation in workshops aimed at training the community in the use and applicability of HNN. Candidates should have a Bachelor’s, Master’s, or PhD in computer science, neuroscience, biomedical engineering, and/or related field. Strong Python programming skills are required, and experience with neural modeling, particularly NEURON, is preferred. Familiarity with MEG/EEG analysis methods and/or non-invasive brain stimulation is also beneficial. Candidates should be able to work in a dynamic, interdisciplinary, and fun work environment. Interested candidates should send a cv and a brief description of qualifications and research interests to the Jones lab manager Dylan_S_Daniels(a)brown.edu <mailto:Dylan_S_Daniels@brown.edu>. ******************************************* Stephanie R. Jones, PhD Associate Professor Brown University Department of Neuroscience Stephanie_Jones(a)Brown.edu https://blogs.brown.edu/joneslab/ <https://blogs.brown.edu/joneslab/> Human Neocortical Neurosolver https://hnn.brown.edu <https://hnn.brown.edu/>

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Postdoctoral researchers - computational modeling, EEG, TMS
by Stephanie Jones Sept. 24, 2018

Sept. 24, 2018

Applications are invited for a postdoctoral position in the lab of Dr. Stephanie Jones at Brown University in Providence RI. Our group combines human electrophysiological recordings and computational neural modeling techniques to study the mechanisms and meaning of brain dynamics in health in disease. We collaborate extensively with animal electrophysiologists and clinicians to develop data constrained models that are translationally relevant. We are expanding our research efforts to including non-invasive brain stimulation. A postdoctoral position is available to integrate simultaneous human EEG and transcranial magnetic stimulation (TMS) in humans, with computational neural modeling developments. The postdoctoral researcher will help expand our new software tool Human Neocortical Neurosolver (https://hnn.brown.edu) <https://hnn.brown.edu)/> for circuit level interpretation of EEG/MEG signals to include TMS studies. She/he will also help with the design, implementation and analysis of our TMS/EEG experiments. Candidates should have a PhD in neuroscience, computational neuroscience, computer science, applied mathematics, biomedical engineering, and/or related field. Strong quantitative and programming skills are required. The ideal candidate would have experience in computational neuroscience methods applied to human EEG and brain stimulation data. However, experience in all areas is not required. Candidates should be able to work in a dynamic, interdisciplinary and fun work environment. Interested candidates should send a cv and a brief description of qualifications and research interests to the Jones lab manager Dylan_S_Daniels(a)brown.edu <mailto:Dylan_S_Daniels@brown.edu>. ******************************************* Stephanie R. Jones, PhD Associate Professor Brown University Department of Neuroscience Stephanie_Jones(a)Brown.edu https://blogs.brown.edu/joneslab/ <https://blogs.brown.edu/joneslab/> Human Neocortical Neurosolver https://hnn.brown.edu <https://hnn.brown.edu/>

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