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Using spike train distances to identify the most discriminative neuronal subpopulation
by Thomas Kreuz Nov. 26, 2018

Nov. 26, 2018

Dear all, may I kindly draw your attention to our most recent paper which contains several new algorithms to address neuronal population coding using spike train distances: Satuvuori E, Mulansky M, Daffertshofer A, Kreuz T: Using spike train distances to identify the most discriminative neuronal subpopulation <https://www.sciencedirect.com/science/article/pii/S0165027018302747> JNeurosci Methods, 308, 354 [PDF <https://www.sciencedirect.com/science/article/pii/S0165027018302747>] and arXiv [PDF <https://arxiv.org/pdf/1805.10892.pdf>] (2018). For the abstract see below. This paper is part of the dissertation "Spike train distances and neuronal coding <http://dare.ubvu.vu.nl/bitstream/handle/1871/55855/abstract%20english.pdf?s…>" of my PhD student Eero Satuvuori whose full thesis can now be found here <http://dare.ubvu.vu.nl/bitstream/handle/1871/55855/abstract%20english.pdf?s…>. Besides some original parts and the paper cited above it also contains these recent works: Satuvuori E, Kreuz T: Which spike train distance is most suitable for distinguishing rate and temporal coding? JNeurosci Methods 299, 22 [PDF <https://ac.els-cdn.com/S0165027018300372/1-s2.0-S0165027018300372-main.pdf?…>] and arXiv [PDF <https://arxiv.org/pdf/1708.07508.pdf>] (2018). Kreuz T, Satuvuori E, Pofahl M, Mulansky M: Leaders and followers: Quantifying consistency in spatio-temporal propagation patterns New J. Phys., 19, 043028 [PDF <https://doi.org/10.1088/1367-2630/aa68c3>] and arXiv [PDF <https://arxiv.org/pdf/1610.07986v4.pdf>] (2017). Satuvuori E, Mulansky M, Bozanic N, Malvestio I, Zeldenrust F, Lenk K, Kreuz T: Measures of spike train synchrony for data with multiple time-scales JNeurosci Methods 287, 25 [PDF <https://doi.org/10.1016/j.jneumeth.2017.05.028>] and arXiv [PDF <https://arxiv.org/pdf/1702.05394.pdf>] (2017). All the best, Thomas Kreuz PS: Satuvuori E, Mulansky M, Daffertshofer A, Kreuz T: Using spike train distances to identify the most discriminative neuronal subpopulation <https://www.sciencedirect.com/science/article/pii/S0165027018302747> JNeurosci Methods, 308, 354 [PDF <https://www.sciencedirect.com/science/article/pii/S0165027018302747>] and arXiv [PDF <https://arxiv.org/pdf/1805.10892.pdf>] (2018). Abstract: Background Spike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated by a neuronal population as a whole or the individual neurons have encoding capacities of their own. The SPIKE-distance estimated either for a single, pooled spike train over a population or for each neuron separately can serve to quantify these responses. New method For the SP case we compare three algorithms that search for the most discriminative subpopulation over all stimulus pairs. For the LL case we introduce a new algorithm that combines neurons that individually separate different pairs of stimuli best. Results The best approach for SP is a brute force search over all possible subpopulations. However, it is only feasible for small populations. For more realistic settings, simulated annealing clearly outperforms gradient algorithms with only a limited increase in computational load. Our novel LL approach can handle very involved coding scenarios despite its computational ease. Comparison with existing methods Spike train distances have been extended to the analysis of neural populations interpolating between SP and LL coding. This includes parametrizing the importance of distinguishing spikes being fired in different neurons. Yet, these approaches only consider the population as a whole. The explicit focus on subpopulations render our algorithms complimentary. Conclusions The spectrum of encoding possibilities in neural populations is broad. The SP and LL cases are two extremes for which our algorithms provide correct identification results. -- Institute for complex systems, CNR Via Madonna del Piano 10 50119 Sesto Fiorentino (Italy) Tel: +39-349-0748506 Email: thomas.kreuz(a)cnr.it Webpage: http://www.fi.isc.cnr.it/users/thomas.kreuz/

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Frontiers Research Topic "Discrimination of Genuine and Posed Facial Expressions of Emotion"
by Jian Liu Nov. 26, 2018

Nov. 26, 2018

Call for Papers: Frontiers Research Topic "Discrimination of Genuine and Posed Facial Expressions of Emotion" More information: https://www.frontiersin.org/research-topics/9221/discrimination-of-genuine-… About this Research Topic Facial expressions demonstrate one emotional states in interpersonal situations. Evidence shows that part of the facial display reflects the emotional experience that is literally felt by the expresser. Interestingly, human beings are capable of identifying facial expressions of the felt emotions as a form of intentional deception to conduct social interaction and to stage displays that have the support of others. Staged or posed facial expressions implement an emotion that an expresser intends to convey, where genuine expressions are considered as the companion of spontaneous emotional expressions. The ability to differentiate genuine displays of emotional experience from posed ones is very important for dealing with day-to-day social interactions. Recent work has been conducted on whether or not people can distinguish between posed and genuine displays of emotion. In spite of few studies to investigate this ability, most prior research suggests that people have the ability to judge genuine and posed facial displays. Unfortunately, previous research has suffered from two major shortcomings: (1) the mixture of staged and genuine displays due to the lack of accounting for possible effects of intentional manipulation, and (2) struggling to consider dynamic aspects when people prepare facial stimuli for experimental investigation. This Research Topic encourages the submission of theoretical and experimental perspectives to broaden understanding of the importance of the discrimination of genuine and posed facial expressions of emotion. These may be new theoretical approaches, those from other disciplines of psychology not usually utilized within the discrimination of genuine and staged emotion identification or new theories and designs. We seek articles that present new hypotheses, concepts, experimental observations, and theories or models; demonstrate how theories adapted from other disciplines may be utilized for emotion identification or provide recommendations to improve current models or theories to enhance their capacity. We look for papers that may combine critical analysis of current models, synthesis of early empirical work by single- or multi-modality analysis (e.g. video, ECG, EEG, fMRI, and single cells in humans and/or animals), and explore the potential for their development applied to the context of emotion recognition. Submissions of systematic reviews and meta-analysis should discuss and promote comprehensive approaches to update and evolve concepts, hypotheses, and theories with potential applications in the community. Keywords: face expression, discrimination, genuine, posed, emotion Guest editors: Huiyu Zhou, Department of Informatics, University of Leicester, United Kingdom. Caroline Ling Li, School of Computing, University of Kent, United Kingdom. Shiguang Shan, Institute of Computing Technology, Chinese Academy of Sciences, China. Shuo Wang, Chemical and Biomedical Engineering, West Virginia University, United States. Jian Liu, Centre for Systems Neuroscience, University of Leicester, United Kingdom.

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Call for applications - 2019 Nengo Summer School
by Peter Blouw Nov. 24, 2018

Nov. 24, 2018

[All details about this school can be found online at https://www.nengo.ai/summerschool] The Centre for Theoretical Neuroscience at the University of Waterloo is excited to announce our 6th annual Nengo summer school on large-scale brain modelling and neuromorphic computing. This two-week school will teach participants to use the Nengo simulation package to build state-of-the-art cognitive and neural models to run both in simulation and on neuromorphic hardware. Summer school participants will be given on-site access to Loihi, Intel’s new neuromorphic research chip [1], and will learn to run high-level applications on Loihi using Nengo! More generally, Nengo provides users with a versatile and powerful environment for designing cognitive and neural systems, and has been used to build what is currently the world's largest functional brain model, Spaun [2], which includes spiking deep learning, reinforcement learning, adaptive motor control, and cognitive control networks. For a look at last year's summer school, check out this short video: https://www.youtube.com/watch?v=NwtYgBB2N6I We welcome applications from all interested graduate students, postdocs, professors, and industry professionals with a relevant background. [1] Davies, et al. (2018). Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro. Vol. 38 no. 1 pp. 82-99. [ https://ieeexplore.ieee.org/document/8259423] [2] Eliasmith, C., Stewart T. C., Choo X., Bekolay T., DeWolf T., Tang Y., Rasmussen, D. (2012). A large-scale model of the functioning brain. Science. Vol. 338 no. 6111 pp. 1202-1205. DOI: 10.1126/science.1225266. [ http://compneuro.uwaterloo.ca/files/publications/eliasmith.2012.pdf] ***Application Deadline: February 15, 2019*** Format: A combination of tutorials and project-based work. Participants are encouraged to bring their own ideas for projects, which may focus on testing hypotheses, modeling neural or cognitive data, implementing specific behavioural functions with neurons, expanding past models, or providing a proof-of-concept of various neural mechanisms. Hands-on tutorials, work on individual or group projects, and talks from invited faculty members will make up the bulk of day-to-day activities. A project demonstration event will be held on the last day of the school, with prizes for strong projects! Participants will have the opportunity to learn how to: - interface Nengo with neuromorphic hardware (e.g. Loihi, SpiNNaker) - build perceptual, motor, and cognitive models using spiking neurons - model anatomical, electrophysiological, cognitive, and behavioural data - use a variety of single cell models within a large-scale model - integrate machine learning methods into biologically oriented models - interface Nengo with cameras and robotic systems - implement modern nonlinear control methods in neural models - and much more… Date and Location: June 9th to June 21st, 2019 at the University of Waterloo, Ontario, Canada. Applications: Please visit http://www.nengo.ai/summerschool, where you can find more information regarding costs, travel, lodging, along with an application form listing required materials. If you have any questions about the school or the application process, please contact Peter Blouw (peter.blouw(a)appliedbrainresearch.com) The school is also partly supported by ONR and ABR, Inc. We look forward to hearing from you!

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Postdoc position at The Krembil Centre for Neuroinformatics
by Leon French Nov. 21, 2018

Nov. 21, 2018

The Computational Neurobiology Laboratory at The Krembil Centre for Neuroinformatics seeks a postdoctoral fellow to join our efforts to understand brain disorders. We focus on mining datasets of connectivity, gene expression, and epigenetics across the genome and the brain. We employ multifaceted approaches that gain information by using data that crosses scales, modalities, and species. We seek one postdoctoral fellow interested in bioinformatics and neuroscience. The primary project will be to join large transcriptomic resources with the polygenic signal of major depression to gain insight into the disorder. In addition to this project, there would be opportunities to pursue other research interests. Some of our frequently used keywords give a good description of our work: neuroinformatics, molecular neuroanatomy, genetics, transcriptomics, machine learning, text mining, neuroimaging, software development, and connectomics. The ideal candidate should have a PhD in computational biology, bioinformatics, computer science or related field with extensive knowledge in genome wide data integration. Significant interest in neuroscience and experience working with large datasets are requested. Familiarity with The Library of Integrative Network-based Cellular Signatures (LINCS) is a plus. Preferred qualifications and experience: - Excellent written and verbal communication skills - Extensive experience in programming and scripting (e.g., R or Python) - Expertise in genomics, bioinformatics, computational biology, neuroscience or related areas - Experience in analyzing, visualizing and integrating large-scale biological data - Contributions to public software projects Writing manuscripts and applying for independent grants and fellowships will be strongly encouraged. Highly motivated individuals (Ph.D. and/or M.D.) should submit a curriculum vitae, names of 3 references and a cover letter explaining their interest, and a brief description of research experience to leonfrench+recruit at gmail.com. You will support a healthy workplace that embraces diversity, encourages teamwork and complies with all applicable and regulatory requirements. This position will be located at 250 College Street, Toronto.

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2 years postdoc position in reinforcement learning-based modeling of animal behavior
by david robbe Nov. 21, 2018

Nov. 21, 2018

Dear Colleagues, Please find below an announcement for a postdoc position in Marseille (France) to develop reinforcement learning-based models of animal behavior. The Turing Centre for Living Systems in Marseille seeks applicants for a postdoc position in computational neuroscience/robotics under the joint supervision of David Robbe and Christophe Eloy. The initial appointment will be for 2 years. The goal of this project is to understand, at an algorithmic level, how animals learn to develop adaptive behavior by trial-and-error interaction with their environment. We propose an interdisciplinary approach combining behavioral experiments in rats and the development of an "artificial" agent facing challenges similar to those experienced by the rats. The experimental data (already available) show that rats challenged in a time estimation task converged progressively toward a conserved embodied strategy (Rueda-Orozco and Robbe, Nature Neuroscience, 2015). Key determinants of that strategy have been isolated experimentally, by altering task rules and environment and quantifying how these alterations affect animal performance. Learning theoretical models will be based on reinforcement learning and more recent developments coming from artificial intelligence (deep learning). The main task of the project will be to develop computational model and therefore we expect the candidates to have a solid background in the gerenal field of machine learning. The successful applicant will work in close collaboration with experimentalists and robotic engineers. We are therefore looking for an enthusiastic applicant with excellent communication skills and a track record in interdisciplinary collaborative environments Deadline for application: 1st of December 2018 (late application will be considered until the end of the year) More information and application.<http://centuri-livingsystems.org/pdp2018-11/>http://centuri-livingsystems.org/pdp2018-11/ -- David Robbe, PhD Neural Bases of Sensorimotor learning Institut de Neurobiologie de la Méditerranée (INMED) INSERM-UMR1249 163, avenue de Luminy. BP13 13273 Marseille Cedex 9 France Tel: +33 4 91 82 81 99 Fax: +33 4 91 82 81 05 Team Web Page <http://www.inmed.fr/en/en-avenir-dynamiques-neuronales-et-fonctions-des-gan…> /"It is disturbingly common for studies to include behavior as simply a hasty add-on in papers that are otherwise crammed full of multiple techniques, types of results, and even species. It is as if every paper needs to be a methodological decathlon in order to be considered important."/ Krakauer et al., Neuron 2017 <https://www.cell.com/neuron/abstract/S0896-6273(16)31040-6>

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New AFOSR Cognitive & Computational Neuroscience research funding portfolio
by GREENWALD, HAL S DR-04 USAF AFMC AFOSR/RTA Nov. 19, 2018

Nov. 19, 2018

Dear Colleagues, Please check out AFOSR's new research funding portfolio in Cognitive and Computational Neuroscience! The Cognitive and Computational Neuroscience program funds high-risk, high-potential basic research that uses experimental and computational modeling techniques from systems neuroscience, cognitive neuroscience, computational/theoretical neuroscience, cognitive science, and cognitive psychology to understand the neural mechanisms responsible for perception, cognition, and behavior. The program also supports brain-inspired algorithm and hardware development provided these are useful for testing proposed neuroscience theories and/or enabling novel capabilities in computing, artificial intelligence, or autonomous systems. The Broad Agency Announcement is posted at https://www.grants.gov/web/grants/view-opportunity.html?oppId=305996 (see file FA9550-18-S-0003 Amendment 002.pdf) Awards under this BAA are not limited to US researchers. Although the BAA remains open year round, to be considered for FY19 funding, full proposals should be submitted via grants.gov no later than mid-February. Emailed pre-proposals in the form of white papers (approximately 3-5 pages, excluding references) describing specific research questions and approaches are encouraged. Regards, Hal Greenwald Hal S. Greenwald, Ph.D. Program Officer, Cognitive & Computational Neuroscience Air Force Office of Scientific Research (AFOSR/RTA) 875 N. Randolph St. Arlington, VA 22203-1768 (703) 588-8441 neuroscience(a)us.af.mil

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PhD studentship, University of Exeter
by Goodfellow, Marc Nov. 19, 2018

Nov. 19, 2018

A fully funded studentship is available to work with Dr. Marc Goodfellow (University of Exeter, UK) and Prof. George Augustine (NTU, Singapore). The deadline for applications is 31st January 2019. Healthy brain function is mediated by the coordination of neuronal activity - both locally and across different brain regions - giving rise to large-scale brain dynamics. These dynamics are measured using a variety of techniques, for example magneto-/electro-encephalography or functional MRI in humans, or by fluorescence-based imaging of voltage- or calcium-sensitive indicators in animal models in vivo. Uncovering the nature and mechanisms of large-scale brain dynamics at rest, or during sensory processing, remains a fundamental challenge in neuroscience. In addition to basic insight, improving our understanding of healthy brain dynamics will help us elucidate reasons why abnormal dynamics occur, for example in neurological or neuropsychiatric disorders. This studentship will develop a novel program of interdisciplinary research across in vivo (mouse) experimentation and mathematical modelling. The overall aim is to construct and validate mathematical models of large-scale brain dynamics that are able to explain the spontaneous activity of the rodent brain in vivo. The student will train in optogenetic technologies and in vivo imaging, as well as mathematical model development, multi-variate time series analysis and parameter fitting tools, thus placing them at the forefront of interdisciplinary neuroscience. In a first step, targeted optogenetic stimuli will be combined with voltage sensitive dye imaging in awake mice to probe the response of brain tissue to excitatory and inhibitory afferent stimuli. Neural mass models will be fit to these data, using machine learning approaches (for example random forests). This information will be compiled into a predictive model of cortical dynamics and tested against experimental recordings of spontaneous activity. The studentship is part of a joint program between The University of Exeter (UoE) and Nanyang Technological University (NTU), Singapore. They are offering six fully funded postgraduate studentships to undertake collaborative research projects at the two institutions, leading to PhD degrees (split-site) to be conferred either by the UoE or NTU. Students pursuing these postgraduate research projects will benefit from the unique opportunity to conduct their research at both institutions. Students will be registered at one or other institution, where they will be based for the majority of their time, but will spend at least 12 and not more than 18 months at the partner institution over the duration of the programme. The frequency and length of stays at each institution will be agreed with successful candidates prior to offers being made. All six projects are advertised concurrently at both institutions and three will be allocated to each institution after the deadline has passed, based on a collaborative decision made between the UoE and NTU. The final decision on the successful applicant for each project will be made by the institution hosting the project. Project allocation will be based on the applicant's best fit to a project, following a review of applications submitted to each institution. Applications to undertake the projects at the UoE and NTU are open to all nationalities. The programme start dates are August 2019 for NTU and September 2019 for UoE The home institution will determine the regulations that will apply to the successful applicant. The student's main supervisor will be based at the home institution. For further information and to register interest, please contact Dr. Marc Goodfellow (m.goodfellow(a)exeter.ac.uk) To apply and for further details, please see http://www.exeter.ac.uk/studying/funding/award/?id=3058

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Ph.D. positions in Math Biology and Computational Neuroscience at NJIT
by Victor Matveev Nov. 17, 2018

Nov. 17, 2018

Dear all, The Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT) is inviting candidates to apply to one of our doctoral program tracks: (1) Applied Mathematics or (2) Applied Probability & Statistics. The Department's research focus spans Mathematical Biology and Computational Neuroscience, Scientific Computing, Fluid Dynamics, Materials Science, Wave Propagation, Applied Analysis, Applied Probability & Statistics, including Biostatistics. Teaching and research assistantships are available, and include a tuition waiver and a competitive stipend. Application target date is December 15, 2018 but ongoing review will continue until all available positions are filled. To apply, please visit our homepage: https://www.njit.edu/graduatestudies/department-mathematical-sciences/?utm_… For more information, please email us (math(a)njit.edu, CC to:rmoore@njit.edu, matveev(a)njit.edu) PhD Program advertisement flyer is available at http://web.njit.edu/~matveev/NJIT_DMS_PhD_flyer.pdf Sincerely, -- Admissions Committee, Department of Mathematical Sciences, NJIT -- _____________________________________________ Victor Matveev, Professor Applied Math, Biophysics, Neuroscience Department of Mathematical Sciences Graduate Faculty, Dept of Biological Sciences New Jersey Institute of Technology Newark, NJ 07102-1982 Office: Cullimore Hall 616 E-mail: matveev(a)njit.edu http://web.njit.edu/~matveev/

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Threshold Networks Workshop - First announcement
by Stephen Coombes Nov. 16, 2018

Nov. 16, 2018

First announcement Threshold Networks July 22 - 24, 2019 Nottingham, UK https://www.nottingham.ac.uk/conference/fac-sci/maths-sci/threshold-network… This workshop will bring together a community of people interested in both networks and dynamics, with an eye to both mathematical tractability and applications to biology, engineering, physics, and the social sciences. The emphasis will be on ‘thresholds’ in their broadest sense as exemplified by node dynamics or interactions that are described by simple, yet possibly non-smooth or discontinuous switch-like processes. The workshop will cover both theory and applications. The meeting will have no parallel sessions, and it is expected to involve around 60 participants (with 12 main speakers) and contributed poster presentations. Speakers: Alex Arenas, Universidad Rovira i Virgili, Spain Mario Di Bernardo, University of Bristol, UK Ginestra Bianconi, Queen Mary University of London, UK Des Higham, University of Strathclyde, UK Renaud Lambiotte, University of Oxford, UK Naoki Masuda, University of Bristol, UK Peter Mucha, University of North Carolina, USA Sarah Muldoon, University at Buffalo, USA Yamir Moreno, University of Zaragoza, Spain Important dates Registration deadline: 1st June 2019 Poster abstract submission deadline: 12th April 2019. Notification of acceptance: 1st May 2019 The registration fee for the conference is 100.00 GBP. A “Tutorial Day” (covering key concepts in dynamical systems and network science) will also take place prior to the meeting (on July 21). Some financial assistance may be available to assist graduate students who attend both the training workshop and the conference. Further details of this meeting and how to register may be found at https://www.nottingham.ac.uk/conference/fac-sci/maths-sci/threshold-network… Scientific Organisers Yi Ming Lai, Rüdiger Thul, and Stephen Coombes, University of Nottingham Mason A. Porter, UCLA Enquiries should be sent to: ThresholdNetworks2019(a)nottingham.ac.uk<mailto:ThresholdNetworks2019@nottingham.ac.uk> ------------------------------------------------------- Stephen Coombes Professor of Applied Mathematics School of Mathematical Sciences Nottingham, UK Tel: 0115 846 7836 http://www.maths.nott.ac.uk/~sc/ ------------------------------------------------------- This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please contact the sender and delete the email and attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham. Email communications with the University of Nottingham may be monitored where permitted by law.

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Multiple Postdocs available in the Neurobiology of Language
by Woolnough, Oscar Nov. 16, 2018

Nov. 16, 2018

POSTDOCTORAL RESEARCH POSITIONS Multiple Postdoctoral research positions are available in the Tandon Lab at The University of Texas in Houston as part of the newly formed Texas Epilepsy Neurotechnologies and Neuroinformatics (TENN) Institute. Positions are funded either via multi year Institute funding or by NIH funds (an R01 and a U01). The lab uses multimodal approaches – fMRI, lesional analysis following epilepsy surgery, intracranial recordings and direct stimulation to create validate network level representations of language. Lab Collaborators include Greg Hickok (UCI), Stanislas Dehaene (NeuroSpin), Nathan Crone (JHU), Simon Fisher Baum (Rice) and Xaq Pitkow (Rice-Baylor); the post-doc will benefit from a close interaction with these experts in the fields of reading, semantics, speech production and computational neuroscience. The selected individual must have a Ph.D. in one or more of the following: neuroscience, psychology, cognitive science, mathematics, electrical engineering or computer science. Previous experience in neural time series data analysis, functional imaging studies of language, or studies of speech production are desirable – but not crucial. They must possess the ability to independently code in any or all of the following: MATLAB, R or python. They are expected to be highly motivated, team players with a passion to study cognitive processes using any or all of the various modalities available in the lab - imaging, direct recordings and closed-loop cortical stimulation in humans. Given the multiple unpredictable variables and privacy issues around data collection in human patients, the individual must possess high ethical and professional standards and be adaptable. A strong publication record and excellent academic credentials are highly desirable. CONTACT: Nitin.Tandon(a)uth.tmc.edu<mailto:Nitin.Tandon@uth.tmc.edu> Eliana.Klier(a)uth.tmc.edu<mailto:Eliana.Klier@uth.tmc.edu> More information @ www.tandonlab.org

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