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Neuromodulation of Neural Microcircuits NM2 Conference 2017
by Srikanth Ramaswamy Aug. 22, 2017

Aug. 22, 2017

We are delighted to announce the upcoming Neuromodulation of Neural Microcircuits (<https://nm2.epfl.ch/>NM2) Conference 2017<https://nm2.epfl.ch> (https://nm2.epfl.ch/) The NM2 Conference will be held at the EPFL, Lausanne, Switzerland on the 18th and 19th of Sep. 2017 and at the Campus Biotech in Geneva, Switzerland on the 20th of Sep. 2017. Neuromodulators dynamically reconfigure neural microcircuits and shape brain states by controlling the function of neurons and glia, dendrites, and synapses. NM2 will bring together world-leading experts to: • Identify the state-of-the-art mechanisms of the neuromodulation of neural microcircuits • Illuminate various strategies enabling the measurement of neuromodulatory states in brain health and disease • Integrate knowledge to build a unifying view of the neuromodulation of different brain regions • Inform and attract new talent to drive forward neuromodulation research • Inspire future directions that will transform our understanding of the neuromodulation of brain function and dysfunction and therapeutic intervention NM2 has an exciting program<https://nm2.epfl.ch/page-145616-en.html> with a stimulating lineup of speakers<https://nm2.epfl.ch/page-145624-en.html>! Please mark your calendars to participate in shaping the future course of neuromodulatory research! Registration<https://nm2.epfl.ch/page-145625-en.html> includes coffee breaks, lunch, refreshments and poster sessions. PhD. students and postdocs are particularly encouraged to submit poster abstracts<https://nm2.epfl.ch/page-146294-en.html>. Some abstracts will be selected for flash talks. Early bird registration is open until August 21st, 2017. We look forward to welcoming you at NM2 in beautiful Switzerland! Best regards from the scientific committee of NM2, Srikanth Ramaswamy Henry Markram Mriganka Sur Anita Disney Menahem Segal Michael Hasselmo Seung-Hee Lee Alexander Thiele Amy Arnsten

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Postdoctoral Fellowship: Enhancing Real-World Memory
by Christopher Honey Aug. 22, 2017

Aug. 22, 2017

POSTDOCTORAL FELLOW: ENHANCING REAL-WORLD MEMORY A fully-funded postdoctoral position is available in Dr. Christopher Honey's lab in the Department of Psychological and Brain Sciences at Johns Hopkins University (www.honeylab.org) Working with the Barense Lab at the University of Toronto, our team is developing a memory prosthetic device which enhances memory for everyday events. This postdoctoral research would lead investigations into how memory can be optimized by grouping, timing and framing of replay events. The ideal candidate would be conversant in both neuropsychological theory of memory as well as the computational analysis of real-world audiovisual data. Johns Hopkins is home to a large and vibrant community of neuroscience researchers, and features strong connections between psychology, systems and cognitive neuroscience, biomedical engineering and computer science. Postdoctoral fellows will have access to the MARCC computing cluster; a top-notch neuroimaging center, including 3T and 7T scanners; new TMS and EEG facilities are housed in the PBS department; human intracranial experiments are possible via neurology collaborators in Baltimore and Toronto. The targeted start date for this position is October 2017. The initial appointment is for one year, with the opportunity for renewal thereafter. The University is an Affirmative Action/Equal Opportunity Employer of women, minorities, protected veterans and individuals with disabilities and encourages applications from these and other protected group members. Consistent with the University's goals of achieving excellence in all areas, we will assess the comprehensive qualifications of each applicant. QUALIFICATIONS Candidates should have a PhD in a relevant field (e.g., psychology, cognitive science, neuroscience, physics, mathematics, computer science) and a record of published work related to memory and/or wearable devices. Strong programming skills are required. APPLICATION INSTRUCTIONS To apply, please email chris.honey(a)jhu.edu, providing: a current CV, a brief summary of research accomplishments and plans; two example publications or computational projects; contact information for three references.

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Postdoctoral researcher, neuroimaging & computational neuroscience
by Megan Peters Aug. 21, 2017

Aug. 21, 2017

Postdoctoral researcher, neuroimaging & computational neuroscience (Megan Peters) The Neural Computation Lab in the Department of Bioengineering at the University of California, Riverside, led by Dr. Megan Peters, is seeking a postdoctoral researcher. Topics of investigation include neural substrates of uncertainty, decision-making, and perceptual awareness using computational analysis and neuroimaging (fMRI, EEG). The successful candidate holds a Ph.D. or equivalent in computational neuroscience, bioengineering, applied math, computer science, or related field, and has substantial computational background in analyzing neuroimaging data (e.g., MVPA, connectivity, graph theory) and ideally computational modeling (Bayesian, signal detection theory, neural networks). Proficiency in at least one programming language (Matlab, Python, etc) is required. Our research group is part of a strong and lively neuroscience research community at UCR, and has strong ties to other groups in southern California and worldwide, including at UCLA (Hakwan Lau’s group; Michele Basso’s group; Alicia Izquierdo’s group), Advanced Telecommunications Research (ATR) in Kyoto (Mitsuo Kawato’s group), and Nagoya University (Kazuhisa Shibata’s group). Initial appointment will be for one year, with the possibility to renew up to five years. Compensation will be according to the NIH payscale. The University of California is an equal opportunity employer. Applications from women and underrepresented minorities are encouraged. To apply, please send a CV, statement of interests, up to 3 letters of recommendation and any relevant reprints to Dr. Peters at mpeters(a)engr.ucr.edu. We are accepting applications until the position is filled. -- Megan Peters, Ph.D. Assistant Professor Department of Bioengineering University of California, Riverside http://www.engr.ucr.edu/faculty/meganpeters.html

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postdoc in Barcelona to do single cell recordings in humans
by Quian Quiroga, Rodrigo (Prof.) Aug. 21, 2017

Aug. 21, 2017

*** Apologies for multiple postings*** Dear all, Please see below details of a postdoc position at the Barcelona ßeta Brain Research Center to study memory mechanisms by performing single cell recordings in humans in collaboration with Hospital del Mar. Cheers, Rodrigo Postdoc Position Barcelona ßeta Brain Research Center (BBRC) The Barcelona ßeta Brain Research Center (BBRC) offers a fully funded postdoc position at the research group headed by Professor Rodrigo Quian Quiroga. The group is part of a new Research Program on Memory, complementary to ongoing research at BBRC dealing with the Prevention of Alzheimer Disease. The group is associated to the epilepsy group headed by Dr. Gerardo Conesa at Hospital del Mar, a health center close to BBRC. Research will involve the study single cell recordings in humans. These recordings are performed in epileptic patients for curative surgery. In particular, the project will investigate concept cells (a.k.a. Jennifer Aniston neurons) – that means, neurons in the hippocampus and surrounding areas that selectively fire to specific concepts, like different pictures and the written or spoken name of a particular person (e.g. Quian Quiroga et al, Nature 2005; Quian Quiroga Nature Reviews Neuroscience 2012), which are involved in declarative memory functions. The successful candidate will contribute to performing recordings with patients at the Hospital del Mar and the analysis of the data to investigate the role of these neurons in memory formation, consolidation, long-term coding and recall. Candidates should have excellent quantitative skills (data processing, and programming, preferably in Matlab) and ideally, a strong background in neuroscience and experience with neurophysiology recordings. They should be able to work both independently and as part of a research team within the BBRC. The position is initially offered for 2 years, with the possibility to extend it for another 2 years. Salary will be competitive, depending on experience. The closing date for the applications is Monday 25th August 2017. Start date will be in October 2017. For application details and further particulars see: https://fpmaragall.org/en/wp-content/uploads/sites/3/2014/07/Postdoc_Neural… Informal inquiries are welcome and should be submitted electronically to: rh(a)fpmaragall.org Rodrigo Quian Quiroga Centre for Systems Neuroscience University of Leicester www.le.ac.uk/csn

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FW: 5th Baltic-Nordic School on Neuroinformatics BNNI 2017
by Marja-Leena Linne Aug. 20, 2017

Aug. 20, 2017

5th Baltic-Nordic School on Neuroinformatics BNNI 2017 Theoretical modelling of brain functions in neurological and psychiatric disorders: Advancing future neuroscience and medicine through Neuroinformatics methods Place: Neuroscience Institute Lithuanian University of Health Sciences Eiveniu str.4, Kaunas Lithuania Time: 6-7 October 2017 More information and applications at: http://web.lsmuni.lt/bnni/en Important dates: 3 September 2017: Application deadline 10 September 2017: Acceptance notification 6-7 October 2017: School We invite advanced master students, doctoral students, and postdoctoral researchers to participate in the BNNI 2017 to be held at the Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania. The course is interdisciplinary and covers modeling at different levels of organization of the brain, focusing on neurological and psychiatric disorders. The course combines lectures, tutorials and computer exercises. This school is the fifth in the series of the Baltic-Nordic Courses on Neuroinformatics (BNNI 2013 in Kaunas, Lithuania; BNNI 2014 in Tampere, Finland; BNNI 2015 in Tartu, Estonia; BNNI in Warsaw 2016, Poland). The target audience of the course will be advanced master students, doctoral students and postdoctoral researchers in biomedical and technology sciences, from medicine, biology, psychology, to mathematics, informatics, information technology, physics, chemistry, who would like to get an introduction to neuroinformatics and computational neuroscience. Some experience with programming and quantitative reasoning as well as strong interest in computational and mathematical modeling of the brain will be desirable but not necessary to benefit from the school. Last year we had participants from 14 countries all over the world! Faculty includes Marja-Leena Linne, Tampere University of Technology, Finland Bruce Graham, University of Stirling, UK Daniel Wojcik, Nencki Institute of Experimental Biology, Warsaw, Poland Arnd Roth, University College London, UK Peter Jedlicka, NeuroScience Center, Goethe-University, Frankfurt, Germany Tuomo Mäki-Marttunen, Simula Research Laboratory, Oslo, Norway Rima Naginiene, Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania Andrius Radziunas, Department of Neurosurgery, Medical Academy, and Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania Ausra Saudargiene, Neuroscience Institute, Lithuanian University of Health Sciences, and Vytautas Magnus University, Kaunas, Lithuania and others. Venue The lectures will be held at the Neuroscience Institute, Lithuanian University of Health Sciences, Eivenių str. 4, Kaunas, Lithuania Practical arrangements There is a small tuition fee of 50 euros. The organizers will provide teaching materials, meals and coffee during the course. The participants are expected to cover their travel and health insurance expenses as well as lodging. The course is supported by the Research Council of Lithuania and Lithuanian University of Health Sciences. We are looking forward to welcoming you at the BNNI 2017 in Kaunas, Lithuania!

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Late registration open for Integrated Systems Neuroscience
by Mark Humphries Aug. 17, 2017

Aug. 17, 2017

Dear colleagues, late registration for the Integrated Systems Neuroscience meeting is now open until Friday September 1st Please register here: https://www.bmh.manchester.ac.uk/conferences-meetings/isn2017/ ISN is a forum for the state-of-the-art in systems and computational approaches to key neural circuits. This year our speakers range across population coding; decision-making; hippocampus; whole CNS imaging; cortical dynamics; and motor circuits Our Speakers Rune Berg (Copenhagen University) Tiago Branco (Sainsbury Wellcome Centre for Neural Circuits) Neil Burgess (Institute of Cognitive Neuroscience, UCL) Megan Carey (Champalimaud Institute, Lisbon) Claudia Clopath (Imperial College, London) Kate Jeffery (UCL) Christian Machens (Champalimaud Institute, Lisbon) Jonathan Pillow (Princeton) Simon Peron (New York University) Manuel Zimmer (IMP, Austria) ISN2017 is being held in Manchester, UK, on the 7-8th September. Any questions? Late-breaking poster? Please contact us at isn2017(a)manchester.ac.uk<mailto:isn2017@manchester.ac.uk> Hope to see you in Manchester! Mark Humphries & Rasmus Petersen Dr Mark Humphries | MRC Senior non-Clinical Research Fellow | Faculty of Biology, Medicine & Health | University of Manchester @markdhumphries Public blog: medium.com/the-spike<http://medium.com/the-spike>

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Open position: Expert in machine learning and high-performance computing
by Daniel Zysman Aug. 16, 2017

Aug. 16, 2017

The Department of Brain and Cognitive Sciences seeks an expert in machine learning and high-performance computing to collaborate on cutting edge research on neuroscience, perception, cognition, and artificial intelligence. The department is a world leader in computational approaches to brain science, and is home to state-of-the-art computing resources as well as the Center for Brains, Minds and Machines, a multi-institutional NSF Science and Technology Center dedicated to the study of intelligence. Salary will be competitive with industry. To apply: https://careers.peopleclick.com/careerscp/client_mit/external/jobDetails.do… <https://careers.peopleclick.com/careerscp/client_mit/external/jobDetails.do…> For more information about our department: https://bcs.mit.edu/ <https://bcs.mit.edu/> Job Description: PROGRAMMER/SOFTWARE DEVELOPMENT ENGINEER/COMPUTATIONAL AND MACHINE LEARNING SPECIALIST, Brain and Cognitive Sciences, to help tackle challenging problems in high-performance computing methods and systems, machine learning, management of large datasets, and artificial intelligence. Responsibilities include helping researchers translate computational algorithms into efficiently functioning (especially parallelized and GPU optimized) code; assisting with installation and implementation of third-party tools; staying up-to-date with cutting-edge computational techniques; providing guidance for storage and management of large data sets; developing and maintaining online courses for computing resources, running in-person tutorials on specific software packages/tools, and helping transition users to new computing tools; providing educational support and training to users of the departmental computing cluster; and monitoring cluster usage and resolving problems. Job Requirements: Bachelor’s degree (advanced degree preferred) in a scientific field or computer science; at least four years’ experience in scientific high-performance (cluster) computing; familiarity with Slurm and Lustre; broad computational background with knowledge of Unix, HPC algorithms, GPU computing, programming models, debuggers, and performance tools; software development experience, especially Python and MATLAB, but also R, Lua, JavaScript, etc.; knowledge of high-level APIs for HPC computing; expertise installing and maintaining third-party software in an HPC environment and with container technology, especially in the context of computational reproducibility; familiarity with mathematical algorithms for high-performance computing, use or design of HPC profiling or optimization tools, and deep neural networks; ability to work effectively with scientists and engineers; initiative, tact, and judgment in developing solutions for users; excellent interpersonal skills and ability to communicate effectively, orally, in writing, and via live presentations; demonstrated ability to assume leadership roles, grasp complex problems, and develop solutions; and extensive background in high-performance computing. MIT is an equal employment opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, sex, sexual orientation, gender identity, religion, disability, age, genetic information, veteran status, ancestry, or national or ethnic origin. ------------------------------------------------------ Daniel Zysman Computational Course Co-Instructor MIT, Department of Brain & Cognitive Sciences

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Fully funded PhD studentship in Computational Neuroscience
by Vassilis Cutsuridis Aug. 16, 2017

Aug. 16, 2017

PhD Position in Computational Neuroscience (EU/UK candidates only) MLearn Research Group (http://mlearn.lincoln.ac.uk/) School of Computer Science University of Lincoln UK Applications are invited for a PhD position in the Machine Learning (MLearn) Research Group at the University of Lincoln. Projects involve the development and simulation of models of neuronal networks (microcircuits) of learning and memory in animal and human entorhinal cortex and hippocampus in health and disease (e.g. epilepsy, schizophrenia, Alzheimer's disease). A starting point of the project is the paper by Cutsuridis V, et al. (2010). Encoding and retrieval in the hippocampal CA1 microcircuit model. *Hippocampus*, 20(3): 423-446. <http://vassiliscutsuridis.com/docs/Journal_papers/CutCobGraHippo2010.pdf> <http://mlearn.lincoln.ac.uk> Student applicants should have excellent computational and numerical skills and a very good first degree in computer science, engineering, maths, physics, or a related discipline. Successful student candidates are eligible for a research studentship award from the University (GBP 14,553 per annum bursary plus payment of the student fees). This PhD studentship is ONLY OPEN to UK/EU students. The ideal candidate should be highly motivated, technically skilled, a team player and driven to push forward the development of the next generation of deep associative memory neural-networks. Your written and spoken English skills should be excellent. As a PhD student, you may also have the opportunity to teach as a demonstrator in the School of Computer Science. Research in Computer Science at the University of Lincoln has been recognised as internationally excellent and world leading. The School of Computer Science provides a very stimulating environment, offering a large number of specialised and interdisciplinary seminars as well as general training and researcher development opportunities. The University is situated in Lincoln, two hours northeast of London. Please contact Dr Vassilis Cutsuridis (vcutsuridis(a)lincoln.ac.uk) or Prof. Stefanos Kollias (skollias(a)lincoln.ac.uk) for informal enquiries. If you’re interested to apply, please email your CV, a brief statement of your scientific interests, and three reference letters to Dr. Vassilis Cutsuridis (vcutsuridis(a)lincoln.ac.uk) <vcutsuridis(a)lincoln.ac.uk> Applications will be assessed on a rolling basis. Vassilis Cutsuridis School of Computer Science University of Lincoln Lincoln LN6 7TS U.K. Tel: +44 (0) 1522 83 5107 Email: vcutsuridis(a)lincoln.ac.uk Web: http://staff.lincoln.ac.uk/vcutsuridis Personal Web: http://www.vassiliscutsuridis.com/

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Postdoctoral position / Optimizing NEST for Modular Supercomputing Architecture
by Hans Ekkehard Plesser Aug. 15, 2017

Aug. 15, 2017

com Dear Colleagues! I am looking for a post-doc to join my group to work on optimizing NEST (www.nest-simulator.org) for a novel modular supercomputing architecture developed by the DEEP-EST collaboration (www.deep-projects.eu) and to combine NEST simulations with simultaneous detailed LFP model (HybridLFPy, http://inm-6.github.io/hybridLFPy) or spike-train analysis (Elephant, neuralensemble.org/elephant) exploiting the heterogeneous DEEP-EST architecture. The position is funded by DEEP-EST, an EU FET-HPC project, coordinated by Forschungszentrum Jülich and involves academic and industry partners. The position is available immediately until the end of the project (June 2020). The full announcement for the position is available at https://www.jobbnorge.no/en/available-jobs/job/141149/postdoctoral-fellow-i… Please get in touch if you have any questions! Hans Ekkehard -- Dr. Hans Ekkehard Plesser Associate Professor Faculty of Science and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway Phone +47 6723 1560 Email hans.ekkehard.plesser(a)nmbu.no Home http://arken.nmbu.no/~plesser

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Leaders and followers: Quantifying consistency in spatio-temporal propagation pattern
by Thomas Kreuz Aug. 15, 2017

Aug. 15, 2017

Dear all, may I kindly draw your attention to our paper on the new multivariate directional measure *SPIKE-Order*. In this paper we propose a new approach to quantify consistency of spatio-temporal propagation patterns in sequences of discrete events (e.g. spike trains). This includes a sorting from leader to follower. As usual we show some applications to neurophysiological data. *Leaders and followers: Quantifying consistency in spatio-temporal propagation pattern <http://iopscience.iop.org/article/10.1088/1367-2630/aa68c3/meta>* Thomas Kreuz, Eero Satuvuori, Martin Pofahl and Mario Mulansky New J. Phys., *19*, 043028 (2017). Abstract: Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a *synfire pattern*. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed *SPIKE-order* and *spike train order*, that define the *synfire indicator* value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neuroscience (giant depolarized potentials in mice slices) and climatology (El Niño sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality. Implementations are provided online in three free code packages called SPIKY <http://www.fi.isc.cnr.it/users/thomas.kreuz/Source-Code/SPIKY.html> (Matlab GUI), PySpike <http://mariomulansky.github.io/PySpike/>(Python library) and, most recently, cSPIKE <http://www.fi.isc.cnr.it/users/thomas.kreuz/Source-Code/cSPIKE.html>(Matlab command line with MEX-files). Best regards, Thomas Kreuz PS: Three further recent articles: *Measures of spike train synchrony for data with multiple time scales <http://www.sciencedirect.com/science/article/pii/S0165027017301619>* Eero Satuvuori, Mario Mulansky, Nebojsa Bozanic, Irene Malvestio, Fleur Zeldenrust, Kerstin Lenk, Thomas Kreuz JNeurosci Methods *287*, 25 (2017). Background Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by adapting to the local firing rate they take into account all the time scales of a given dataset. New method In data containing multiple time scales (e.g. regular spiking and bursts) one is typically less interested in the smallest time scales and a more adaptive approach is needed. Here we propose the A-ISI-distance, the A-SPIKE-distance and A-SPIKE-synchronization, which generalize the original measures by considering the local relative to the global time scales. For the A-SPIKE-distance we also introduce a rate-independent extension called the RIA-SPIKE-distance, which focuses specifically on spike timing. Results The adaptive generalizations A-ISI-distance and A-SPIKE-distance allow to disregard spike time differences that are not relevant on a more global scale. A-SPIKE-synchronization does not any longer demand an unreasonably high accuracy for spike doublets and coinciding bursts. Finally, the RIA-SPIKE-distance proves to be independent of rate ratios between spike trains. Comparison with existing methods We find that compared to the original versions the A-ISI-distance and the A-SPIKE-distance yield improvements for spike trains containing different time scales without exhibiting any unwanted side effects in other examples. A-SPIKE-synchronization matches spikes more efficiently than SPIKE-synchronization. Conclusions With these proposals we have completed the picture, since we now provide adaptive generalized measures that are sensitive to firing rate only (A-ISI-distance), to timing only (ARI-SPIKE-distance), and to both at the same time (A-SPIKE-distance). *Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains <https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.022203>* Irene Malvestio, Thomas Kreuz, Ralph G Andrzejak Physical Review E *96*, 022203 (2017). The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L. Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data. *SPIKE-order <http://www.scholarpedia.org/article/SPIKE-order>* Thomas Kreuz, Eero Satuvuori, Mario Mulansky Scholarpedia, *12*(7):42441 (2017). -- 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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