- Comp-neuro - lists.cnsorg.org

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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Research Associate, Neuroimaging analyst, Western University
by Jorn Diedrichsen Aug. 15, 2017

Aug. 15, 2017

Western University has received a $66M investment from the Canada First Research Excellence Fund (CFREF). This investment will bring together researchers from across the University under a unifying initiative called BrainsCAN. The mission of BrainsCAN is to reduce the burden of brain disorders that affect sensory, cognitive, and motor functions. The initiative aims at extending and mobilizing knowledge of the mappings between neural circuits and mental functions to deliver evidence-based interventions. The Research Associate, Neuroimaging Analyst will apply their expertise and knowledge to support ongoing research projects directed by the principal investigators of the BrainsCAN initiative. The incumbent will become part of BrainsCAN’s Computational Core, which aims to develop new computational techniques for the analysis of behavioral, brain imaging, and neuronal data. The incumbent will play a lead role in training staff, students and postdoctoral fellows in the application of data analysis and modelling techniques. The incumbent will have developed an independent method-based research portfolio, and will support and operationalize new and innovative computational techniques. Working with individual laboratories, the incumbent will help to develop research and project plans, advise on data analysis techniques and statistical inference related to neuroimaging and electrophysiological research, and assist in the technical and statistical aspects of manuscripts and research reports. Deadline for applications: September 27th Further particulars: https://www.academicacareers.com/node/7227 Please contact me if you have further questions, Jörn Diedrichsen Western Research Chair Brain Mind Institute Department of Computer Science Department of Statistics Email: jdiedric(a)uwo.ca<mailto:jdiedric@uwo.ca> Tel: 1-519-661-2111 x86994

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Research associate position in matlab programming
by Tania Rinaldi Barkat Aug. 14, 2017

Aug. 14, 2017

Our lab, hosted by the Department of Biomedicine of Basel University, Switzerland, is currently seeking a research associate for a 6-month project. The role would be to write an analysis program in matlab for neuronal data from electrophysiological recordings, as well as analyze the data. JOB DESCRIPTION The first part of the project consists of writing an extension of a professional, user-friendly matlab program for the analysis of electrophysiological data based on an existing program we already have in the lab. The successful candidate will interact with experimentalists in our lab, understand their needs and produce a modular and user-friendly matlab program for extracellular neuronal recordings. The second part of the project consists of using this program to analyze data aimed at deepening our understanding of the brain. Start date is as soon as possible. The period of employment is 6 months, with possibility of extension. YOU ARE OUR NEW RESEARCH ASSOCIATE IF YOU: - Have extensive programming experience in matlab - Have previously worked with neuroscience data, ideally electrophysiological recordings - Have the dual ability of analyzing researchers’ need and translating it into code - Are a creative problem-solver - Are a self-starter and an independent researcher ABOUT THE LAB www.brainsoundlab.com The aim of our lab is to understand the role of specific neural circuits in making sense of sounds. We combine optogenetics, in vivo electrophysiology, voltage-sensitive dye imaging and behavioral assays to explore the functions of neuronal circuits in the mouse auditory cortex. To apply, please send your CV and a cover letter briefly summarizing your research interests to Tania Barkat (tania.barkat(a)unibas.ch) --------------------------- Tania Rinaldi Barkat Assistant Professor in Neurophysiology Basel University Department of Biomedicine, room 7001 Klingelbergstrasse 50-70 4056 Basel Switzerland +41 61 207 1638 www.brainsoundlab.com

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International Brain Laboratory Staff Positions Ads
by Zachary Mainen Aug. 11, 2017

Aug. 11, 2017

Dear all, Please see below 5 available positions for staff with the International Brain Lab. Dissemination through your contacts would be much appreciated. Best, Zach Mainen --- The International Brain Laboratory (https://www.internationalbrainlab.com/) is seeking 5 collaborators who will play key roles in a new large-scale international collaboration in brain research. The IBL combines the efforts of approximately 50 scientists in 20 laboratories toward understanding the brain-wide basis of a complex behavior. The project will involve recording the activity of millions of neurons in the working brain and building mathematical models of the resulting data. The data sets this project will produce are vast and complex, including physiological recordings, behavioural measurements, and video, all of which must be standardized into common formats, integrated into a single database, and subjected to quality control. These core positions will support the both experimental and theoretical work through the entire lifecycle of data, including acquisition, analysis, modeling and dissemination. Project Manager (PM) - The primary responsibilities of the PM will include ensuring delivery of key milestones, managing overall project budget and finances, facilitating project governance and internal communication, and liaising with funders, external agencies and partners. Candidates should have demonstrated success in project management of a comparable scale and a strong background in scientific research. Technical Manager (TM) - The TM will be responsible for ensuring reproducibility of experiments across labs by (1) helping to establish and maintain a common set of experimental apparatus for behavior and neural recordings and (2) establishing procedures for standardizing, monitoring and troubleshooting experimental conditions, including animal subjects, surgical procedures, materials, etc. The position will involve extensive travel between ten experimental laboratories at six institutions across the U.S. and Europe. The ideal candidate would have a PhD in neuroscience and very strong technical and experimental skills. Strong organizational and interpersonal skills are required. Prior experience in laboratory or project management is desirable. Data Coordinator (DC) - The DC will work closely with our contributing labs and scientific programming staff to ensure the integrity and organization of data collected by experimental labs. The ideal candidate would have a PhD in neuroscience, and extensive experience with large neurophysiological and behavioral datasets. Attention to detail and ability to work collaboratively are both essential. Strong computing ability is also important, including knowledge of relational databases, Python and MATLAB. Scientific MATLAB Programmer (SMP) - The SP will contribute to the design of applications and pipelines for organization, storage and analysis of this data. The job will involve building software and data infrastructure for this collaborative research project, and supporting it. The ideal candidate will have extensive experience with MATLAB and previous work with big data in scientific research. Knowledge of Python and relational databases and experience interfacing with experimental control hardware is desirable. Senior Scientific Programmer (SSP) - The SSP will lead the design of applications and pipelines for organization, storage and analysis of this data. The job will involve building software and data infrastructure, as well as a role in ensuring data integrity, working closely with data providers to ensure it meets project standards. The ideal candidate will have extensive experience with relational databases and Python. Experience with MATLAB, previous work with big data in scientific research, and experience interfacing with experimental control hardware would also be desirable. Remuneration will be competitive and commensurate with experience. There is considerable flexibility in base location within the U.S. and Europe. To apply, please send cover letter and CV to info(a)internationalbrainlab.com with subject: PositionAcronym LastName. Review of applications will begin immediately and continue until the position is filled, with an ideal start date of Sept.-Oct., 2017. --- Zachary F. Mainen, Ph.D. Champalimaud Neuroscience Programme Champalimaud Centre for the Unknown Av. Brasília s/n 1400-038 Lisbon Portugal +351 210 480 100 zmainen(a)neuro.fchampalimaud.org <zmainen(a)fchampalimaud.org> www.neuro.fchampalimaud.org/group/mainen @zmainen

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Postdoc Position in Retina Research / Computational Neuroscience
by Gollisch, Tim Aug. 11, 2017

Aug. 11, 2017

A postdoc position is available in the lab of Tim Gollisch at the University Medical Center Göttingen, Germany. The group studies information processing and neural coding in the neural network of the vertebrate retina, using a combination of electrophysiological and computational approaches. Experimental methods include extracellular multielectrode-array recordings and intracellular recordings from neurons in the isolated retina (mouse and salamander), using both wild-type retinas and optogenetic retina models of vision restoration therapy. A strong focus of the group is to combine these experiments with novel tools for data analysis and mathematical modeling. Our research group if part of the strong and lively neuroscience research community of Göttingen, including the university, several Max Planck Institutes, the German Primate Center, and the European Neuroscience Institute. We are also part of the Bernstein Center for Computational Neuroscience Göttingen and of the Collaborative Research Center "Cellular Mechanisms of Sensory Processing" (http://sfb889.uni-goettingen.de/) For more information about the research group, please visit the group's website: http://www.retina.uni-goettingen.de/. We are looking for a highly motivated scientist with a good background in electrophysiology or computational/theoretical neuroscience. Experience in both of these areas is a bonus, but not required. Experience in computer programming or in scripting of data analysis routines is also a plus. The position is for 2 years initially with the possibility for renewal. Please send your application to Tim Gollisch (tim.gollisch(a)med.uni-goettingen.de<mailto:tim.gollisch@med.uni-goettingen.de>), including a CV, a statement of what interests you about the group's work, and contact details for two references. The University Medical Center Göttingen is an equal opportunities employer, and women are especially encouraged to apply. Applicants with disabilities and equal qualifications will be given preferential treatment. -- Prof. Dr. Tim Gollisch University Medical Center Goettingen, Dept. of Ophthalmology Waldweg 33, 37073 Goettingen Tel. +49 (0)551 39-13542 tim.gollisch(a)med.uni-goettingen.de<mailto:tim.gollisch@med.uni-goettingen.de> www.retina.uni-goettingen.de<http://www.retina.uni-goettingen.de>

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[INNS-BDDL 2018] Call for Papers
by Teng Teck Hou Aug. 11, 2017

Aug. 11, 2017

[Apologies for cross-postings] ########################################################### CALL FOR PAPERS The 3rd INNS Conference on Big Data and Deep Learning 2018 April 17-19, 2018, Sanur - Bali, Indonesia Homepage: http://www.innsbigdata2018.org #######################Description:###################### The International Neural Network Society (INNS) is the premiere organization for individuals interested in a theoretical and computational understanding of the brain and applying that knowledge to develop new and more effective forms of machine intelligence. INNS was formed in 1987 by the leading scientists in the neural network field. Researchers and colleagues who work in the area of big data and machine learning, we are happy to announce "The 3 rd INNS Conference on Big Data and Deep Learning 2018 (INNS BDDL 2018) will be held on April 18 19, 2018 in Sanur Bali, Indonesia. The aim of this conference is to create a valuable and important forum for scientists and engineers throughout the world to present the latest research findings and idea at the forefront of Big Data and Deep Learning. Accepted papers will be published by Elsevier, Scopus indexed. Several papers will be selected for possible publication in top journals. The conference will feature a comprehensive technical program with technical tracks on: Track 1: Big Data Track 2: Big Data Algorithms Track 3: Deep Learning Track 4: Application Areas Important Dates ################################################################### * Tutorial and workshop proposals (Submission) 15 September 2017 * Tutorial and workshop proposals (Decision) 30 September 2017 * Paper submission 2 November 2017 * Decision notification 31 December 2017 * Conference 17 - 19 April 2018 ################################################################### Previous INNS Conference: INNS 2016 in Thessaloniki, Greece INNS 2015 in San Francisco, USA #################### Organizing committees ############### General chairs Seiichi Ozawa, Kobe University, Japan Ah-Hwee Tan, Nanyang Technological University, Singapore Program Chairs Plamen P. Angelov, Lancaster University, UK Asim Roy, Arizona State University, USA Mahardhika Pratama, Nanyang Technological University, Singapore Local Committee Chairs Dieky Adzkiya, Institut Teknologi Sepuluh Nopember, Indonesia Advisory Board Yew-Soon Ong, Nanyang Technological University, Singapore Robert Kozma, University of Memphis, USA Sankar K. Pal, Indian Statistical Institute, India Haibo He, University of Rhode Island, USA Witold Pedrycz, University of Alberta, Alberta, Canada Leszek Rutkowski, Czestochowa University of Technology, Poland Nikola Kasabov, Auckland University of Technology, New Zealand Fernando Gomide, University of Campinas, Brazil Marley Vellasco, Pontifícia Universidade Católica do Rio de Janeiro, Brazil Yoonsuck Choe, Texas A&M University Minho Lee, Kyungpook National University, South Korea Bao-Liang Lu, Shanghai Jiao Tong University, China Irwin King, the Chinese University of Hong Kong, Hong kong Mohammad Nuh, Institut Teknologi Sepuluh Nopember, Indonesia Joni Hermana, Institut Teknologi Sepuluh Nopember, Indonesia Heru Setyawan, Institut Teknologi Sepuluh Nopember, Indonesia Tutorials/Workshop Chairs Igor Skrjanc, University of Ljubljana, Slovenia Sundaram Suresh, Nanyang Technological University, Singapore Poster Sessions Chairs Eko Setiadji, Institut Teknologi Sepuluh Nopember, Indonesia Agus Salim, La Trobe University, Australia Special Sessions Chairs Justin Wang, La Trobe University, Australia Yongping Pan, National University of Singapore, Singapore Panel Chairs Sreenatha Anavatti, University of New South Wales, Australia Mukesh Prasad, University of Technology, Sydney, Australia Achmad Affandi, Institut Teknologi Sepuluh Nopember, Indonesia Awards Chairs Tapabrata Ray, University of New South Wales, Australia Dejan Dovzan, University of Ljubljana, Slovenia Richard J. Oentaryo, McLaren Applied Technologies, Singapore Publication Chairs Edwin Lughofer, Johannes Kepler University, Austria Jose Antonio Iglesias, Carlos III University of Madrid, Spain Moamar Sayed?Mouchaweh, Institute Mines Telecom Lille Douai, France Publicity Chair Simone Scardapane, Sapienza University, Italy Teng Teck Hou, Singapore Management University, Singapore Hendro Nurhadi, Institut Teknologi Sepuluh Nopember, Indonesia International Liaison Chairs Yun Sing Koh, University of Auckland, New Zealand Deepak Puthal, University of Technology Sydney, Australia Wirawan, Institut Teknologi Sepuluh Nopember, Indonesia Webmaster Mohamad Abdul Hady, Institut Teknologi Sepuluh Nopember, Indonesia Andri Ashfahani, Institut Teknologi Sepuluh Nopember, Indonesia Choiru Zain, La Trobe University, Australia ###### Topics and Areas include, but not limited to the following###### >>BIG DATA Autonomous, online, incremental learning in big data High dimensional data, feature selection, feature transformation for big data Scalable algorithms for big data Big data analytics Data stream analytics Parallel & distributed computing for big data analytics (cloud, map-reduce, etc.) Online learning Online multimedia/stream/text analytics Link and graph mining Big data and cloud computing, large scale stream processing on the cloud Big data and collective intelligence/collaborative learning Big data and hybrid systems Big data and self-aware systems Big data and infrastructure Big data visualization >>Big Data Algorithm Neuromorphic hardware for scalable machine learning Evolving systems for big data analytics Evolutionary systems and big data Fuzzy systems and big data Cognitive modelling and big data Probabilistic approach for big data Concept drift detection for big data Granular computing for big data Transfer learning for big data >>Deep Learning Deep belief network Convolutional neural network Long short term memory Deep network architecture Deep autoencoder Deep stacked network Deep learning for natural language processing Deep learning for machine vision Evolving deep network Transfer learning in deep learning Online deep learning >>Application Areas Banking and Securities Communications, Media and Entertainment Healthcare Providers Education Manufacturing & Natural Resources Government Insurances Retail & Wholesale Trade Transportation Energy & Utilities, Etc. ##########################Sponsoring Organizations########################## * INNS - International Neural Network Society * MTC - Mechatronic Technology Center, Institut Tecknologi Sepuluh Nopember ############################################################################

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Bernstein 2017 Workshop: "Multiscale modeling and simulation"
by Salvador Dura Aug. 10, 2017

Aug. 10, 2017

Dear All, We would like to bring your attention to the "Multiscale modeling and simulation" workshop at the Bernstein Conference 2017, Sept 12-13, Göttingen, Germany. Website: http://www.nncn.de/en/bernstein-conference/2017/satellite-workshops/multisc… Short abstract: Understanding brain function requires characterizing the interactions occurring across many temporal and spatial scales. Mechanistic multiscale modeling aims to organize and explore these interactions across these scales. Multiscale models can provide insights into how changes at the molecular and cellular levels affect dynamics at local networks and brain areas. At the highest levels, they allow us to connect neural activity to theories of behavior, memory and cognition. The recent introduction of large neuroscience projects in US and EU Brain Research through Advancing Innovative Neurotechnologies (BRAIN) and Human Brain Project (HBP) respectively provide an opportunity to rapidly gather new and more accurate data to incorporate into the multiscale models. This workshop will present some of the latest multiscale modeling tools and approaches in neuroscience, as well as representative examples of datadriven multiscale neural simulations. The workshop aims to encourage both computational and experimental neuroscientists to make use of multiscale modeling in their research. Organizers: Salvador Dura-Bernal and Bill Lytton (SUNY Downstate) Speakers: Tue, Sept 12: - Bill Lytton (SUNY Downstate) - Jean Pierre Changeux (Pasteur Institute) - Volker Steuber (University of Hertfordshire) - Robert McDougal (Yale University) - Salvador Dura-Bernal (SUNY Downstate) - Cliff C Kerr (University of Sydney) Wed, Sept 13: - Szabolcs Kali (Hungarian Academy of Science/HBP) - Marianne Bezaire (Boston University) - Daniel Durstewitz (ZI Mannheim) - Kael Dali (Allen Institute for Brain Science) We look forward to seeing you there! Best Salva -- Salvador Durá-Bernal, Ph.D. Research Assistant Professor State University of New York Downstate Medical Center

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