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Postdoctoral position studying neurovascular coupling

by Patrick Drew

A postdoctoral fellowship studying the neural circuits involved in neurovascular coupling is available in the laboratory of Patrick Drew. We are looking for an independent, highly motivated scientist to use a combination of optical (two-photon microscopy and intrinsic imaging), optogenetic, and electrophysiological techniques to study neurovascular coupling in awake, behaving mice. The successful applicant will have Ph.D. in neuroscience, biomedical engineering, physiology or a related field, and at least one first author publication. The candidate should have excellent English communication skills, experience with in vivo electrophysiology, optogenetics and/or optical imaging techniques. Strong data analysis and quantitative skills are required. The position is for one year, with possibility of extension up to two additional years contingent on funding. Applications will be reviewed upon receipt and will be accepted until the position is filled. Apply to job #52876 at http://apptrkr.com/504686 More information about the lab can be found at - http://www.esm.psu.edu/~patrick/Drew_Lab/Drew_Lab.html. Applications must be submitted electronically and include a statement of research interests, CV and contact details of three letters of reference to Dr. Patrick Drew: pjd17(a)psu.edu _________________________________ Patrick J. Drew Assistant Professor Center for Neural Engineering Dept. Engineering Science and Mechanics & Dept. Neurosurgery The Pennsylvania State University W-317 Millennium Science Complex University Park, PA 16802 (814) 863-1473 http://www.esm.psu.edu/~patrick/Drew_Lab/

Sept. 1, 2014

Call for Papers - Neural Networks special issue on Big Data

by Asim Roy

Apologies for cross posting. Special Issue on Neural Network Learning in Big Data [Special Issue on Neural Network Learning in Big Data] Neural Networks Special Issue: Neural Network Learning in Big Data Big data is much more than storage of and access to data. Analytics plays an important role in making sense of that data and exploiting its value. But learning from big data has become a significant challenge and requires development of new types of algorithms. Most machine learning algorithms encounter theoretical challenges in scaling up to big data. Plus there are challenges of high dimensionality, velocity and variety for all types of machine learning algorithms. The neural network field has historically focused on algorithms that learn in an online, incremental mode without requiring in-memory access to huge amounts of data. The brain is arguably the best and most elegant big data processor and is the inspiration for neural network learning methods. Neural network type of learning is not only ideal for streaming data (as in the Industrial Internet or the Internet of Things), but could also be used for stored big data. For stored big data, neural network algorithms can learn from all of the data instead of from samples of the data. And the same is true for streaming data where not all of the data is actually stored. In general, online, incremental learning algorithms are less vulnerable to size of the data. Neural network algorithms, in particular, can take advantage of massively parallel (brain-like) computations, which use very simple processors, that other machine learning technologies cannot. Specialized neuromorphic hardware, originally meant for large-scale brain simulations, is becoming available to implement these algorithms in a massively parallel fashion. Neural network algorithms, therefore, can deliver very fast and efficient real-time learning through the use of hardware and this could be particularly useful for streaming data in the Industrial Internet. Neural network technologies thus can become significant components of big data analytics platforms and this special issue will begin that journey with big data. For this special issue of Neural Networks, we invite papers that address many of the challenges of learning from big data. In particular, we are interested in papers on efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of online learning to solve real-world big data problems (e.g. health care, transportation, and electric power and energy management). RECOMMENDED TOPICS: Topics of interest include, but are not limited to: 1. Autonomous, online, incremental learning - theory, algorithms and applications in big data 2. High dimensional data, feature selection, feature transformation - theory, algorithms and applications for big data 3. Scalable neural network algorithms for big data 4. Neural network learning algorithms for high-velocity streaming data 5. Deep neural network learning 6. Neuromorphic hardware for scalable neural network learning 7. Big data analytics using neural networks in healthcare/medical applications 8. Big data analytics using neural networks in electric power and energy systems 9. Big data analytics using neural networks in large sensor networks 10.Big data and neural network learning in computational biology and bioinformatics SUBMISSION PROCEDURE: Prospective authors should visit http://ees.elsevier.com/neunet/ for information on paper submission. During the submission process, there will be steps to designate the submission to this special issue. However, please indicate on the first page of the manuscript that the manuscript is intended for the Special Issue: Neural Network Learning in Big Data. Manuscripts will be peer reviewed according to Neural Networks guidelines. Manuscript submission due: December 15, 2014 First review completed: March 1, 2015 Revised manuscript due: April 1, 2015 Second review completed, final decisions to authors: April 15, 2015 Final manuscript due: April 30, 2015 GUEST EDITORS: Asim Roy, Arizona State University, USA (asim.roy(a)asu.edu<mailto:asim.roy@asu.edu>) (lead guest editor) Kumar Venayagamoorthy, Clemson University, USA (gkumar(a)ieee.org<mailto:gvenaya@clemson.edu>) Nikola Kasabov, Auckland University of Technology, New Zealand (nkasabov(a)aut.ac.nz<mailto:nkasabov@aut.ac.nz>) Irwin King, Chinese University of Hong Kong, China (irwinking(a)gmail.com<mailto:irwinking@gmail.com>)

Sept. 1, 2014

PhD top-ups and full scholarships in Computational Neuroscience of Alertness

by Svetlana Postnova

*PhD top-ups and full scholarships in Computational Neuroscience of Alertness* Multiple PhD opportunities are available at the University of Sydney node of the Cooperative Research Centre for Alertness, Safety, and Productivity. *Supervisors: *Dr Svetlana Postnova, Dr Jong Won Kim*, *Prof. Peter Robinson. For the group website see http://sydney.edu.au/science/physics/research/complex-systems/brain-dynamic… *Projects* We focus on understanding the biological system controlling alertness and on developing quantitative models and tools to predict alertness in variety of situations and develop interventions to improve it. All projects involve quantitative modelling on the level of interacting brain nuclei and neurotransmitter systems with the effects manifested on the behavioural level. Project 1: Effects of light wavelength on the circadian system and alertness. Project 2: Individual variability in sleep-wake dynamics and vulnerability to shiftwork. Project 3: Real-time prediction of alertness using data fusion and modelling techniques. These opportunities are affiliated with the Cooperative Research Centre for Alertness Safety and Productivity which involves modellers, experimentalists, and industry partners from universities and industries across Australia and worldwide. Successful candidates will be located at the School of Physics of the University of Sydney and collaborating with sleep and chronobiology experimentalists across the University of Sydney, Monash University, and Harvard. All PhD projects provide an opportunity for up to 6 months placements with industrial partners. *Process and deadlines* For questions and to apply send an e-mail with CV to Dr Svetlana Postnova ( postnova(a)physics.usyd.edu.au) Full stipends ($25,392) from the Alertness CRC Ltd are available. High achieving applicants will be encouraged to apply for the prestigious APA/UPA (Australian) or IPRS/USYDIS (international) postgraduate scholarships. Successful candidates will be provided with additional top-up scholarships of $5000-$10.000 per year depending on merit. Applications for Alertness CRC Ltd stipends will be considered until the positions are filled. Applications for APA/UPA national scholarships close on the *31st of October 2014* to start in January 2015*. * http://sydney.edu.au/scholarships/research/postgraduate_awards.shtml#APAUPA Applications for IPRS/USYDIS international scholarships close on the *15th of December 2014 * to start in July 2015*.* http://sydney.edu.au/scholarships/prospective/international_postgraduate_sc… Best wishes Sveta -- Dr Svetlana Postnova CRC for Alertness Safety and Productivity & Complex Systems Group School of Physics, University of Sydney Physics Annex, A29, NSW 2006, Australia Phone: +61 2 9036 7971

Sept. 1, 2014

Donders Discussions 2014: by PhDs, for PhDs

by Charl

Dear all, We are pleased to announce the Donders Discussions 2014, a two-day conference for PhD students in neuroscience and cognition. This year, the conference will take place on Oct 30th and 31st in Nijmegen, The Netherlands. The aim of the Donders Discussions is to bring PhD students together in an informal, interdisciplinary atmosphere. Last year we welcomed more than 160 participants from 14 countries. We invite you to join us and make this year's edition an even bigger success! Our exciting programme features many interesting topics including visual perception, fear regulation, social neuroscience, and predictive coding among others. We also offer interactive workshops on networking and visibility, statistics issues in neuroimaging, and the art of presenting science. We warmly invite all participants to submit a poster abstract. The deadline is September 15, but registration may close earlier if the maximum number of participants has been reached. For more information on the programme and registration, please visit our website: http://www.ru.nl/dondersdiscussions. The registration fee is 50 euro. We look forward to seeing you in Nijmegen! The Donders Discussions Organizing Committee 2014

Sept. 1, 2014

POSTDOCTORAL POSITIONS IN COMPUTATIONAL NEUROSCIENCE AT BROWN UNIVERSITY

by Wilson Truccolo

POSTDOCTORAL POSITIONS IN COMPUTATIONAL NEUROSCIENCE AT BROWN UNIVERSITY: Applications for NIH-funded post-doctoral positions are invited for the Laboratory of Dr. Wilson Truccolo in the Department of Neuroscience at Brown University, Providence, RI, USA. The positions are for the development of data-driven stochastic and biophysical models of multiscale neural dynamics in human epilepsy. Data consist of simultaneous intracortical microelectrode array recordings of ensembles of single neurons and local field potentials, as well as ECoGs, during seizure and non-seizure states in individuals with intractable epilepsy. The project involves a collaboration between the labs of Dr. Truccolo and Dr. Sydney Cash (Massachusetts General Hospital, Harvard Medical School, Boston). The ideal candidate will have a strong background in computational/theoretical neuroscience and stochastic modeling of neural dynamics. Related recent publications can be found on the Truccolo lab website ( http://dl.dropbox.com/u/983891/Truccolo_home.htm) Inquiries should be addressed to Dr. Truccolo (wilson_truccolo(a)brown.edu) Applications including a CV, statement of research interests, and the names and full contact details of three referees should be sent to wilson_truccolo(a)brown.edu. -- Wilson Truccolo, Ph.D. Pablo J. Salame '88 Goldman Sachs Assistant Professor of Computational Neuroscience Institute for Brain Science and Department of Neuroscience, Brown University S.E. Frank Hall, 185 Meeting Street, Office 552 Providence – RI – 02912

Sept. 1, 2014

Two Postdoctoral Positions in Computational Modeling of Primate Motor Control

by Ilya Rybak

=============== Two postdoctoral/research associate positions in computational neuroscience for the duration of up to three years are available in the externally funded project focusing on multiscale computational modeling of motor control mechanisms, involving interactions between basal ganglia, thalamus, cerebellum, cortex, and spinal cord in connection with realistic simulation of pathological changes in motor control during Huntington’s disease. This collaborative project is performed jointly by the groups of Drs. Ilya Rybak (Dept. of Neurobiology and Anatomy, Drexel University, Philadelphia, PA, USA) and Yaroslav Molkov (Dept. of Mathematical Science, Indiana University – Purdue University, Indianapolis, IN, USA). Applicants must have a PhD degree in Neuroscience, Applied Mathematics, Physics, or a related discipline and have excellent C++ programming skills. Previous experience in computational neuronal modeling is a plus. Interested candidates are encouraged to contact Dr. Rybak (rybak(a)drexel.edu) or Dr. Molkov (ymolkov(a)iupui.edu) by email. To apply, please send an email along with (1) curriculum vitae; (2) a cover letter describing previous research experience and interests; (3) the names and contact information of three references. ================================= Ilya A. Rybak, Ph.D. Professor Department of Neurobiology and Anatomy Drexel University College of Medicine 2900 Queen Lane Philadelphia, PA 19129 USA Tel.: (215) 991-8596 FAX: (215) 843-9082 E-mail: rybak(a)drexel.edu http://neurobio.drexelmed.edu/rybakweb/rybak.html http://www.drexelmed.edu/Home/AboutOurFaculty/IlyaARybak.aspx https://www.researchgate.net/profile/Ilya_Rybak/publications/ http://scholar.google.com/citations?hl=en&user=SnA068QAAAAJ http://www.pubfacts.com/author/Ilya+A+Rybak Yaroslav Molkov, Ph.D. Assistant Professor Indiana University - Purdue University Indianapolis Department of Mathematical Sciences 402 N. Blackford St, LD 270, Indianapolis, IN 46202 Office: LD 224U Phone: (317) 274-6934 Fax: (317) 274-3460 Email: ymolkov(a)iupui.edu

Aug. 31, 2014

Post-Doctoral Position; Indianapolis, USA – Paris, France

by Alexey Kuznetsov

Post-Doctoral Position in Computational Neuroscience; Indianapolis, USA – Paris, France A post-doctoral position is available for a USA-France collaborative project on the influence of alcohol on the central dopamine system. The project combines computational and experimental techniques. The post-doc will design a biophysical model of the brain circuitry affected by alcohol. The position is available immediately, September 2014, for up to three years. The time will be divided between two institutions: Indiana University-Purdue University at Indianapolis, USA, and École Normale Supérieure, Paris, France. In Indianapolis, the post-doc will work with Prof. Kuznetsov (Mathematics Dept.) and Prof. Lapish (Psycology Dept.). In Paris, he or she will work with Prof. Gutkin (Group of Neural Theory, ENS) and Prof. Mamelli (Institut du Fer a Moulin, INSERM). Both locations provide an excellent environment for collaborative research in quantitative biosciences. Applicants should have a Ph.D in Mathematics, Physics, or a related field, a background in Dynamical Systems, programming skills and working experience in Computational Neuroscience. An experience with stochastic processes is also a plus. To apply, please send your CV, list of publications, research statement, and contact information for three references to Prof. Alexey Kuznetsov via e-mail: askuznet(a)iupui.edu, or by regular mail: 402 N. Blackford Street, LD 270, Indianapolis, IN 46202. IUPUI is an EEO/AA Employer, M/F/D. -- Alexey Kuznetsov Associate Professor Dept. of Mathematical Sciences, IUPUI Science Building, LD 270, 402 N. Blackford St., Indianapolis, IN

Aug. 30, 2014

PhD position in Visual Psychophysics at the Institute of Vision in Paris, France

by Angelo ARLEO

One fully-funded PhD position in Visual Psychophysics at the Institute of Vision in Paris, France Job description Applications are invited for one PhD position in the Aging in Vision and Action laboratory at the Institute of Vision (INSERM-CNRS-University Pierre & Marie Curie) Paris, France. The gradual impairment of vision-dependent functions in the elderly is at the core of the research carried out in the Aging in Vision and Action laboratory. The main goal is to characterize how healthy aging shapes perceptual and cognitive aspects of vision. This PhD project will focus on the impact of healthy aging on low-level visual perception and visual processing functions. Requirements Applicants’ background should be in cognitive neuroscience/psychology. Previous experience with visual psychophysics, eye-movement analysis, visual space perception, or aging is welcome. Computer programming skills are also a plus. Proficiency in oral and written English is required. Knowledge of French is not mandatory. Successful candidates are expected to work in an interdisciplinary environment with collaborations with biologists, theoreticians and clinicians. They will be awarded a 3-year PhD fellowship from the University Pierre and Marie Curie Paris. Working environment and laboratory The Institute of Vision is one of the top international centers for integrated research on vision and eye diseases. It is located at the heart of Paris, on the campus of the National Hospital Center for Ophthalmology. The Institute of Vision includes multidisciplinary research groups, which share state-of-the-art platforms for human and animal experimentation. It also harbors a clinical investigation center, which fosters truly translational research activity. More information can be found online (http://www.institut-vision.org) The Institute of Vision and Essilor International, world leader in ophthalmic optics, have recently supported the creation of the laboratory of Aging in Vision and Action. This new laboratory aims at evaluating and understanding the functional aspects of the degeneration mechanisms related to visual aging. This research has the potential to produce fundamental knowledge suited for opening to assistive technological developments and rehabilitation solutions. The faculty members of the Aging in Vision and Action laboratory, led by Angelo Arleo, are specialized in visual psychophysics, neurobiology of spatial orientation, neural coding, neurocomputational modeling, and preclinical evaluation. The group has access to a wide range of state-of-the-art platforms including eye trackers, motion capture rooms, virtual reality environments, brain imaging, artificial street labs and home labs (http://www.streetlab-vision.com/en/) How to apply Candidates should send a single pdf file containing a motivation letter, a full CV, and names and contact information of at least two referees, to angelo.arleo(a)inserm.fr. Short listed candidates will be contacted for an interview (either face-to-face or via videoconference). For further inquiries, please contact Angelo Arleo, angelo.arleo(a)inserm.fr, phone: +33 (0)1 53 46 26 52. Angelo ARLEO Institute of Vision, Aging in Vision and Action Lab, CNRS - INSERM - University Pierre&Marie Curie, 17, rue Moreau F-75012 Paris, France Phone: +33 (0)1 53 46 26 52 Mobile: +33 (0)6 89 89 07 23 email: angelo.arleo(a)inserm.fr; angelo.arleo(a)upmc.fr

Aug. 29, 2014

PhD Studentship in Computer Science: Understanding and modelling emergent functionality in small biological neural systems

by Peter Andras

PhD Studentship in Computer Science Understanding and modelling emergent functionality in small biological neural systems Reference Number: EPSAM2014-1 Application Deadline: 15th September 2014 Supervisor: Professor Peter Andras, School of Computing and Mathematics, Keele University Project description: The functionality of biological neural systems emerges through the interaction of neurons. Recent technological advances allow the recording and analysis of many neurons individually, making possible the understanding and modelling the emergence of system level functionality in biological neural circuits. This project will use as model system the stomatogastric ganglion (STG) of brown crabs. The focus will be on the understanding how the roles of neurons may change in response to neuromodulation in the context of changing functional behaviour of the pyloric rhythm neural circuit within the STG. We will use fast voltage-sensitive dye imaging to collect the data about the activity of neurons and computational modelling of these neurons and the impact of neuromodulators on them in order to model and explain the observed activity of the STG. We aim to analyse the roles of apparently functionally equivalent neurons (e.g. two PD neurons, five PY neurons) and model computationally the changes in their functional roles in response to neuromodulation. The successful candidate will be expected to learn the relevant biological experimental techniques, perform biological experiments, learn the relevant computational modelling techniques, and build and analyse computational models of neurons and neural circuits. The stipend is £13,863 (2014/15) per year. The studentship is not available for candidates from outside of the EU. Person specification: You should have either a First class honours degree in Computing Science, Mathematics, Physics, Biology or other relevant science or engineering subject, or a or 2.1 in Computing Science, Mathematics, Physics, Biology or other relevant science or engineering subject and a distinction level Masters degree in a related subject. Equivalent experience will also be considered. Application procedure: You must apply through the Universitys online postgraduate application website (http://www.keele.ac.uk/pgresearch/howtoapply/) specifying the reference number EPSAM2014-1 and selecting PhD in Computer Science, as the programme of study. Only mandatory fields need to be completed and a personal statement, CV and (if English is not your first language) a copy of your English language qualifications must be attached. The personal statement must state the title of the studentship, quote the reference number EPSAM2014-1 and describe how your research interests fit with the topic of the research projected outlined in the advertisement (max. 2 pages). If you already have published research papers a list of these providing bibliographic details should be included in the covering letter. You should also send your personal statement and CV by e-mail to Professor Peter Andras, ( <mailto:p.andras@keele.ac.uk> p.andras(a)keele.ac.uk) Further information: For further details, please contact Professor Peter Andras ( <mailto:%20p.andras@keele.ac.uk> p.andras(a)keele.ac.uk) 01782 733412 For general enquiries contact: http://www.keele.ac.uk/pgresearch/howtoapply --- Professor Peter Andras Professor of Computer Science and Informatics Head of Computing Division School of Computing and Mathematics Keele University Keele, Staffordshire ST5 5BG UK Tel. +44-1782-733412 Fax. +44-1782-734268 E-mail: p.andras(a)keele.ac.uk Web: www.scm.keele.ac.uk/staff/p_andras/

Aug. 29, 2014

New book: Growing Adaptive Machines

by Nicolas Bredeche

<We are sorry if you have received multiple copies of this email> Dear all, Some of you will be interested in our recent book, Growing Adaptive Machines: Combining Development and Learning in Artificial Neural Networks. The topic is the generation of low-level neural network architectures using bio-inspired models and simulations. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs and thus move closer towards the creation of brain-like systems. The book includes two broad reviews, summaries of research portfolios from established researchers, and new research contributions. We believe it will form a valuable reference for advanced students and practitioners. See below for the table of contents. http://www.springer.com/engineering/computational+intelligence+and+complexi… http://www.amazon.com/Growing-Adaptive-Machines-Computational-Intelligence/… Best regards, Taras Kowaliw, Nicolas Bredeche, René Doursat (Editors) =-=-=-=-= Table of Contents Artificial Neurogenesis: An Introduction and Selective Review. T. Kowaliw, N. Bredeche, S. Chevallier and R. Doursat A Brief Introduction to Probabilistic Machine Learning and Its Relation to Neuroscience. T. Trappenberg Evolving Culture Versus Local Minima. Y. Bengio Learning Sparse Features with an Auto-Associator. S. Rebecchi, H. Paugam-Moisy and M. Sebag HyperNEAT: The First Five Years. D. D’Ambrosio, J. Gauci and K. Stanley Using the Genetic Regulatory Evolving Artificial Networks (GReaNs) Platform for Signal Processing, Animat Control, and Artificial Multicellular Development. B. Wróbel and M. Joachimczak Constructing Complex Systems Via Activity-Driven Unsupervised Hebbian Self-Organization J. Bednar Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural Networks. J. Miller Artificial Evolution of Plastic Neural Networks: A Few Key Concepts. J.-B. Mouret and P. Tonelli

Aug. 29, 2014