- Comp-neuro - lists.cnsorg.org

Post doc/engineer position in direct brain control of muscle stimulators
by Dawn Taylor Jan. 9, 2015

Jan. 9, 2015

We currently have an opening for a postdoc or engineer for the vA-funded neuroprosthetics study described below. If interested, please send a CV to Dr. Dawn Taylor at dxt42(a)case.edu. The long-term goal of this project is to enable paralyzed individuals to use their brain signals to control their upper limb via implanted muscle stimulators. Most labs working on brain-controlled neuroprosthetics decode intended limb kinematics (e.g. velocity, joint angles, etc.) from the recorded brain signals. However, that approach still requires converting those kinematic commands into the appropriate stimulation patterns required to generate the desired limb motion. That conversion process has not been resolved for the upper limb due to the limb's complex dynamical nature and the fact that the limb is subject to unknown external forces during use. We bypass this obstacle by retraining the brain to control muscle stimulators directly. We have come up with some novel, but clinically feasible ways of mapping neural signals directly to muscle stimulators. Our methods can enable the user to have good control over both limb motion and stiffness. To demonstrate and refine our methods, we are training monkeys to control the movements of a realistic musculoskeletal model of a paralyzed limb activated via implanted muscle stimulators. The paralyzed limb simulator (developed by the lab of Robert Krisch) provides real-time visual feedback to the animal of the limb motion that would result from stimulating the paralyzed muscles based on the animal's neural signals decoded in real time. The use of this real-time paralyzed arm simulator allows us to test and refine our process of brain-controlled muscle stimulation in monkeys without actually having to paralyze any animals. -- Dawn M. Taylor, Ph.D. Assistant Professor of Neuroscience, Cleveland Clinic Researcher Scientist, Cleveland VA Medical Center, Functional Electrical Stimulation Center Assistant Professor of Biomedical Engineering & of Molecular Medicine, Case Western Reserve University The Cleveland Clinic Lerner Research Institute 9500 Euclid Ave. / NC30 Cleveland, OH 44195 email: dxt42(a)case.edu or taylord8(a)ccf.org Phone: (216) 636-0140 Fax: (216) 778-4259

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[jobs] Post Doc position in Humanoid Robotics Research
by Patricia Hazel Shaw [phs] Jan. 9, 2015

Jan. 9, 2015

We Apologise for multiple cross postings ========================================================================= Post-Doctoral Research Associate vacancy Three-year fixed-term Robotics Research Group, Department of Computer Science, Aberystwyth University, Wales, UK £32,277 – £37,384 Applicants are invited to join the Robotics Laboratory for a new project (entitled Developmental Algorithms for Robotics”). The project is focusing on developmental robotic learning based on an iCub humanoid robot. This is a three year EPSRC funded project running at Aberystwyth University, with the support of an International Scientific Advisory Board containing leading developmental psychologists. We wish to appoint two Post-Doctoral Research Associates who will develop and implement models based on the psychological literature, and conduct experiments on our iCub humanoid robot. One successful applicant will be responsible for understanding psychological literature and generating models based on the literature, whilst the other successful applicant will be responsible for software implementation and experimentation. This invitation is for the Scientific Modelling post, details below. For informal enquiries contact Patricia Shaw: phs(a)aber.ac.uk +44 (0)1970-622432 Ref: IMPACS.14.22  Closing Date: 5 February 2015  Interview Date: Week commencing 16 February 2015 For information and application forms please go to www.aber.ac.uk/en/hr/jobs/vacancies-external/ ========================================================================= The Project: “Developmental Algorithms for Robotics” The project aims to investigate a mechanism for robots, building on top of basic spatial sensorimotor competencies, that drives the autonomous development of new behaviours and self learning about novel events for which they have no prior experience. This will involve (1) expanding existing motor-babbling activity into object play behaviour, (2) formulating play behaviour as a viable mechanism for autonomous learning about unknown environments, (3) establishing an extended schema concept as a mechanism for integrating memory, experience generalization and action generation, (4) exploring action perception from the agent's experience, and (5) close monitoring of the work with psychological data and expert psychologists. The project will closely follow current knowledge on infant development and aim to reproduce behaviour as reported in the psychological literature. In particular the focus will be on three areas of core knowledge: (1) Object understanding, (2) Interaction with animate objects, and (3) Tool use. The work will be supported by collaboration with leading developmental psychologists on the project scientific advisory panel. This will produce models and mechanisms that can be implemented and tested experimentally against psychological benchmarks. The models will be validated with challenging demonstrators based on a single humanoid robotic platform (iCub). As a main outcome, the project will advance the understanding and application of intrinsic motivations in autonomous learning systems and robots. This overall goal will be achieved with the support of an International Scientific Advisory Board, consists of: Kevin O'Regan, Jacqueline Fagard, Merideth Gattis, David Whitebread, Giorgio Metta, QinetiQ and Lego. The Appointment (PDRA) – Scientific modeller Applications are invited for the post of Post-Doctoral Research Associate on this project at Aberystwyth University. The successful applicant will have a relevant background, experience of research (as demonstrated by PhD and/or publications), and proven abilities in mathematical/scientific modelling. The main tasks will involve analysing the psychological literature to develop models and experiments for testing on an iCub humanoid robot. This will be supported by another PDRA, and PhD student, who will have special responsibility for implementing the models whilst maintaining and supporting the robot. Other tasks include writing scientific documents and papers, documenting experiments and writing reports, supervising a PhD student, responsibility for day-to-day management of project needs, and collaborating with the scientific advisory panel and other researchers. Regular European travel will be involved. This post is available on a three-year fixed-term contract, funded by the EPSRC. Salary will be on the IA scale for Research Staff in the range: £32,277 - £37,384 (depending upon qualifications and experience). We expect to appoint for a start date in the first quarter of 2015.

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Postdoctoral and PhD positions, Personal Robotics lab, Imperial College London
by Demiris, Yiannis Jan. 8, 2015

Jan. 8, 2015

Dear colleagues, two research positions (either at the postdoctoral or the PhD level) in machine learning for user-modelling and human-robot Interaction are available at the Personal Robotics Laboratory of the Department of Electrical and Electronic Engineering at Imperial College London. Successful applicants will work under the supervision of Dr Yiannis Demiris (www.demiris.info) in the context of the new EU H2020 project PAL (Personal Assistants for healthy Lifestyle, 2015-2019), which aims to develop personalised robotic systems and avatars to assist diabetic (T1DM) children and their caregivers. The PAL positions are available from 1st of March 2015 until the end of the project at the end of Feb 2019 subject to renewal. Additional PhD positions through the A*STAR-Imperial partnership programme (project “Robot Learning by Demonstration for Heterogeneous Bimanual Collaboration Tasks”), the Chinese Scholarship Council programme, and the Imperial PhD scholarship scheme, among others, are also available. For all positions, applicants should have an excellent background in mathematics, machine learning and software engineering, and should be committed to applying their research to real systems interacting with people in challenging environments. The positions offer an excellent working environment in one of the world's top research universities, in one of the most exciting cities in the world. The Department of Electrical and Electrical Engineering at Imperial was ranked as the top EE department in the UK in the recent REF 2014, while its Personal Robotics Laboratory offers an energetic, friendly intellectual environment with state of the art facilities http://www.imperial.ac.uk/PersonalRobotics For further information and links to application material: http://www.imperial.ac.uk/personalrobotics/join_us With best wishes, Yiannis ---- Dr Yiannis Demiris, FIET, FBCS, FRSS Reader (Associate Professor) in Personal Robotics, Department of Electrical and Electronic Engineering, Rm 1014, Imperial College London, South Kensington Campus, Exhibition Road, London, SW7 2BT, UK Tel: +44-(0)2075946300, FaxL +44-(0)2075946274 Personal webpage: http://www.iis.ee.ic.ac.uk/yiannis Laboratory webpage: http://www.imperial.ac.uk/PersonalRobotics - Visiting Scholar, Harvard University School of Engineering and Applied Sciences (SEAS), Maxwell Dworkin Building MD-336, 33 Oxford Street, Cambridge, MA 02138, USA

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Research Topic "Metastable dynamics of neural ensembles"
by Emili Balaguer-Ballester Jan. 6, 2015

Jan. 6, 2015

Research Topic "Metastable dynamics of neural ensembles" http://journal.frontiersin.org/ResearchTopic/1955 A classical view on neural computation is that it can be characterized in terms of deterministic convergence to fixed-point-type attractor states (representing, e.g., memory patterns in Hopfield 1982) or limit-cycle-like sequential transitions among states (representing e.g. motor or syntactical sequences). Is this still a valid model of how brain dynamics implements cognition? The idea that neuro-computational dynamics is more or less deterministically driven by convergence to simple attractor states has recently been challenged both empirically and by computational work. In this Research Topic of Frontiers in Systems Neuroscience we welcome experimental studies and modelling contributions addressing the question of stable vs. transient neural population dynamics, and the potential role of noise and trial-to trial variability in neural computation. Major topics are, but are not restricted to: -Attracting and meta-stable dynamics of neural ensembles, both from empirical and computational modelling perspectives. -Coding by non-stationary and transient states in neural recordings -Spontaneous cortical activity dynamics -Metastable dynamics during cognitive processing -Oscillatory emergent patterns and propagation of waves in an excitable network -Trial-to-trial variability Please see more information in http://journal.frontiersin.org/ResearchTopic/1955. Deadline is on the 31st of March. BU is a Disability Two Ticks Employer and has signed up to the Mindful Employer charter. Information about the accessibility of University buildings can be found on the BU DisabledGo webpages This email is intended only for the person to whom it is addressed and may contain confidential information. If you have received this email in error, please notify the sender and delete this email, which must not be copied, distributed or disclosed to any other person. Any views or opinions presented are solely those of the author and do not necessarily represent those of Bournemouth University or its subsidiary companies. Nor can any contract be formed on behalf of the University or its subsidiary companies via email.

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Special Session on Transfer Learning - International Work Conference on Artificial Neural Networks, 10-12 June, 2015
by Jorge M. Santos Jan. 6, 2015

Jan. 6, 2015

Please consider to contribute to the Special Session on Transfer Learning International Work Conference on Artificial Neural Networks, 10-12 June, 2015 - http://iwann.ugr.es/2015 Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. The TL approach has gained significant interest in the Machine Learning (ML) community since it paves the way to devise intelligent learning models that can easily be tailored to many different domains of applicability. The following aspects have recently contributed to the emergence of TL: Generalization Theory: TL often produces algorithms with good generalization capability for different problems; Efficient TL algorithms: TL provides learning models that can be applied with far less computational effort than standard ML methods; Unlabeled data: TL can be advantageous since unlabeled data can have severe implications in some fields of research, such as in the biomedical field. Some examples of topics for this special session: Big Data with Deep Neural Networks; Generalization Bounds; Domain Adaptation or Covariate Shift; Algorithms for TL; New advancements in TL; Real-world applications. Deadline: 6 February 2015 Organizers Luís M. Silva, Dep. of Mathematics, University of Aveiro, Portugal - lmas(a)ua.pt Jorge M. Santos, Dep. of Mathematics, School of Engineering, Polytechnic of Porto, Portugal - jms(a)isep.ipp.pt

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CFP (Deadline, 1 Feb 2015): IEEE Computational Intelligence Magazine (CIM) Special Issue: “Computational Intelligence for Changing Environments"
by Dr Amir Hussain Jan. 6, 2015

Jan. 6, 2015

CALL FOR PAPERS (Deadline: 1 Feb 2015) - With advance apologies for any cross postings! IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (CIM) (http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10207) SPECIAL ISSUE (Nov 2015) ON "Computational Intelligence for Changing Environments" (http://www.cs.stir.ac.uk/~ahu/IEEE-CIM-CICE2015.pdf) AIMS AND SCOPE: Over the past decade or so, computational intelligence techniques have been highly successful for solving big data challenges in changing environments. In particular, there has been growing interest in so called biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors. Examples include the hierarchy of the brain neocortex and neural circuits, which have resulted in biologically-inspired features for encoding, deep neural networks for classification, and spiking neural networks for general modelling. To ensure that these models are generalizable to unseen data, it is common to assume that the training and test data are independently sampled from an identical distribution, known as the sample i.i.d. assumption. In dynamic and non- stationary environments, the distribution of data changes over time, resulting in the phenomenon of ‘concept drift’ (also known as population drift or concept shift), which is a generalization of covariance shift in statistics. Over the last five years, transfer learning and multitask learning have been used to tackle this problem. Fundamental analyses using probably approximately correct (PAC) and Rademacher complexity frameworks have explained why appropriate incorporation of context and concept drift can improve generalizability in changing environments. It is possible to use human-level processing power to tackle concept drift in changing environments. Concept drift is a real-world problem, usually associated with online and concept learning, where the relationships between input data and target variables dynamically change over time. Traditional learning schemes do not adequately address this issue, either because they are offline or because they avoid dynamic learning. However, BIL seems to possess properties that would be helpful for solving concept drift problems in changing environments. Intuitively, the human capacity to deal with concept drift is innate to cognitive processes, and the learning problems susceptible to concept drift seem to share some of the dynamic demands placed on plastic neural areas in the brain. Using improved biological models in neural networks can provide insight into cognitive computational phenomena. However, a main outstanding issue in using computational intelligence for changing environments and domain adaptation is how to build complex networks, or how networks should be connected to the features, samples, and distribution drifts. Manual design and building of these networks are beyond current human capabilities. Recently, computational intelligence methods has been used to address concept drift in changing environments, with promising results. A Hebbian learning model has been used to handle random, as well as correlated, concept drift. Neural networks have been used for concept drift detection, and the influence of latent variables on concept drift in a neural network has been studied. In another study, a timing-dependent synapse model has been applied to concept drift. These works mainly apply biologically-plausible computational models to concept drift problems. Although these results are still in their infancy, they open up new possibilities to achieve brain-like intelligence for solving concept drift problems in changing environments. Taking the current state of research in computational intelligence for changing environments into account, the objective of this special issue is to collate this research to help unify the concepts and terminology of computational intelligence in changing environments, and to survey state-of-the-art computational intelligence methodologies and the key techniques investigated to date. Therefore, this special issue invites submissions on the most recent developments in computational intelligence for changing environments, algorithms and architectures, theoretical foundations, and representations, & their application to real-world problems. We also welcome timely surveys & review papers. TOPICS OF INTEREST include (but are not limited to): • Computational intelligence methodologies and implementation for changing environments •Transfer learning, Multitask learning, Domain adaption •Incremental Learning architectures, Unsupervised and semi-supervised learning architectures •Incremental Knowledge augmentation, Representation learning and disentangling •Incremental Adaptive Neuro-fuzzy systems •Incremental and single-pass data mining •Incremental Neural Clustering & Regression •Incremental Adaptive decision systems •Incremental Feature selection and reduction •Incremental Constructive Learning •Novelty detection in Incremental learning SUBMISSION PROCESS The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via: https://easychair.org/conferences/?conf=ieeecimcdbil2015 IMPORTANT (REVISED) DATES (for November 2015 Issue) 1st Feb, 2015: Submission of Manuscripts 15th April, 2015: Notification of Review Results 15th May, 2015: Submission of Revised Manuscripts 15th June, 2015: Submission of Final Manuscripts GUEST EDITORS Professor Amir Hussain, University of Stirling, Stirling FK9 4LA, Scotland, UK Email: ahu(a)cs.stir.ac.uk http://cs.stir.ac.uk/~ahu/ Professor Dacheng Tao, University of Technology, Sydney, 235 Jones Street, Ultimo, NSW 2007, Australia Email: dacheng.tao(a)uts.edu.au Professor Jonathan Wu University of Windsor, 401 Sunset Avenue, Windsor, ON, Canada Email: jwu(a)uwindsor.ca Professor Dongbin Zhao Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China E-mail: dongbin.zhao(a)gmail.com ----- A PDF copy of the CFP is attached with this email for forwarding to interested colleagues. It is also available for download from: http://www.cs.stir.ac.uk/~ahu/IEEE-CIM-CICE2015.pdf For more information on the IEEE CIM, see: http://cis.ieee.org/ieee-computational-intelligence-magazine.html -- The University of Stirling has been ranked in the top 12 of UK universities for graduate employment*. 94% of our 2012 graduates were in work and/or further study within six months of graduation. *The Telegraph The University of Stirling is a charity registered in Scotland, number SC 011159.

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MBL Methods in Computational Neuroscience Course 2015: applications due March 5
by Mark Goldman Jan. 6, 2015

Jan. 6, 2015

Applications are open for the Methods in Computational Neuroscience course at the Marine Biology Laboratory in Woods Hole, MA. The course will run from July 29 to August 26, 2014, and the online application form can be found at: http://ws2.mbl.edu/studentapp/studentapp.asp?CourseID=MCN. The course application deadline is March 5. The course covers a range of topics in computational neuroscience including neuronal biophysics, neural coding & information processing, circuit dynamics, learning & memory, motor control, and cognitive processing & disease. In addition, numerous tutorials and problem sets will cover a broad range of computational and mathematical modeling methods. The course strongly emphasizes the collaboration between theory and experiment in solving neuroscience problems, and lectures will be given by a mixture of theorists and experimentalists. The final weeks of the course are primarily reserved for development and work on projects that students design in collaboration with the resident faculty. Further information can be found on the MCN website: http://www.mbl.edu/mcn/ 2015 Course Directors: Michale Fee, MIT Mark Goldman, UC Davis 2015 Confirmed Faculty: Larry Abbott, Columbia University Steve Baccus, Stanford University William Bialek, Princeton University Dmitri Chklovskii, HHMI Janelia Farm Peter Dayan, University College London Bard Ermentrout, University of Pittsburgh Adrienne Fairhall, University of Washington Ila Fiete, UT Austin Loren Frank, UCSF Michael Frank, Brown University Surya Ganguli, Stanford University John Huguenard, Stanford University David Kleinfeld, UC San Diego Nancy Kopell, Boston University John Lisman, Brandeis University Eve Marder, Brandeis University Bartlett Mel, University of Southern California Jonathan Pillow, Princeton University Terry Sejnowski, Salk Institute Michael Shadlen, Columbia University Josh Shaevitz, Princeton University Sara Solla, Northwestern University Haim Sompolinsky, Hebrew University David Tank, Princeton University Josh Tenenbaum, MIT Xiao-Jing Wang, NYU Daniel Wolpert, Cambridge University Ryohei Yasuda, Duke University

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CNS*2015 Call for Workshop Proposals - Deadline Approaching
by Farzan Nadim Jan. 5, 2015

Jan. 5, 2015

CNS 2015 Prague July 1823, 2015: Call for Workshops DEADLINE APPROACHING We are requesting proposals for workshops from the international community of computational neuroscientists. Proposals from all levels of faculty as well as advanced postdoctoral fellows are welcome. This is a great opportunity to organize a small meeting with just a few of the headaches of actually organizing it. Workshop proposal submission instructions for CNS 2015 The last two days (July 22-23) of the 24th annual CNS meeting will be devoted to workshops, in which computationally related neuroscience topics can be presented and discussed. Workshops can be anywhere between one half to two days in duration. Usually several speakers are invited to introduce a unifying theme, but ample time for discussion should also be planned. Submit workshop proposals to: workshops(a)cnsorg.org. The Past Meetings page gives access to archives of workshops held at previous CNS meetings. The proposal should be submitted as a Word or pdf file and MUST include the following sections: 1. Workshop Title 2. Organizers (list primary organizer first; include affiliations and emails) 3. One or two days (2 sessions per day) 4. Number of expected speakers 5. Brief Description (~150 words; if possible, say why this is significant or timely) 6. Speakers (mark expected or confirmed) Also please note the following rules which were approved by the OCNS Board on July 15, 2013: Each individual can be the organizer or co-organizer on only one workshop. The number of confirmed speakers is a criterion for accepting the proposal. Overlapping proposals may be asked to be combined. If the organizers do not wish to combine the proposals, only one of the proposals may be accepted. Workshops submitted before January 15, 2015 will be given priority in acceptance. Workshop proposals arriving after January 15, 2015 will be evaluated based on remaining space for additional workshops. No further workshop acceptances will be anticipated after May 15, 2015. Registration: Workshop registration will occur through the OCNS registration web site for CNS 2015. All workshop participants, including speakers must register. Each workshop is eligible to receive registration waivers for 2 speakers. Travel awards: a limited number of Travel Awards will be available for postdoctoral researchers to lead and be included as speakers. Exceptionally starting assistant professors may also be given consideration. These Travel Awards will be variable depending on distance traveled. Please indicate which speakers you would like to be considered for this mechanism but take into account that there will be less travel awards than workshops. Springer Computational Neuroscience Book Series: Some of the workshops may be published by the Springer Series in Computational Neuroscience. Workshop organizers interested in this mechanism should submit a book proposal to Simina Calin (Simina.calin(a)springer.com) and indicate in the workshop proposal their interest in publishing a book. Logistics: Rooms, AV equipment, snacks and beverages during breaks will be provided by OCNS to the workshop organizers.

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[publication and call for dialog] IEEE CIS Newsletter on Autonomous Mental Development, Fall 2014
by Pierre-Yves Oudeyer Jan. 5, 2015

Jan. 5, 2015

Dear colleagues, For this new year, I am happy to announce the release of the Fall 2014 issue of the IEEE CIS Newsletter on Autonomous Mental Development. This is the biannual newsletter of the computational developmental sciences and developmental robotics community, studying mechanisms of lifelong learning and development in machines and humans. It is available at: http://www.cse.msu.edu/amdtc/amdnl/AMDNL-V11-N2.pdf Featuring: === “Trained on everything" === Dialog Initiated by Katharina Rohlfing, Britta Wrede and Gerhard Sagerer, with responses from Giulio Sandino and David Vernon, Franck Ramus and Thérèse Collins, Maha Salem, Juyang Weng, Thomas Schultz, and Christina Bergmann: In the years to come, one very important challenge in developmental sciences is education. Taking an integrated and interdisciplinary approach requires to handle with dexterity concepts and methods from diverse scientific fields ranging from psychology, neuroscience, biology, robotics, computer science or mathematics. How can we grow a community of young researchers mastering the latest advances? How can we teach them to establish cross-disciplinary collaboration and impact? === "Will social robots need to be consciously aware?” === New dialog initiated by Janet Wiles A large research community is today working towards the objective of building robots capable of believable, relevant and useful social interaction with humans. We are far from understanding what “consciousness” is, but intuition tells us that it would be very difficult for an “unconscious” human to enter into a social interaction. So what about robots? At least can we identify levels of awareness (of the self, of others) which constitute a necessary basis on which to build social competence? Those of you interested in reacting to this dialog initiation are welcome to submit a response by March 30th, 2015. The length of each response must be between 600 and 800 words including references (contact pierre- yves.oudeyer(a)inria.fr) Let me remind you that previous issues of the newsletter are all open-access and available at: http://www.cse.msu.edu/amdtc/amdnl/ I wish you a stimulating reading! Best regards, Pierre-Yves Oudeyer, Editor of the IEEE CIS Newsletter on Autonomous Mental Development Research director, Inria Head of Flower project-team Inria and Ensta ParisTech, France http://www.pyoudeyer.com https://flowers.inria.fr

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Postdoc Opportunity at Stanford in the Modulation of Neural Circuitry for Cognitive and Emotional Control
by Wei Wu Jan. 5, 2015

Jan. 5, 2015

The Laboratory of Amit Etkin, MD PhD at Stanford Universityis currently accepting applications for a postdoctoral research fellowship focused on understanding and modulating the neural systems underlying cognitive and emotional control in both healthy individuals and patients with a range of psychiatric conditions. Special emphasis is put on use of causal circuit manipulation tools (eg TMS and concurrent TMS and fMRI) as well as a range of cognitive neuroscience paradigms at the behavioral, physiological and neural levels. The successful applicant will have a PhD in Cognitive Neuroscience, Neurophysiology, Psychology, Computer Science, Statistics or related fields. Experience with analysis of fMRI data and/or TMS is required. Additional experience with psychophysiology or programming is a plus. A US Citizenship is also required. Duties will also include manuscript preparation, presentation of findings at conferences, management of research assistants and contribution to the preparation of grants. Laboratory and Stanford resources include research-dedicated 3T and 7T MRI scanners, concurrent TMS/fMRI setups and concurrent TMS/EEG setups. Salary commensurate with experience. More information about our ongoing studies can be found at: http://etkinlab.stanford.edu. To apply, please send a curriculum vitae, a statement describing research interests and relevant background and three letters of recommendations, as well as relevant reprints/preprints of research articles to: Amit Etkin, MD, PhD Department of Psychiatry and Behavioral Sciences Stanford University amitetkin(a)stanford.edu

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