September 2020 - Comp-neuro - lists.cnsorg.org

ANDA 2020 - G-Node Advanced Neural Data Analysis Course, April 14-30, 2020, Jülich, Germany
by Thomas Wachtler Dec. 23, 2020

Dec. 23, 2020

ANDA 2020 - G-NODE ADVANCED NEURAL DATA ANALYSIS COURSE April 14 - 30, 2020 Haus Overbach, Jülich-Barmen, Germany Techniques to record neuronal data from populations of neurons are rapidly improving. Simultaneous recordings from hundreds of channels are possible while animals perform complex behavioral tasks. The analysis of such massive and complex data becomes increasingly challenging. This advanced course aims at providing deeper training in state-of-the-art analysis approaches in systems neuroscience. The course is addressed to excellent master and PhD students and young researchers who are interested in learning advanced techniques in data analytics and in getting hands-on experience in the analysis of electrophysiological data. Internationally renowned researchers will give lectures on statistical data analysis and data mining methods with accompanying exercises. Students will define and perform their own analyses on provided data to solve a challenge. Participants are required to have a strong interest in data analysis, a background in a mathematical or related field, knowledge of algebra, matrix operations, and statistics, and need to have solid programming experience (preferably in Python). FACULTY · Moshe Abeles, Hebrew Univ. Jerusalem, Israel · Michael Denker, Jülich Research Center, Germany · Udo Ernst, Univ. Bremen, Germany · Sonja Grün, Jülich Research Center, Germany · Adam Kohn, Albert Einstein College of Medicine, New York, USA · Jakob Macke, TU Munich, Germany · Luca Mazzucato, Univ. of Oregon, Eugene, USA · Martin Nawrot, Univ. of Cologne, Germany · Yifat Prut, Hebrew Univ. Jerusalem, Israel · Hansjörg Scherberger, German Primate Center, Göttingen, Germany · Thomas Wachtler, LMU Munich, Germany TOPICS COVERED Single neuron properties and statistics · Stochastic processes · Surrogate methods · Detection of spatio-temporal patterns · Unitary Events · Statistical analysis of massively parallel spike data · Higher-order correlation analyses · Elephant toolbox · Spike-LFP relationship · Population coding · State space analysis · Machine learning · Data mining · Research data management and reproducibility REQUIREMENTS Applicants should be familiar with linear algebra, probability, differential and integral calculus and experienced using Python or Matlab. Preparatory reading material will be provided. Students should bring their own laptops and should be able to install software on their system. Students that do not have a suitable laptop should indicate this immediately after acceptance to the course. We will be able to provide a small number of laptops for the time of the course. COURSE FEE A course fee of 1000 Euros will be charged to cover costs for accommodation and meals. Limited financial support may be available for students that otherwise would not be able to attend, which is to be indicated in the application. HOUSING Accommodation in 2-bed rooms for students will be provided at the course site. HOW TO APPLY The application should include · a letter of motivation (max 1 page) · curriculum vitae (please indicate the relevant courses you have taken) · description of programming experience · a letter of recommendation. Please send all documents as a single PDF file to <advanced-course(a)g-node.org>. APPLICATION DEADLINE Applications must be received by NOVEMBER 15, 2019. Early application is encouraged. For further information see http://www.g-node.org/anda

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SMILES virtual workshop @ICDL 2020
by Xavier Hinaut Oct. 27, 2020

Oct. 27, 2020

The SMILES (Sensorimotor Interaction, Language and Embodiment of Symbols) Workshop will take place virtually at the ICDL 2020 (International Conference on Developmental Learning). * Call for abstracts and papers: - Deadline: 25th of September 2020 - papers abstracts: 2 pages - long papers: 4-6 pages - Submissions: smiles.conf(a)gmail.com <mailto:smiles.conf@gmail.com> - Papers format: same as ICDL conference https://cis.ieee.org/publications/t-cognitive-and-developmental-systems/tcd… <https://cis.ieee.org/publications/t-cognitive-and-developmental-systems/tcd…> - Workshop dates (coming soon): the days before or after the ICDL conference (26-30 October 2020) * Workshop Short Description On the one hand, models of sensorimotor interaction are embodied in the environment and in the interaction with other agents. On the other hand, recent Deep Learning development of Natural Language Processing (NLP) models allow to capture increasing language complexity (e.g. compositional representations, word embedding, long term dependencies). However, those NLP models are disembodied in the sense that they are learned from static datasets of text or speech. How can we bridge the gap from low-level sensorimotor interaction to high-level compositional symbolic communication? The SMILES workshop will address this issue through -an interdisciplinary approach involving researchers from (but not limited to): - Sensori-motor learning, - Emergent communication in multi-agent systems, - Chunking of perceptuo-motor gestures (gestures in a general sense: motor, vocal, ...), - Sensori-motor learning, - Symbol grounding and symbol emergence, - Compositional representations for communication and action sequence, - Hierarchical representations of temporal information, - Language processing and acquisition in brains and machines, - Models of animal communication, - Understanding composition and temporal processing in neural network models, and - Enaction, active perception, perception-action loop. * More info - contact: smiles.conf(a)gmail.com <mailto:smiles.conf@gmail.com> - organizers: Xavier Hinaut, Clement Moulin-Frier, Silvia Pagliarini, Joni Zhong, Loo CHU KIONG, Michael Spranger, Tadahiro Taniguchi - invited speakers (coming soon) - workshop website (comming soon): https://sites.google.com/view/smiles-workshop/ <https://sites.google.com/view/smiles-workshop/> - ICDL conference website: https://cdstc.gitlab.io/icdl-2020/ <https://cdstc.gitlab.io/icdl-2020/> * Workshop Long Description Recently Deep Learning networks have broken many benchmarks in Natural Language Processing (NLP), e.g. [1]. Such breakthroughs are realized by a few mechanisms (e.g. continuous representations like word embedding, attention mechanism, ...). However, no equivalent finding happened towards the understanding of which mechanisms enable the brain to perform similar functions. Deep Learning does not reproduce learning mechanisms nor the dynamics of the brain. The brain needs to parse incoming stimuli and learn from them incrementally, it cannot unfold time like deep learning algorithms such as Back-propagation through time (BPTT). Thus, we still lack the key neuronal mechanisms needed to properly model the (hierarchies of) functions in language perception and production. Other models of language processing reproducing the behavior of brain dynamics (Event-Related-Potentials (ERPs) [2] or functional Magnetic Resonance Imaging (fMRI) [3]) have been developed. However, such model softens lack explanatory power demonstrating the causes of such observed dynamics: i.e. what is computed and why is it computed – for which purpose? We need more biologically plausible learning mechanisms while producing causal explanations of the experimental data modelled. There is converging evidence that language production and comprehension are not separated processes in a modular mind, they are rather interwoven, and this interweaving is what enables people to predict themselves and each other [4].Interweaving of action and perception is important because it allows a learning agent (or a baby) to learn from its own actions: for instance, by learning the perceptual consequences(e.g. the heard sounds) of its own actions (e.g. vocal productions) during babbling [5]. Thus, the agent learns in a self-supervised way instead of relying only on supervised learning, which in contrast, imply non-biological teacher signals cleverly designed by the modeler. Explicit neuronal models explaining which are the mechanisms shaping these perceptuo-motor units through development are missing. The existence of sensorimotor (i.e. mirror) neurons at abstract representation levels (often called action-perception circuits [6]), jointly with the perceptuo-motor shaping of sensorimotor gestures [5], suggest the existence of similar action-perception mechanisms implemented at different levels of hierarchy. How could we go towards such hierarchical architectures based on action-perception mechanisms? Importantly, a language processing model needs a way to acquire the semantics of the (symbolic) perceptuo-motor gestures and of the more abstract representations, otherwise it would consider only morphosyntactic and prosodic featuresof language. These symbolic gestures, i.e. signs, need to be grounded to the mental concept, i.e. signified, they are representing. Several theories and robotic experiments give examples of how symbols could be grounded or how symbols could emerge [7]. However, current neurocomputational models aiming to explain brain processes are not grounded. Robotics have an important role here for the grounding of semantics by experiencing the world through interactions with the physical world and with humans. We need mechanisms that start from raw sensory perception and raw motor commands in order to let emerge plausible representations through development, instead of arbitrary representations. Finally, computational models of emergent communication in agent populations are currently gaining interest in the machine learning community [8], [9]. These contributions show how a communication system can emerge to solve cooperative tasks in sequential environments. However, they are still relatively disconnected from the earlier theoretical and computational literature aiming at understanding how language might have emerged from a prelinguistic substance [10], [11]. We need to conceive communication as the emergent result of a collective behavior optimization process and to ground the resulting computational models into the theoretical literature from language evolution research. The SMILES workshop will aim at discussing each of the questions mentioned above, together with original approaches on how to integrate them. [1] J. Devlin et al., 2018. [2] H. Brouwer and J. C. J. Hoeks, 2013. [3] M. Garagnani et al. 2008. [4] M. J. Pickering and S. Garrod, 2013. [5] J.-L. Schwartz et al., 2012. [6] F. Pulvermüller and L. Fadiga, 2010. [7] T. Taniguchi et al., 2016. [8] S. Sukhbaatar et al.,2016. [9] I. Mordatch and P. Abbeel, 2017. [10] M. Tomasello et al., 2005. [11] P.-Y. Oudeyer and L. Smith, 2015. Xavier Hinaut Inria Researcher (CR) Mnemosyne team, Inria LaBRI, Université de Bordeaux Institut des Maladies Neurodégénératives +33 5 33 51 48 01 www.xavierhinaut.com

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Lecture video about brain mechanisms that cause mental disorders
by Grossberg, Stephen Oct. 17, 2020

Oct. 17, 2020

Dear Colleagues, I gave a pre-recorded keynote lecture on Zoom to the World Congress on Computational Intelligence on July 22 about how particular brain dysfunctions can cause symptoms of mental disorders like Alzheimer's disease, autism, amnesia, and neglect: http://sites.bu.edu/steveg/files/2020/07/Grossberg-WCCI-keynote-Glasgow-vid… The lecture can also be found on my web page sites.bu.edu/steveg along with a pdf of its powerpoint slides. Best, Steve Grossberg Stephen Grossberg http://en.wikipedia.org/wiki/Stephen_Grossberg http://scholar.google.com/citations?user=3BIV70wAAAAJ&hl=en https://youtu.be/9n5AnvFur7I https://www.youtube.com/watch?v=_hBye6JQCh4 Wang Professor of Cognitive and Neural Systems Director, Center for Adaptive Systems Professor Emeritus of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering Boston University sites.bu.edu/steveg steve(a)bu.edu

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NEURAL COMPUTATION - February 1, 2020
by Terry Sejnowski Oct. 1, 2020

Oct. 1, 2020

Neural Computation - Volume 32, Number 2 - February 1, 2020 Available online for download now: http://www.mitpressjournals.org/toc/neco/32/2 http://cognet.mit.edu/content/neural-computation ----- Article Face Representations via Tensorfaces of Various Complexities Sidney Lehky, Anh-Huy Phan, Andrzej Cichocki, and Keiji Tanaka Letters Transition Scale-spaces: A Computational Theory for the Discretized Entorhinal Cortex Nicolai Waniek From Synaptic Interactions to Collective Dynamics in Random Neuronal Networks Models: Critical Role of Eigenvectors and Transient Behavior Ewa Gudowska-Nowak, Maciej A Nowak, Dante Chialvo, Jeremi K Ochab, and Wojciech Mateusz Tarnowskic Synaptic Scaling Improves the Stability of Neural Mass Models Capable of Simulating Brain Plasticity Jure Demsar, Rob Forsyth Scaled Coupled Norms and Coupled Higher Order Tensor Completion Kishan wimalawarne, Makoto Yamada, and Hiroshi Mamitsuka Improving Generalization via Attribute Selection on Out-of-the-box Data Xiaofeng Xu, Ivor W. Tsang, and Chuancai Liu ------------ ON-LINE -- http://www.mitpressjournals.org/neuralcomp MIT Press Journals, One Rogers Street, Cambridge, MA 02142-1209 Tel: (617) 253-2889 FAX: (617) 577-1545 journals-cs(a)mit.edu ------------

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POSTDOC POSITIONS IN COMPUTATIONAL NEUROSCIENCE IN PARIS AND BASEL
by Rava A. da Silveira Sept. 30, 2020

Sept. 30, 2020

*Several postdoctoral openings in the lab of Rava Azeredo da Silveira **(Paris & Basel)* The lab of Rava Azeredo da Silveira invites applications for *Postdoctoral Researcher* positions at ENS, Paris, and IOB, an associated institute of the University of Basel. Research questions will be chosen from a broad range of topics in theoretical/computational neuroscience and cognitive science (see the description of the lab’s activity, below). One of the postdoc positions to be filled in Basel will be part of a collaborative framework with Michael Woodford (Columbia University) and will involve projects relating the study of decision making to models of perception and memory. Candidates with backgrounds in mathematics, statistics, artificial intelligence, physics, computer science, engineering, biology, and psychology are welcome. Experience with data analysis and proficiency with numerical methods, in addition to familiarity with neuroscience topics and mathematical and statistical methods, are desirable. Equally desirable are a spirit of intellectual adventure, eagerness, and drive. The positions will come with highly competitive work conditions and salaries. *Application deadline:* For full consideration, please apply by 30 October 2020. *How to apply:* Please send the following information *in one single PDF, to * *silveira(a)iob.ch* <silveira(a)iob.ch>*:* 1. letter of motivation; 2. statement of research interests, limited to two pages; 3. curriculum vitæ including a list of publications; 4. any relevant publications that you wish to showcase. In addition, please arrange for three letters of recommendations to be sent to the same email address. In all email correspondence, please include the mention “APPLICATION-POSTDOC” in the subject header, otherwise the application will *not* be considered. *** *ENS,* together with a number of neighboring institutions (College de France, Institut Curie, ESPCI, Sorbonne Université, and Institut Pasteur), offers a rich scientific and intellectual environment, with a strong representation in computational neuroscience and related fields. *IOB* is a research institute combining basic and clinical research. Its mission is to drive innovations in understanding vision and its diseases and develop new therapies for vision loss. IOB is an equal-opportunity employer with family-friendly work policies. *The Silveira** Lab* focuses on a range of topics, which, however, are tied together through a central question: How does the brain represent and manipulate information? Among the more concrete approaches to this question, the lab analyses and models neural activity in circuits that can be identified, recorded from, and perturbed experimentally, such as visual neural circuits in the retina and the cortex. Establishing links between physiological specificity and the structure of neural activity yields an understanding of circuits as building blocks of cerebral information processing. On a more abstract level, the lab investigates the representation of information in populations of neurons, from a statistical and algorithmic—rather than mechanistic—point of view, through theories of coding and data analyses. These studies aim at understanding the statistical nature of high-dimensional neural activity in different conditions, and how this serves to encode and process information from the sensory world. In the context of cognitive studies, the lab investigates mental processes such as inference, learning, and decision-making, through both theoretical developments and behavioral experiments. A particular focus is the study of neural constraints and limitations and, further, their impact on mental processes. Neural limitations impinge on the structure and variability of mental representations, which in turn inform the cognitive algorithms that produce behavior. The lab explores the nature of neural limitations, mental representations, and cognitive algorithms, and their interrelations.

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COSYNE 2021: Meeting announcement and call for abstracts
by Tomas Hromadka Sept. 29, 2020

Sept. 29, 2020

==================================================== Computational and Systems Neuroscience 2021 (Cosyne) MAIN MEETING ONLINE 27 February - 01 March 2021 www.cosyne.org ==================================================== ---------------------------------------------------- MEETING ANNOUNCEMENT ---------------------------------------------------- The annual Cosyne meeting provides an inclusive forum for the exchange of empirical and theoretical approaches to problems in systems neuroscience, in order to understand how neural systems function. Cosyne 2021 will take place online, February 27 - March 1, 2021. The goal of this year’s meeting is to promote early stage investigators. Therefore, there are no invited talks from established researchers. The format will include featured contributed talks and an interactive poster-like presentations (short recorded presentation + live interactions). More details to follow. All abstract submissions will be reviewed as in previous years, but reviewing will be fully double blind. The deadline for Abstract submission will be November 12, 2020. Cosyne topics include but are not limited to: neural basis of behavior, sensory and motor systems, circuitry, learning, neural coding, natural scene statistics, dendritic computation, neural basis of persistent activity, nonlinear receptive field mapping, representations of time and sequence, reward systems, decision-making, synaptic plasticity, map formation and plasticity, population coding, attention, and computation with spiking networks. IMPORTANT DATES Abstract submission opens: 11 October 2020 Abstract submission deadline: 12 November 2020 When preparing an abstract, authors should be aware that not all abstracts can be accepted for the meeting. Abstracts will be selected based on the clarity with which they convey the substance, significance, and originality of the work to be presented. ORGANIZING COMMITTEE General & Program Chairs: Anne-Marie Oswald (U Pittsburgh) and Srdjan Ostojic (Ecole Normale Superieure Paris) Diversity Chairs: Eva Dyer (Georgia Tech, Emory) and Eric Shea-Brown (U Washington) Publicity Chair: Adam Calhoun (Princeton) Development Chair: Michael Long (NYU) EXECUTIVE COMMITTEE Stephanie Palmer (U Chicago) Zachary Mainen (Champalimaud) Alexandre Pouget (U Geneva) Anthony Zador (CSHL) CONTACT meeting [at] cosyne.org COSYNE MAILING LISTS Please consider adding yourself to Cosyne mailing lists (groups) to receive email updates with various Cosyne-related information and join in helpful discussions. See Cosyne.org -> Mailing lists for details.

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(on behalf of Loren Frank) faculty position @ UCSF
by Peter Dayan Sept. 29, 2020

Sept. 29, 2020

The Department of Physiology at UCSF is seeking a visionary neuroscientist to join our vibrant and collaborative discovery research community. This open-rank, tenure-track faculty position is part of a multi-year UCSF institutional initiative that began in 2018 to foster equity and inclusion in biomedical research and to promote the success of our diverse student population. Since launching this effort, UCSF has tripled the number of underrepresented faculty in our basic sciences departments and this critical mass will continue to grow. Candidates are expected to hold a Ph.D. and/or M.D., to have demonstrated significant research accomplishment and potential, and to be committed to the mentorship of students, with a particular emphasis on candidates with demonstrated success mentoring students from groups that are historically underrepresented in biomedical science. We encourage outstanding candidates who are early in their postdoctoral research training to apply, even if their main findings are not yet published. The successful candidate will be expected to establish a dynamic and interactive research program focused in the field of integrative neuroscience, which could include molecular, genetic, cellular, systems, behavioral, computational, and engineering approaches. The candidate would become a member of the Kavli Institute for Fundamental Neuroscience, the Center for Integrative Neuroscience, and the Weill Institute for Neuroscience at UCSF. The candidate would be expected to join the UCSF Neuroscience Graduate Program and, if so desired, one or more of our other top-ranked interdisciplinary graduate programs (see https://graduate.ucsf.edu/list-programs) The candidate, like all faculty at UCSF, will also be expected to contribute to graduate teaching, to be a leader in inclusive mentoring practices, and to participate in activities that promote equity and inclusion at our institution and in the broader scientific community. Service and teaching expectations are comparable to those of any other UCSF tenure-track faculty position. Faculty appointment will be made at a professorial rank commensurate with current academic standing. UCSF offers competitive salaries, research set-up funds, and recruitment allowances, and has numerous opportunities for faculty development and further training in mentorship. Applications must be submitted online and include the following documents in a single PDF: (1) cover letter briefly describing how the candidate fits the position; (2) curriculum vitae; (3) 1-page summary of research accomplishments demonstrating the candidate’s scientific leadership through their contribution to their project(s) and describing the potential impact of their work on the field of neuroscience; (4) 2-page description of future research plans that includes both high risk / high reward projects and an NIH-fundable program; and 5) a statement of personal experiences with, and contributions to, issues related to social and racial justice, equity, diversity, and inclusion, with particular attention to demonstrated success with formal and informal mentoring of individuals from historically underrepresented groups. We encourage candidates to describe specific mentoring strategies to support the success of all trainees in their research group. Copies of major publications that demonstrate the candidate’s ability to carry out creative and impactful work should be included. Applicants should request three to five letters of recommendation describing how the candidate meets the criteria in this job description. We encourage recommenders to provide explicit examples to illustrate their statements. We expect candidates to provide a letter from their postdoctoral and graduate advisors but will consider candidates who can provide justifications when these will be lacking. Review will begin on December 1, 2020 and continue until the position is filled. Please direct inquiries about the position and suggestions of potential candidates to Dr. Loren Frank (loren(a)phy.ucsf.edu) As an institution that values diversity, UC San Francisco especially encourages application from historically excluded groups, including but not limited to racially underrepresented individuals, women, and individuals with a commitment to mentoring historically excluded groups in the sciences. UC San Francisco seeks candidates whose experience, teaching, research, or community service that has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status. Please apply online at https://aprecruit.ucsf.edu/JPF03083.

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postdoc/PhD students needed
by Alexey Kuznetsov Sept. 28, 2020

Sept. 28, 2020

*POSTDOCTORAL POSITION* *Indianapolis, Indiana, United States* A postdoctoral position and/or PhD student positions are available for a cutting-edge project on computational modeling of habit formation. The project takes place in the excellent collaborative environment of the Indiana Alcohol Research Center and studies the influence of alcohol on habits. The post-doc will develop a model of the cortico-striatal brain circuitry affected by alcohol. The post-doc will work with Prof. Kuznetsov (Mathematics Dept. IUPUI) and collaborate with experimentalists Prof. Atwood (IUSM) and Prof. Lapish (Psycology Dept. IUPUI). The position is available beginning December 2020 for one year. The continuation is contingent upon the availability of funds. *Qualifications*: Applicants should have a Ph.D. in Mathematics, Physics, or a related field, a background in Dynamical Systems, programming experience and working knowledge in Computational Neuroscience. Experience in modeling the basal ganglia is desirable. *How to Apply:* 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. IUPUI is an Equal Opportunity/Affirmative Action Institution. Individuals who require a reasonable accommodation in order to participate in the application process must notify Cindy Ritchie at 402 N. Blackford Street, LD 270, Indianapolis, Indiana 46202 or call (317) 274-6920, a reasonable time in advance. IUPUI strongly encourages applications from women and underrepresented minorities. Additional information about IUPUI and the Department is available at www.iupui.edu and www.math.iupui.edu. -- Alexey Kuznetsov Associate Professor Dept. of Mathematical Sciences, IUPUI Science Building, LD 270, 402 N. Blackford St., Indianapolis, IN

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Postdoctoral position at BU (Nancy Kopell) and MIT (Emery Brown)
by McCarthy, Michelle M Sept. 28, 2020

Sept. 28, 2020

Dear Comp-Neuro Community, The Kopell lab at Boston University has an open position (2 years with the possibility of renewal) for a postdoctoral associate to work on computational models concerning mechanisms of anesthesia, with experimental work to be done in the labs of Emery Brown, Earl Miller and others. The models are physiologically detailed (Hodgkin-Huxley based), linking anesthetic-mediated changes in physiology to changes in brain rhythms and their coordination both locally and across the brain; the models further link the brain rhythms to changes in consciousness. The work is relevant to sleep and brain disorders. Programming proficiency and modeling experience is necessary and some neuroscience training is desirable. Please send inquiries and applications to Xiaoshi Shi (xiaoshi(a)bu.edu<mailto:xiaoshi@bu.edu>) and nk(a)bu.edu<mailto:nk@bu.edu>. Nancy Kopell Department of Mathematics and Statistics Boston University, Boston MA 02215

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Two Cardiac Simulation/Computational Neuroscience postdoc computer modeling positions are available for development of a novel ICNS (intrinsic cardiac nervous system) simulation project.
by Salvador Dura Sept. 28, 2020

Sept. 28, 2020

Our new NIH SPARC Initiative ICNS project aims to build and establish a new model of RAGP (right atrial ganglionic plexus) The work is being done as a collaboration between SUNY Downstate (Brooklyn, NY), and Jefferson University (Philadelphia, CT) under the auspices of a 2-year NIH SPARC Initiative grant. The two new postdocs will work closely together - one focusing primarily on cell-level modeling and the other primarily on network but with both expected to work in both areas. Candidates should have a strong background in computer programming, with demonstrated proficiency in Python. Strong interest, though not necessarily research experie nce, in neuroscience is a must. Some experience with NEURON and NetPyNE is desirable. Job Types: Full-time or Contract contact: bill.lytton(a)downstate.edu<mailto:bill.lytton@downstate.edu> -- William W. Lytton, MD SUNY Distinguished Professor Downstate Medical Center, Brooklyn, NY bill.lytton(a)downstate.edu; http://neurosimlab.com ________________________________________________________________

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