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an emerging neural theory of grid and place cell development: and navigation: modules, spiking dynamics, cholinergic inactivation, oscillations, and attention
by Stephen Grossberg 07 Jun '14

07 Jun '14

The following articles summarize an emerging neural theory of how grid and place cells develop neurophysiological properties that support navigational behaviors, including properties of modular organization, spiking dynamics, effects of cholinergic inactivation, oscillations, and attention. ******************************************************************************************************************************************************************************** Grossberg, S., and Pilly, P. K. (2014). Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention, and oscillations. Philosophical Transactions of the Royal Society B., 369, 20120524, http://rstb.royalsocietypublishing.org/content/369/1635/20120524.full.pdf+h… Abstract. A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps. The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning, and remembering the most frequent and energetic co-occurrences of their inputs. The model’s parsimonious properties include: Similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same self-organizing map mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to hippocampus (“neural relativity”). The model clarifies how top-down hippocampus-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta, and gamma oscillations. The article also compares the three main types of grid cell models in light of recent data. **************************************************************************************************************** Pilly, P.K., and Grossberg, S. (2013). Spiking neurons in a hierarchical self-organizing map model can learn to develop spatial and temporal properties of entorhinal grid cells and hippocampal place cells. PLOS ONE, http://dx.plos.org/10.1371/journal.pone.0060599 Abstract. Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation. ******************************************************************************************************************************************************************************** Pilly, P.K., and Grossberg, S. (2014) How does the modular organization of entorhinal grid cells develop? Frontiers in Human Neuroscience, doi:10.3389/fnhum.2014.0037, http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00337/full Abstract. The entorhinal-hippocampal system plays a crucial role in spatial cognition and navigation. Since the discovery of grid cells in layer II of medial entorhinal cortex (MEC), several types of models have been proposed to explain their development and operation; namely, continuous attractor network models, oscillatory interference models, and self-organizing map (SOM) models. Recent experiments revealing the in vivo intracellular signatures of grid cells (Domnisoru et al., 2013; Schmidt-Heiber and Hausser, 2013), the primarily inhibitory recurrent connectivity of grid cells (Couey et al., 2013; Pastoll et al., 2013), and the topographic organization of grid cells within anatomically overlapping modules of multiple spatial scales along the dorsoventral axis of MEC (Stensola et al., 2012) provide strong constraints and challenges to existing grid cell models. This article provides a computational explanation for how MEC cells can emerge through learning with grid cell properties in modular structures. Within this SOM model, grid cells with different rates of temporal integration learn modular properties with different spatial scales. Model grid cells learn in response to inputs from multiple scales of directionally-selective stripe cells (Krupic et al., 2012; Mhatre et al., 2012) that perform path integration of the linear velocities that are experienced during navigation. Slower rates of grid cell temporal integration support learned associations with stripe cells of larger scales. The explanatory and predictive capabilities of the three types of grid cell models are comparatively analyzed in light of recent data to illustrate how the SOM model overcomes problems that other types of models have not yet handled. ********************************************************************************************************************************************************** Pilly, P.K., and Grossberg, S. (2013). How reduction of theta rhythm by medial septum inactivation may covary with disruption of entorhinal grid cell responses due to reduced cholinergic transmission. Frontiers in Neural Circuits, doi: 10.3389/fncir.2013.00173, http://www.frontiersin.org/Journal/10.3389/fncir.2013.00173/full?utm_source… Abstract. Oscillations in the coordinated firing of brain neurons have been proposed to play important roles in perception, cognition, attention, learning, navigation, and sensory-motor control. The network theta rhythm has been associated with properties of spatial navigation, as has the firing of entorhinal grid cells and hippocampal place cells. Two recent studies reduced the theta rhythm by inactivating the medial septum (MS) and demonstrated a correlated reduction in the characteristic hexagonal spatial firing patterns of grid cells. These results, along with properties of intrinsic membrane potential oscillations (MPOs) in slice preparations of medial entorhinal cortex (MEC), have been interpreted to support oscillatory interference models of grid cell firing. The current article shows that an alternative self-organizing map (SOM) model of grid cells can explain these data about intrinsic and network oscillations without invoking oscillatory interference. In particular, the adverse effects of MS inactivation on grid cells can be understood in terms of how the concomitant reduction in cholinergic inputs may increase the conductances of leak potassium (K+) and slow and medium after-hyperpolarization (sAHP and mAHP) channels. This alternative model can also explain data that are problematic for oscillatory interference models, including how knockout of the HCN1 gene in mice, which flattens the dorsoventral gradient in MPO frequency and resonance frequency, does not affect the development of the grid cell dorsoventral gradient of spatial scales, and how hexagonal grid firing fields in bats can occur even in the absence of theta band modulation. These results demonstrate how models of grid cell self-organization can provide new insights into the relationship between brain learning and oscillatory dynamics. ********************************************************************************************************************************************************************************* Stephen Grossberg Wang Professor of Cognitive and Neural Systems Professor of Mathematics, Psychology, and Biomedical Engineering Director, Center for Adaptive Systems http://cns.bu.edu/~steve steve(a)bu.edu

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ECML Workshop - “Neural Connectomics: From Imaging to Connectivity”
by Demian Battaglia 06 Jun '14

06 Jun '14

ECML Workshop - “Neural Connectomics: From Imaging to Connectivity” September 15, 2014 - Nancy, France Paper submission deadline: June 20, 2014 Description: Systematic extraction of connectivity information is gaining growing importance for the understanding of the general functioning of the brain and its learning capabilities, as well as for designing biomarkers for diagnosis, prediction and prevention in neuropathologies. At the neural level, recovering the exact wiring of the brain (connectome) including nearly 100 billion neurons, having on average 7000 synaptic connections to other neurons, is a daunting task. The goal of this workshop is to bring together researchers in machine learning and neuroscience to discuss progress and remaining challenges in this exciting and rapidly evolving field. We aim to attract machine learning and computer vision specialists interested in learning about a new problem, as well as computational neuroscientists who may be interested in modeling connectivity data. We will discuss also the results of the First ChaLearn Neural Connectomics Challenge, who attracted over 100 participants, many of them advancing considerably the state-of-the-art. Topics of interest to the workshop include, but are not limited to: • building connectomes from EM data • building connectomes from fMRI data • building connectomes from neurophysiology data • bridging neuroanatomy and neurophysiology • connectomics and learning • neuroimaging technology advances • network reconstruction algorithms • causality in time series • feature selection vs. causal discovery • generative vs. discriminative modeling • sharing data • sharing code • organizing new challenges • establishing ground truth, benchmarking • quantitative metrics of evaluation • theoretical understanding Important dates: o Paper submission: June 20, 2014 o Notification of acceptance: July 05, 2014 o Camera-ready: July 25, 2014 o ECML Workshop: September 15, 2014 Important - Submission Guidelines: We encourage contributions in any of these areas. We welcome 2-page short-form submissions and 6-page long-form submissions. Submissions should be formatted using JMLR Workshop and Proceedings format, style files for which are available at: http://www.tex.ac.uk/tex-archive/help/Catalogue/entries/jmlr.html. We also encourage submissions of previously-published material that is closely related to the workshop topic (for presentation only). Everybody can attend the workshop even if he does not participate in the challenge (http://connectomics.chalearn.org/) Challenge participants are encouraged to contribute a paper on their results and also submit papers for presentation on the topics of the workshop. The papers have to be submitted via Easy Chair: https://www.easychair.org/conferences/?conf=ncw2014

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REMINDER: Two PhD Positions and one Postdoc Position in Machine Learning / Sensory Data Processing
by Jörg Lücke 06 Jun '14

06 Jun '14

Dear researchers, There is a bit more than one week left until the deadline for the positions. If you have already applied, please see the end of this e-mail for more details on the required application documents. The given information is in response to some questions we got. Best wishes, Jörg Lücke On 28.04.2014 11:54, Jörg Lücke wrote: > The Machine Learning research group at the University of Oldenburg is > seeking to fill > > Two PhD Research Positions and one (postdoctoral) Research Associate > Position > > All positions are part of the Machine Learning group which develops > learning and inference technology for sensory data. We pursue basic > research, develop new technology, and apply our approaches to > different tasks. Our research hereby combines modern probabilistic > data descriptions, modern computer technology and insights from the > neurosciences. We contribute to the improvement of current methods for > computer hearing, pattern recognition and computer vision as well as > to the understanding of biological and artificial intelligence. > Research will be conducted in close collaboration with leading > international and national research labs. Our research field can be > considered as part of the Data Sciences, Computational Sciences, or > Big Data approaches (to name some terms recently used in the media). > > Salary levels of the positions are based on the TV-L scale of the > German public sector (Öffentlicher Dienst). After the deduction of > health insurance, pension tax and other taxes, the salary for the PhD > positions amounts to approximately 1600 EUR per month, and to about > 2000 EUR per month for the (postdoctoral) Research Associate Position. > Depending on the experience of the candidates, the salary can be > higher. But please note that the only definite sources for all > information on the positions including salary, job description and > application/selection procedure are the central websites of the > University of Oldenburg. > > For the PhD positions see: > http://www.uni-oldenburg.de/stellen/?stelle=63323 > For the postdoc position see: > http://www.uni-oldenburg.de/stellen/?stelle=63322 > > Depending on the skills and interests of the candidates, the research > focus will be on the development of new probabilistic learning > algorithms and/or their applications to high-dimensional sensory data. > Projects can emphasize basic research for general purpose learning and > pattern recognition, or applications of algorithms to specific tasks. > > Candidates for the PhD positions have to hold a Master degree in > Physics, Computer Science, Mathematics or a closely related subject > (at the latest at the time when the contract is signed). > Analytical/mathematical skills and programming skills (e.g. python, > matlab, C++) are required for all candidates. Experiences with Machine > Learning algorithms are desirable. Experiences with acoustic data, > other types of sensory data, and an interest in the neurosciences are > a plus but are not strictly required. > > The candidates for the (postdoctoral) Research Associate position have > to hold a Master degree or (preferably) a PhD/Doctoral degree in > Physics, Computer Science, Mathematics or a closely related subject > (alternatively, a statement can be provided that a PhD/Doctoral degree > will be issued when the position is taken or shortly thereafter). > Analytical/mathematical skills, programming skills, and experience > with Machine Learning algorithms are required. Experiences with > acoustic data, other types of sensory data, and an interest in the > neurosciences are a plus but are not strictly required. The > (postdoctoral) Research Associate position is suitable for part-time > work. > > All positions can be filled immediately and are available for > initially two years with an option for extension. > > The appointed researchers will be part of a very new working > environment. The research group is currently established through a > strategic investment into Machine Learning technology at Oldenburg. > The group is located in a new building, and the Cluster of Excellence > Hearing4all is part of the German Excellence Initiative which funds > top-tier research in Germany. The cluster comprises many > interdisciplinary research groups that are currently set up or that > are extended. As a consequence, many new PhD, postdoc and faculty > positions allow for interesting collaboration opportunities and > provide an attractive scientific and social environment. The city of > Oldenburg is one of Germany's cities with the highest rated living > quality, it is close to the North See and near to the cities of Bremen > and Hamburg. > > For more information about the Machine Learning research group visit: > http://www.uni-oldenburg.de/ml/ > > For more information about the Cluster of Excellence Hearing4all > visit: http://hearing4all.eu/EN/ > > The University of Oldenburg is dedicated to increasing the percentage > of women in science. Therefore, female candidates are particularly > encouraged to apply. According to § 21 III NHG (legislation governing > Higher Education in Lower Saxony) preference will be given to female > candidates in cases of equal qualification. > > Handicapped applicants will be given preference if equally qualified. > > Please send your application including the usual documents (including > contact details for reference letters) preferably electronically (PDF) > to Jörg Lücke <joerg.luecke(a)uni-oldenburg.de> or per mail to: Carl von > Ossietzky Universität Oldenburg, Fakultät VI, Machine Learning, z.Hd. > Frau Jennifer Köllner, 26111 Oldenburg, Germany. Please use “Research > Position (PhD)” as subject line for applications to the PhD positions, > and “Research Associate Position (postdoc)” for applications to the > (postdoctoral) Research Associate position. > > The application deadline is 15 June 2014. Applications received after > this date are not guaranteed to enter the selection process. > > > MORE INFORMATION ON APPLICATION DOCUMENTS Note that detailed reviewing of the applications will start immediately after the deadline of June 15. We got some request asking for more information about application documents. Please be advised that the usual application documents include: - a scientific CV with education details including universities visited, degrees taken, final mark/rank (other skills, special courses and other interests are usually part of the CV) - if applicable (mainly postdoc applicants), a list of publications (can be part of the CV) - contact details of at least two researchers who can provide reference letters (you can also send the letters, or send one letter and one other reference) - scans of the original university degrees and translations to English if possible (unless they are in German) Some applicants also do provide a short research statement / statement of interest which can also be part of the cover letter or e-mail. It would also be interesting for us if you state when you could potentially start the position. If you have not provided all the documents stated above, please do provide them in time for the deadline of the position (15 June 2014). Please send an e-mail to Frau Jennifer Köllner <jennifer.koellner(a)uni-oldenburg.de> with subject line “Research Position (PhD) - additional documents” or “Research Position (postdoc) - additional documents” -- Jörg Lücke (PhD) Associate Professor Machine Learning Lab and Cluster of Excellence Hearing4all Department of Medical Physics and Acoustics School of Medicine and Health Sciences University of Oldenburg 26111 Oldenburg Germany www.uni-oldenburg.de/ml

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PhD and Post-doctoral opportunities in machine learning and neuroscience.
by Artur Luczak 06 Jun '14

06 Jun '14

We seek highly motivated individuals with strong computational backgrounds to work at the interface of machine learning and neuroscience. We have extensive data sets recorded with state-of-the-art optical and electrophysiological methods, spanning normal and abnormal brain function in several experimental preparations. Recent developments in unsupervised machine learning (e.g. deep neural networks) now enable discovery of high-level features of such complex data sets, and provide an opportunity for breakthroughs in understanding brain function. Conversely, many of the most influential ideas in artificial neural networks have been inspired by biological networks. We therefore also endeavor to implement computational elements revealed by recent experimental findings in order to endow artificial neural networks with more brain-like capabilities. Candidates should have a strong interest in understanding how the brain works, and expertise in computational science. Prior experience with deep learning algorithms is preferred but not required. Successful candidates will join the highly collaborative and interdisciplinary Brain Dynamics group (http://lethbridgebraindynamics.com/ ), which has outstanding research strengths in the areas of learning and memory, decision making, information coding and related diseases such as Alzheimer's, schizophrenia, addiction and stroke. Funding is available through our departmental NSERC CREATE grant in Biological Information Processing, which provides a competitive stipend and unique training opportunities in quantitative sciences and entrepreneurship. Lethbridge is a small city located two hours from Calgary, 90 minutes from the Canadian Rockies, and has a sunny and dry climate. For more information please contact Dr. Aaron Gruber <aaron.gruber(a)uleth.ca> or Dr. Artur Luczak <luczak(a)uleth.ca> Canadian Centre for Behavioural Neuroscience Department of Neuroscience, University of Lethbridge 4401 University Drive, Lethbridge, AB, T1K 3M4, Canada http://lethbridgebraindynamics.com/ http://lethbridgebraindynamics.com/artur_luczak http://lethbridgebraindynamics.com/aaron_gruber

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POSTDOC in SPINAL CORD INJURY IMAGING AND MODELING
by Andre Longtin 05 Jun '14

05 Jun '14

We seek a postdoctoral candidate to work on MRI image analysis and modeling of spinal cord injury. The candidate would be put forth for the Rick Hansen Fellowship described below. The successful translation of spinal cord repair therapies from animal models to the clinic requires an improved understanding of the injured motor and sensory tracts in humans. While histological tract tracing in animals can delineate the specific injured tracts and allow for therapy evaluation, these tracing methods cannot be applied to patients. Conventional magnetic resonance imaging (MRI) techniques allow spinal cord injury to be imaged; however they do not permit clinicians to reliably determine motor or sensory function. This limitation is due to the fact that conventional MRI imaging results are known to have poor correlation patient motor or sensory tract injury. Our objective is to develop an MRI technique that utilizes techniques such as diffusion tensor imaging (DTI), tractography and functional MRI imaging techniques as a biomarker. This will permit more detailed imaging of the spinal cord and allow improved correlation with neurological function. These improved imaging techniques can then be developed to improve correlation with patient clinical function which can then be used to improve the development and selection of successful therapeutic repair strategies in humans with spinal cord injury. The research consists in developing MRI imaging techniques such as DTI, tractography and functional MRI measures as a stand alone or complementary biomarker that correlates with specific motor or sensory tract function in humans. A large DTI dataset has been compiled from a variety of human spinal cord lesions. The candidate will quantify the level of damage and relate it through modeling and statistical analysis to the observed clinical scores. The DTI and tractography analyses will then be optimized to produce a strong biomarker. The candidate must hold a PhD degree in a relevant field, and will be co-supervised by a team of three investigators: Eve Tsai, a spinal cord neurosurgeon at the Ottawa Hospital Research Institute and University of Ottawa Faculty of Medicine; Andre Longtin, Physics and Medicine, University of Ottawa, a specialist in neural modeling, and Ian Cameron, a senior MRI medical physicist at the OHRI and University of Ottawa. Ideally the candidate would be able to start as soon as possible after receipt of a positive answer (typically within a month or two of being nominated), and no later than December 1st 2014. Applications consisting of a CV should be submitted to alongtin(a)uottawa.ca<mailto:alongtin@uottawa.ca> before June 25 2014 in time for submission of the file of the selected candidate to the Rick Hansen Institute. RICK HANSEN INSTITUTE POSTDOCTORAL FELLOWSHIP INTRODUCTION The Rick Hansen Institute is a Canadian-based not-for-profit organization committed to accelerating the translation of discoveries and best practices into improved treatments for people with spinal cord injuries. Their mission is to lead collaboration across the global SCI community by providing resources, infrastructure and knowledge, and to identify, develop, validate and accelerate the translation of evidence and best practices to reduce the incidence and severity of paralysis after SCI, improve health care outcomes, reduce long-term costs, and improve the quality of life for those living with SCI. PURPOSE OF FUND Award a fellowship to a postdoctoral fellow at the Faculty of Medicine whose research is primarily focused on knowledge transfer and/or implementation sciences related to spinal cord injury (SCI). The recipient would have an opportunity to participate in the various knowledge transfer-related projects currently funded by the Rick Hansen Institute and its SCI partners. FELLOWSHIP DETAILS Eligibility Criteria The applicant must: 1. be a Canadian citizen, a permanent resident, a person with the protected/refugee status, or an international student ; 2. intend to register as a postdoctoral fellow in the Faculty of Medicine of the University of Ottawa 3. be within 4 years of completing a PhD in knowledge transfer, implementation science, spinal cord injury or a related field; and 4. propose a program of research that is primarily focused on knowledge transfer and/or implementation sciences related to spinal cord injury (SCI). Note: The awarding of the fellowship is conditional to the recipient’s registration as a postdoctoral fellow at the Faculty of Medicine, through the Faculty of Graduate and Postdoctoral Studies (as per the FGPS policy on postdoctoral appointments - http://www.grad.uottawa.ca/Default.aspx?tabid=1412) Value of the fellowship: $75,000 CAN per year Number of fellowships: 1 Frequency of awarding: For one year, with possibility of renewal for a second year Level or program of study: Postdoctoral Application contact: Graduate Studies Office, Faculty of Medicine Application deadline: June 30, 2014

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Call For Participation to our upcoming Research Topic: Real-world unisensory and multisensory processing
by Jason Sherwin 05 Jun '14

05 Jun '14

Dear Colleagues, It is with great pleasure that I invite you to contribute to a Special Issue for Frontiers. The topic description can be found on its homepage via the link below. We look forward to your boundary-pushing contributions in this exciting area of psychology, neuroscience and other related disciplines. In collaboration with Frontiers in Psychology, section Perception Science, we are organizing a Research Topic titled "Real-world unisensory and multisensory processing: Perceptual and neural mechanisms in complex natural scene processing”, hosted by Jason Sherwin, Jeremy Gaston, Kelvin Oie. As host editor, I would like to encourage you to contribute to this topic. Frontiers, a Swiss open-access publisher, recently partnered with Nature Publishing Group to expand its researcher-driven Open Science platform. Frontiers articles are rigorously peer-reviewed, can be disseminated freely and are widely read by your colleagues and by the broader scientific and medical research communities. The idea behind a research topic is to create an organized, comprehensive collection of several contributions, as well as a forum for discussion and debate. Contributions can be articles describing original research, methods, hypothesis & theory, opinions, etc. We have created a homepage on the Frontiers website (Frontiers in Psychology, section Perception Science) where all articles will appear after peer-review and where participants in the topic will be able to hold relevant discussions: http://www.frontiersin.org/Perception_Science/researchtopics/Real-world_uni…. Frontiers will also compile an e-book, as soon as all contributing articles are published, that can be used in classes, be sent to foundations that fund your research, to journalists and press agencies, or to any number of other organizations. Among others, we have contacted the following authors: Bruno Lucio Giordano David C Jangraw : author Edward A Vessel : author Daniel P Ferris : author Bill Geisler Peter Gerhardstein Giulio Tononi : author Uri Hasson : author Jacek Dmochowski : author Joel Snyder Jordan Muraskin : author Tzyy-Ping Jung : author Klaus Gramman : author Linbi Hong : author Andreas Lozano : author As such, a manuscript accepted for publication incurs a publishing fee, which varies depending on the article type. Research Topic manuscripts receive a significant discount on publishing fees. Please take a look at this fee table: http://www.frontiersin.org/about/PublishingFees. Once published, your articles will remain free to access for all readers, and will be indexed in PubMed and other academic archives. As an author in Frontiers, you retain the copyright to your own papers and figures. I would be delighted if you considered participating in this Research Topic. Should you choose to participate, please confirm by sending me a quick email and then your abstract no later than Jun 27, 2014 using the following link: http://www.frontiersin.org/submissioninfo Please note that the deadline for manuscript submission is on: Dec 19, 2014 Since I am using the Frontiers system to manage this topic, I would really appreciate if you could also please indicate your decision by clicking on one of the links below. AGREE to Participate http://www.frontiersin.org/AcceptContributor.aspx?activationkey=8c7388de-dd… DECLINE to Participate http://www.frontiersin.org/DeclineContributor.aspx?activationkey=8c7388de-d… With best regards, Jason Sherwin Guest Associate Editor, Frontiers in Perception Science www.frontiersin.org Jason Sherwin, Ph.D. Columbia University Department of Biomedical Engineering Laboratory for Intelligent Imaging and Neural Computing 530 West 120th Street, Mail Code: 8904 New York, NY 10027 Phone: +1 - 212 - 854 - 8997 Email: jason.sherwin(a)columbia.edu Managing Editor, IEEE Transactions on Neural Systems and Rehabilitation Engineering Email: managertnsre(a)gmail.com http://tnsre.bme.columbia.edu

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Virtual Physiology for Teaching: SimNerv, SimNeuron etc.
by Hans Albert Braun 04 Jun '14

04 Jun '14

Dear Colleagues, we would like to draw your attention to the recently reprogrammed "*Virtual Physiology*" teaching tools, including two neurophysiology labs: SimNerv and SimNeuron. More information is given on our website www.virtual-physiology.com from where you also can download fully functioning demo versions for Windows. * **SimNerv* offers a realistic lab environment on the computer screen to perform classical experiments of compound action potential recordings from the frog sciatic nerve. All parameters of the stimulating and recording devices are freely adjustable. Mathemathical algorithms guarantee for appropriate reactions of the nerves, also considering their physological diversity. *SimNeuron* offers easy to overlook laboratories for voltage- and current clamp experiments on the basis of simplified Hodgkin-Huxley type equations, described, for example, in Tchaptchet et al., Brain Res, 1536: 159-167. A neuron editor allows altering the neuron's parameters to examine their impact on the neurons' dynamics. In addition to these neurophysiology labs you also can download fully functioning demo versions of *SimMuscle* and *SimHeart*, featuring classical experiments with nerve-muscle preparations of the frog and with the isolated heart of the rat in the widely used Langendorff set-up for drug perfusion. To ask for more information, reprints (see short list below), or prices for fully licensed versions for teaching, please, send a reply. We hope you and your students will enjoy working in the virtual physiology laboratories. Best regards Hans Braun and the Virtual Physiology team */ References: /*/see also //*http://www.uni-marburg.de/fb20/physiologie/ags/braun/publications*/* */or //*https://www.res**earchgate.net/profile/Hans_Braun/contributions?ev=prf_act*/ */SimNeuron related references:/* Tchaptchet A, Postnova S, Finke C, Schneider H, Huber MT, Braun HA (2013): Modelin Neuronal Activity in Relation to Experimental Voltage-/Patch-Clamp Recordings. Brain Res 1536: 159-167, http://dx.doi.org/10.1016/j.brainres.2013.06.029 /PubMed: //http://www.ncbi.nlm.nih.gov/pubmed/23911648 Science direct: //http://www.sciencedirect.com/science/article/pii/S0006899313009153/ -/or send a mail to braun(a)uni-marburg.de / Postnova S, Finke C, Huber MT, Voigt K, Braun HA (2011): Conductance-Based Models of Neurons and Synapses for the Evaluation of Brain Functions, Disorders and Drug Effects. In: Biosimulation in Biomedical Research, Health Care and Drug Development. Eds.: Erik Mosekilde, Olga Sosnovtseva, Amin Rostami-Hodjegan. Springer, Wien - New York, pp 93 -- 126. /Can be downloaded from our website www.virtual-physiology .com/ //*/General reports on the first Virtual Physiology release:/* Braun HA (2003) Virtual versus real laboratories in life-science education: Concepts and experiences. In: Jukes N and Chiuia M (Eds) From guinea pig to computer mouse. Interniche, pp 81-87. /Can be downloaded from our website www.virtual-physiology .com/ Bahar S (2000) The Real and Virtual Laboratory: A Conversation with Dr. Hans Braun. In: The Biological Physicist, The Newsletter of the Division of Biological Physics of the American Physical Society. Vol.1 No.1 June 2001 p 5-7. /Can be downloaded from our website www.virtual-physiology .com/ */A recent Poster for the 2014 Conference of the German Physiological Society:/* //Braun HA, Tchaptchet A, dell Oro-Friedl J, Immer D, Schneider H, Braun T, Postnova S, Semyachkina-Glushkovskaya O, Schwabedal J, Wenzel S, Voigt K, Hirsch M: Learning by Doing with the Virtual Physiology Series:Physiological and Pharmacological in silico Experiments. /Can be downloaded from our website www.virtual-physiology .com/ -- Hans A. Braun Neurodymics Lab, Institute of Physiology, Philipps University of Marburg Deutschhaustr. 2, D-35037 Marburg, Germany Phone: +49 6421 286-2305, Mobile: +49 173 319 3028, email: braun(a)staff.uni-marburg.de URL: www.uni-marburg.de/fb20/physiologie/ags/braun see also: www.virtual-physiology.com/

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New code repository for Comp Neuro undergrad course materials
by Rob Clewley 03 Jun '14

03 Jun '14

Hi, I have taught two introductory comp neuro classes based mostly on Hugh Wilson's great book, with some additional material from Izhikevich's recent book. The course is intended to be a very practical introduction to the basics of DEs and dynamical systems for modeling neural systems. It focuses on electrophysiology of single neurons and small networks, and is split between lecture and lab format. I have updated a lot of the codes from Wilson's book and put them into PyDSTool, or at least in Python. I never got the chance to rigorously organize these codes, but have decided to post them on github as they are because I've already been contacted by some instructors who wanted to to use PyDSTool for their comp neuro class. There are some homework and project ideas up there too. Feel free to fork and contribute on github for others to benefit from any further input. https://github.com/robclewley/compneuro http://www.ni.gsu.edu/~rclewley/Teaching/CompNeuro/NEUR4030.html Best, Rob -- Robert Clewley, Ph.D. Assistant Professor Neuroscience Institute and Department of Mathematics and Statistics Georgia State University PO Box 5030 Atlanta, GA 30302, USA tel: 404-413-6420 fax: 404-413-5446 http://neuroscience.gsu.edu/rclewley.html

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Applicant Call for Goettingen's Computational Neuroscience Summer Course
by Maximilian Puelma Touzel 03 Jun '14

03 Jun '14

Applications are invited for the 12th summer course on COMPUTATIONAL NEUROSCIENCE in Göttingen, Germany September 08th - 12th, 2014 Organized by Agostina Palmigiano, Joscha Liedtke and Maximilian Puelma-Touzel The course is intended to provide graduate students and young researchers from within and outside of neuroscience with working knowledge of theoretical and computational methods used in the field and to acquaint them with recent developments therein. The focus of this year's course is modelling and theory of neuronal ensembles. The course includes tutorials and lectures from the following researchers: * Carl Van Vreesijk, Universite Rene Descartes, Paris, France * Claudia Clopath, Imperial College London, London, England * Srdjan Ostojic, École Normale Supérieure, Paris, France * Gasper Tkacik, Institute of Science and Technology, Vienna, Austria * Alfonso Renart, Champalimaud Centre for the Unknown, Lisbon, Portugal The course takes place at the Department of Nonlinear Dynamics of the Max-Planck Institute for Dynamics and Self-Organization, Göttingen. A course fee of 100 Euro includes participation in the tutorials, study materials, and part of the social events. The number of participants is limited to 30. Course language is English. To apply please fill out the application form at: http://www.bccn-goettingen.de/events/cns-course by *August 5, 2014* Best wishes and looking forward to seeing you in Göttingen, Max Puelma Touzel (on behalf of the organizers) Max Planck Institute for Dynamics and Self-Organization Fassberg 17, 37077 Goettingen, Germany +49 551 5176 444 Office +49 (0)176 6 876 2821 Mobile mptouzel(a)nld.ds.mpg.de www.nld.ds.mpg.de Maximilian Puelma Touzel Max Planck Institute for Dynamics and Self-Organization Fassberg 17, 37077 Goettingen, Germany +49 551 5176 444 Office +49 (0)176 6 876 2821 Mobile <mailto:mptouzel@nld.ds.mpg.de> mptouzel(a)nld.ds.mpg.de <http://www.nld.ds.mpg.de/> www.nld.ds.mpg.de

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PhD position in computational modelling
by Jane Neumann 03 Jun '14

03 Jun '14

The Max Planck Institute for Human Cognitive and Brain Sciences Leipzig and the Collaborative Research Center (CRC) 1052 "Obesity mechanisms" at the Leipzig University Hospital are offering a PhD studentship in computational modelling. Within the project, computational modelling is used to investigate leaning and decision-making in humans. The project is aimed at the multi-modal integration of available and newly acquired behavioral, structural/functional magnetic resonance imaging (MRI), PET and genetic data towards comprehensive computational and neurocognitive models of cognitive control over behavior. Work will be carried out at the Max Planck Institute for Human Cognitive and Brain Sciences Leipzig under the supervision of Dr Jane Neumann and Dr Annette Horstmann. Both Leipzig's long tradition in conducting neuroscientific research and the ultra-modern equipment at the Max Planck Institute provide an environment that offers new perspectives in neuroimaging research. Further, the position will be part of the CRCs Integrated Research Training Group. This graduate program offers interdisciplinary qualification in various research methods and transferable skills, and provides support in career planning and in establishing an own scientific network. Applicants should hold a Master's degree (or equivalent) in one of the following disciplines: computational or cognitive neuroscience, computer science, maths, psychology, biology, cognitive science or related. Prior experience in the fields of computational neuroscience and/or neuroimaging are of great advantage. Sound knowledge of statistics and excellent programming skills are essential. A good command of written and spoken English is requested of all applicants. Please send your application as a single pdf-file to neumann(a)cbs.mpg.de referring to 'Modelling SFB 1052'. Complete applications include cover letter, CV, letter(s) of recommendation, and copies of university degrees and additional certificates. Informal enquiries should be made to Dr Jane Neumann (+49 (0) 341 99 40 26 21). For meeting Dr. Neumann and Dr. Horstmann at *OHBM 2014*, please send an email to neumann(a)cbs.mpg.de or horstmann(a)cbs.mpg.de including days and times when you are available during the conference. The salary is based on the German E 13 TV-L salary scale. In order to increase the proportion of female staff members, applications from female scientists are particularly encouraged. Disabled applicants are preferred if qualification is equal. Deadline for application: until position is filled Further details about the project can also be found at www.o-brain-project.de

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