- Comp-neuro - lists.cnsorg.org

Graduate studies in the neuroscience of spatial motion estimation and motor planning

by Andrea Green

Graduate studies in the neuroscience of spatial motion estimation and motor planning Département de Neurosciences, Université de Montréal Applications are invited for doctoral and postdoctoral studies in systems neuroscience in the laboratory of Dr. Andrea Green. The successful applicant will join a multidisciplinary research group studying how the brain integrates multisensory cues to create estimates of our spatial motion and how such estimates are used for perception and motor planning. Research in my laboratory involves computational models of the nervous system, behavioral and neural recording experiments in non-human primates as well as human behavioral studies. Depending on the applicant's qualifications and interests, they will help to design and conduct behavioral and/or neurophysiological experiments, analyze data, develop theoretical models of neural systems, prepare manuscripts for publication, and participate in international conferences. While students with a strong background in biological sciences, engineering, mathematics, or computer science, are particularly encouraged to apply, all motivated students with an interest in understanding the brain will be considered. The successful applicant will receive a competitive salary in accordance with university guidelines. For further information, please contact Dr. Andrea Green (andrea.green(a)umontreal.ca) Applicants are asked to submit a curriculum vita, a transcript of previous studies, and the contact information for two references to: Dr. Andrea Green andrea.green(a)umontreal.ca Tel: 514-343-6111 x3301 Département de Neurosciences, Université de Montréal C.P 6128 Succursale Centre-Ville Montréal, QC H3C 3J7, CANADA Applications will be accepted until the positions are filled.

June 10, 2014

Reminder: Valentino Braitenberg Award for Computational Neuroscience - Call for Nominations

by Kerstin Schwarzwälder

Dear Colleagues, Please let me remind you that the Bernstein Association for Computational Neuroscience <http://www.nncn.de/en/bernstein-association> invites nominations for the *Valentino Braitenberg Award for Computational Neuroscience.* The award is biannually presented by the Bernstein Association to a scientist in recognition of outstanding research that contributes to our understanding of the functioning of the brain. The major criterion for the award is the impact or potential impact of the recipient's research on the field of brain science. In the spirit of Valentino Braitenberg's research, special emphasis is given to theoretical studies elucidating the functional implications of brain structures and their neuronal network dynamics. The crucial work should preferentially have been carried out in a European institution. The awardee receives a €5.000 prize donated by the Autonome Provinz Bozen Südtirol as well as complimentary participation (registration, travel, and hotel accomodation) in the Bernstein Conference 2014. Here, the prize is awarded together with a Golden Neuron pin badge in a special ceremony that includes the Valentino Braitenberg lecture given by the awardee. Nominations may be submitted by scientists working in the field of Computational Neuroscience and should include the following documents: * One-page laudation, in which the scientific work of the candidate is honored with regard to the award's criteria * CV and list of publications *Deadline for nominations is June 16, 2014* by e-mail to info(a)bcos.uni-freiburg.de The call for nominations can be found under the following URL: www.nncn.de/en/bernstein-association/valentino-braitenberg-award-for-comput… For inquiries please contact info(a)bcos.uni-freiburg.de Best regards, Kerstin Schwarzwälder -- Dr. Kerstin Schwarzwälder Bernstein Coordination Site of the National Bernstein Network Computational Neuroscience Albert Ludwigs University Freiburg Hansastr. 9A 79104 Freiburg Germany phone: +49 761 203 9594 fax: +49 761 203 9585 schwarzwaelder(a)bcos.uni-freiburg.de www.nncn.de Twitter: NNCN_Germany <http://twitter.com/NNCN_Germany> YouTube: Bernstein TV <http://www.youtube.com/user/BernsteinNetwork> Facebook: Bernstein Network Computational Neuroscience, Germany <https://www.facebook.com/BernsteinNetwork> LinkedIn: Bernstein Network Computational Neuroscience, Germany <http://www.linkedin.com/company/bernstein-network-computational-neuroscienc…>

June 10, 2014

Integrative Brain Function Workshop: Multi-modal approaches to understand brain functions

by Joseph Lizier

Dear All, Please find below details of the Integrative Brain Function Workshop: Multi-modal approaches to understand brain functions, to be held in Melbourne on Monday 30 June. Integrative Brain Function Workshop Multi-modal approaches to understand brain functions The goal of this workshop is to offer an opportunity for neuroscientists with various backgrounds to interact and exchange ideas. Specifically, we encourage participation of neuroscientists who are interested in understanding the neural systems at the perceptual and behavioural level and who try to combine multiple methodologies, such as electrophysiology, imaging, advanced signal processing and computational modelling. Date: Monday June 30, 2014. (from 9am) Deadline for abstracts: June 9 Location: Monash Biomedical Imaging facility, 770 Blackburn Rd, Clayton VIC, Australia. Keynote speaker: David Leopold from NIH/NIMH, USA Cortical circuits underlying social visual perception in the primate brain Speakers: Elizabeth Zavitz (Monash University) Population coding of motion direction in marmoset area MT is rapid and sustained Alex Fornito (Monash University) Mapping the context-dependent organization of large-scale brain networks with fMRI Farshad Mansouri (Monash University) The role frontal pole cortex (area 10) in cognitive flexibility and executive control Ben Fulcher (Monash University) Highly comparative time-series analysis for neuroscience Stefan Bode (University of Melbourne) The application of multivariate pattern classification analysis to fMRI and EEG data Spencer Chen (University of Sydney) Population correlations and spatial discrimination in area MT Dean Freestone (University of Melbourne) Estimation of Functional Dynamics using Data-Driven Mesoscopic Nerual Modeling Rory Townsend (University of Sydney) Detecting spatiotemporal dynamics in primate neural oscillations Joseph Lizier (CSIRO, Sydney) Local active information storage in distributed cortical information processing Steven Petrou (the Florey Institute of Neuroscience and Mental Health) Revealing pathological mechanisms in genetic epilepsy Enquiries please contact Nao Tsuchiya Click here to Register http://www.med.monash.edu.au/psych/research/activities/seminars.html -- ------------------------------------------------------------------------- Nao (Naotsugu) Tsuchiya, Ph.D. 1. Associate Professor School of Psychological Sciences Faculty of Medicine, Nursing and Health Sciences, Monash University 2. ARC Future Fellow homepage: http://users.monash.edu.au/~naotsugt/Tsuchiya_Labs_Homepage/Main.html

June 10, 2014

NEURAL COMPUTATION - July 1, 2014

by Terry Sejnowski

Neural Computation - Contents -- Volume 26, Number 7 - July 1, 2014 Available online for download now: http://www.mitpressjournals.org/toc/neco/26/7 ----- Article Motor Cortex Microcircuit Simulation Based on Brain Activity Mapping George L Chadderdon, Ashutosh Mohan, Benjamin A Suter, Samuel A Neymotin, Cliff C Kerr, Joseph T Francis, Gordon MG Shepherd, and William W Lytton Letters Discovering Functional Neuronal Connectivity From Serial Patterns in Spike Train Data Casey Diekman, Kohinoor Dasgupta, Vijay Nair, and K.P. Unnikrishnan Risk-sensitive Reinforcement Learning Yun Shen, Michael J. Tobia, Tobias Sommer, and Klaus Obermayer On Criticality in High-dimensional Data Saeed Saremi, Terrence J Sejnowski Spiking Neural P Systems With Thresholds Xiangxiang Zeng, Xingyi Zhang, Tao Song, and Linqiang Pan Balanced Crossmodal Excitation and Inhibition Essential for Maximizing Multisensory Gain Osamu Hoshino Universal Approximation Depth and Errors of Narrow Belief Networks With Discrete Units Guido Francisco Montufar Multiple Tests Based on a Gaussian Approximation of the Unitary Events Method With Delayed Coincidence Count Christine Tuleau-Malot, Amel Rouis, Franck Grammont, and Patricia Reynaud-Bouret Intrinsic Graph Structure Estimation Using Graph Laplacian Atsushi Noda, Hideitsu Hino, Masami Tatsuno, Shotaro Akaho, and Noboru Murata Causal Discovery via Reproducing Kernel Hilbert Space Embeddings Zhitang Chen, Kun Zhang, Laiwan Chan, and Bernhard Scholkopf ------------ ON-LINE -- http://www.mitpressjournals.org/neuralcomp SUBSCRIPTIONS - 2014 - VOLUME 26 - 12 ISSUES USA Others Electronic Only Student/Retired $70 $193 $65 Individual $124 $187 $115 Institution $1,035 $1,098 $926 Canada: Add 5% GST MIT Press Journals, 238 Main Street, Suite 500, Cambridge, MA 02142-9902 Tel: (617) 253-2889 FAX: (617) 577-1545 journals-orders(a)mit.edu ------------

June 9, 2014

Method development for coupled EEG-fMRI (2x full EPSRC PhD studentships)

by Etienne B. Roesch

Method development for coupled EEG-fMRI (2x full EPSRC PhD studentships) The goal of the project is to develop novel methods for the joint analysis of signals from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), when they are obtained concurrently. Technological advances of the last 10 years make it possible for scientists to record both modalities concurrently, in what is now known as coupled EEG-fMRI. By simultaneously recording these two modalities, scientists can potentially say where and when neural activity is occurring, whereas previously researchers had to settle for one or the other. This engineering feat, however, has not yet been met with the ability to make use of the combination of these signals to infer more knowledge than would otherwise be gathered in separate experiments. Filling this gap is the ambition of the present project. This is a method project, which requires skills and knowledge in neuroscience, applied mathematics/statistics/physics and programming. Candidates who have a strong background in at least two of these three fields are encouraged to apply, if they are enthusiastic about the third. Two full studentships are available, and we expect to appoint one candidate who is stronger in empirical work and a second who is stronger in analytics. Candidates are expected to have had some prior exposure to at least one of the two modalities, but will receive training in both. The two students will be supervised by Dr. Etienne Roesch and receive support from Dr. Michael Lindner and Prof. Tom Johnstone, with R&D support from Brain Products, one of the world’s leading manufacturers of MRI-compatible EEG systems. The group led by Dr. Roesch fosters interdisciplinary thinking, and students will have the opportunity to engage in ongoing empirical and modelling work related to perception and action. The Centre for Integrative Neuroscience and Neurodynamics (CINN) is host to a research-dedicated 3T Siemens TRIO, with a full license for sequence development, and a full suite of MR-compatible systems. Additionally, the students will be granted access to the cluster of NVIDIA Tesla GPUs and other facilities at CINN, as well as at the Brain Embodiments Laboratory, in the School of Systems Engineering. The University of Reading is ranked as one of the UK’s 20 most research-intensive universities and as one of the top 200 universities in the world (Times Higher Education 2013). Our campus was voted first in the Times Higher Education Student Experience survey and has a Green Flag Award. It is situated 25 minutes West of London. Reading University Students’ Union was voted the 6th best in the UK (National Student Survey 2012). Essentials: Commitment to academic research and personal development; Ability to work collaboratively; Effective interpersonal and communication skills, including writing to a high standard, document preparation for technical notes and journal papers; Experience of work in interdisciplinary settings; Attention to details. Desirables: Self-guided work in developing statistical designs and approaches in research; Creative approach to problem solving; Ability to work independently; Experience in giving presentations and conveying complex ideas clearly. Eligibility: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in a relevant subject. Please note that due to restrictions on the funding this studentship is for UK/EU applicants only. Funding Details: Studentship will cover Home/EU Fees, pay the Research Council minimum stipend (£13,863 in 2014/15) for up to 3 years and include funding for international conferences. How to apply: To apply for this studentship please submit an application for a PhD in Cybernetics (full time) to the University – see http://www.reading.ac.uk/Study/apply/pg-applicationform.aspx . In the section ‘Research proposal’, please upload or copy-paste your covering letter. When prompted as part of your online application, you should provide details of the funding you are applying for, quoting the reference GS14-68. Once you have submitted your application, you should receive an email to confirm receipt of your online application. Please forward this email, along with your covering letter and CV (as pdf), to Dr. Etienne B. Roesch, e.b.roesch(a)reading.ac.uk, by the application deadline. Application Deadline: Monday 14th July 2014 (interviews in August) Further Enquiries: Please contact Dr. Etienne B. Roesch, e.b.roesch(a)reading.ac.uk. ––– Dr. Etienne B. Roesch Lecturer (Assistant Professor) in Cognitive Science University of Reading, UK http://doodle.com/MeetWithEtienne Too brief of an email? Here's why! http://emailcharter.org

June 7, 2014

an emerging neural theory of grid and place cell development: and navigation: modules, spiking dynamics, cholinergic inactivation, oscillations, and attention

by Stephen Grossberg

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

June 7, 2014

ECML Workshop - “Neural Connectomics: From Imaging to Connectivity”

by Demian Battaglia

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

June 6, 2014

REMINDER: Two PhD Positions and one Postdoc Position in Machine Learning / Sensory Data Processing

by Jörg Lücke

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

June 6, 2014

PhD and Post-doctoral opportunities in machine learning and neuroscience.

by Artur Luczak

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

June 6, 2014

v5.7 of NeuroMorpho.Org released on 30 May, 2014

by NeuroMorpho Administrator

Version 5.7 of NeuroMorpho.Org was released on 30 May, 2014. The release included 29 new data sets (1341 reconstructions), including from three new animal species (dragonfly, moth, and sheep). The database now contains 11,335 reconstructions from 144 contributing labs. More than 3.2 million reconstructions were downloaded in over 130,000 unique visits from 146 countries. Please visit the What's new<http://neuromorpho.org/neuroMorpho/WIN.jsp> page for details on data included in this release, including new species strains, brain regions, cell types, and experimental conditions. The Acknowledgements <http://neuromorpho.org/neuroMorpho/acknowl.jsp> include details on contributing labs, and the About<http://neuromorpho.org/neuroMorpho/about.jsp> page provides an updated overview of the repository content. A new user-friendly functionality, OntoSearch<http://neuromorpho.org/neuroMorpho/OntoSearch.jsp>, was introduced in this release, enabling more powerful searches of species and strains with automated synonym translation, taxonomical relations, and keyword auto-completion. The literature coverage database was also updated to include publications through March 2014. We are continuously grateful to all the data owners who freely share their data with the community. We always appreciate any and all feedback and comments. Sincerely, The NeuroMorpho.Org team -- Ruchi Parekh, Ph.D. NeuroMorpho.Org Project Lead Postdoctoral Research Fellow Center for Neural Informatics, Structures, and Plasticity Krasnow Institute for Advanced Study MS2A1, George Mason University Fairfax, VA 22030 (USA) Ph - +1-703-993-4382<tel:%2B1-703-993-4382> www.neuromorpho.org<http://www.neuromorpho.org/> nmadmin(a)gmu.edu<mailto:nmadmin@gmu.edu>?

June 6, 2014