October 2015 - Comp-neuro - lists.cnsorg.org

Postdoctoral Position in Robust Intelligence / Theoretical Neuroscience

by Stepanyants, Armen

The Center for Interdisciplinary Research on Complex Systems and the Department of Physics at Northeastern University, Boston, invite applicants for a postdoctoral position in Robust Intelligence / Theoretical Neuroscience to begin in the fall of 2015. The successful candidate will work on an AIR FORCE sponsored project to create a theoretical framework for the analysis of the effects of learning and robust memory storage on synaptic connectivity in local cortical networks. Applicants should have a Ph.D. in computational neuroscience, physics, mathematics, or related quantitative disciplines. Track record of experience in computational neuroscience, as well as excellent theoretical and computational skills are required. Knowledge of machine learning, replica/cavity theory from statistical physics, cortical neuroanatomy and connectivity is preferred. To apply, please send (1) a CV, including a list of publications, (2) a summary of prior research accomplishments, and (3) names and contact information of three reference providers by email to Prof. Armen Stepanyants, a.stepanyants(a)neu.edu<mailto:a.stepanyants@neu.edu>. The position is available immediately, and applications will be accepted until the position is filled. The initial appointment is for one year with the possibility of renewal on a yearly basis. Northeastern is an Equal Opportunity/Affirmative Action Title IX Employer. More information can be found at the Neurogeometry Lab website, www.neurogeometry.net<http://www.neurogeometry.net>. Armen Stepanyants Associate Professor Department of Physics and Center for Interdisciplinary Research on Complex Systems Northeastern University 110 Forsyth St., Boston, MA 02115 Phone: (617) 373-2944 Fax: (617) 373-2943 http://www.neurogeometry.net/

Oct. 5, 2015

PhD position in Zurich

by Jean-Pascal Pfister

Applications are invited for one PhD student position at the University of Zurich. The position is funded by a SystemsX grant from the Swiss National Science Foundation. The project is entitled "Baysian learning of quantal parameters at single synapse resolution", and involves both experimentation and theory. Most models of synaptic transmission assume that all synapses between two neurons are identical. However, there is experimental evidence for significant heterogeneity in synaptic transmission between synapses. The major goal of this project is the development of an approach that allows for quantification of functional synaptic parameters incorporating single synapse heterogeneity. Half of the project will be devoted to experiments (electrophysiological and optophysiological investigation of synaptic transmission at Drosophila synapses), while the second part will involve modeling (Bayesian learning). The PhD student will be co-supervised by Prof. Martin Müller (Institute of Molecular Life Sciences, University of Zurich) and by Prof. Jean-Pascal Pfister (Institute of Neuroinformatics, ETH and University of Zurich). Both institutes offer ideal environments for performing theory-driven experiments due to the presence of many theoretical and experimental labs. The candidate should hold a Master degree/Diplom in Physics, Biology, Neuroscience, Computational Neuroscience or a related field. She/he should have prior experience in an experimental lab, a very strong mathematical background, and good programming skills. The applicant should submit a CV, a statement of research interests, marks obtained for the Master/Diploma, and the abstract of the Master/Diploma thesis in electronic format to Jean-Pascal Pfister (jpfister(a)ini.uzh.ch <mailto:jpfister@ini.uzh.ch>) or Martin Müller (Martin.Mueller(a)imls.uzh.ch <mailto:Martin.Mueller@imls.uzh.ch>). In addition, two referees should directly send a letter of recommendation by email. The position is offered for a period of three years, starting on the 1st of January 2016. Salary scale is provided by the Swiss National Science Foundation (www.snf.ch <http://www.snf.ch/>). Deadline for application is the 30th of October 2015 or until the position is filled. +++ Martin Mueller SNSF Assistant Professor Institute of Molecular Life Sciences University of Zurich Y55-K78 Winterthurerstrasse 190 8057 Zurich Switzerland www.imls.uzh.ch/mueller.html <http://www.imls.uzh.ch/mueller.html> www.neuroscience.ethz.ch/research/molecular_cellular/mueller <http://www.neuroscience.ethz.ch/research/molecular_cellular/mueller> +++ Jean-Pascal Pfister SNSF Assistant Professor Theoretical Neuroscience Group Institute of Neuroinformatics University of Zurich and ETH Zurich Winterthurerstrasse 190 8057 Zurich, Switzerland www.ini.uzh.ch/~jpfister <http://www.ini.uzh.ch/~jpfister> www.neuroscience.ethz.ch/research/computation_modeling/pfister <http://www.neuroscience.ethz.ch/research/computation_modeling/pfister>

Oct. 5, 2015

NIPS Workshop on Modelling and Inference for Dynamics on Complex Interaction Networks

by Yasser Roudi

We invite contributions to the workshop on Modelling and Inference for Dynamics on Complex Interaction Networks Friday 11 December 2015 Palais des Congrès de Montréal, Montréal, Canada Submissions of both contributed talks and posters are welcome. Please email an abstract of up to 1 page (PDF preferred) to peter....(a)kcl.ac.uk <> by 30 October 2015 and indicate your preference for talk or poster. We will get back to you with decisions on acceptance of submissions by mid-November. Further information on the workshop theme, invited speakers etc can be found below or at http://www.netadis.eu/ <http://www.netadis.eu/> (go to "NIPS 2015" tab). Please distribute to any researchers that might be interested in participating. Many thanks, Manfred Opper, Yasser Roudi and Peter Sollich --------------------------------------------- INVITED SPEAKERS and indicative topics • Jose Bento Ayres Pereira, Boston College – learning networks of stochastic differential equations • Mathias Bethge, MPI Tübingen – dynamics of generalized linear models • Alfredo Braunstein, Polictecnico di Torino – inference problems for irreversible stochastic epidemic models • Ramon Grima, University of Edinburgh – approximate solutions of Fokker-Planck equations, Feynman system size expansion • Andrea Montanari, Stanford – to be confirmed • Federico Ricci-Tersenghi, “La Sapienza” University, Roma – cross-correlations to infer delay structure then interactions • Graham Taylor, University of Guelph – deep learning in dynamical networks ORGANIZERS Prof Manfred Opper (Technische Universitaet Berlin, Germany), Prof Yasser Roudi (NTNU, Trondheim, Norway), Prof Peter Sollich (King’s College London, UK) CONTACT Prof Peter Sollich: peter....(a)kcl.ac.uk <> ABSTRACT Inference and learning on large graphical models, i.e. large systems of simple probabilistic units linked by a complex network of interactions, is a classical topic in machine learning. Such systems are also an active research topic in the field of statistical physics. The main interaction between statistical physics and machine has so far been in the area of analysing data sets without explicit temporal structure. Here methods of equilibrium statistical physics, developed for studying Boltzmann distributions on networks of nodes with e.g. pairwise interactions, are closely related to graphical model inference techniques; accordingly there has been much cross-fertilization leading to both conceptual insights and more efficient algorithms. Models can be learned from recorded experimental or other empirical data, but even when samples come from e.g. a time series this aspect of the data is typically ignored. More recently, interest has shifted towards dynamical models. This shift has occurred for two main reasons: * Most of the interesting systems for which statistical analysis techniques are required, e.g. networks of biological neurons, gene regulatory networks, protein-protein interaction networks, stock markets, exhibit very rich temporal or spatiotemporal dynamics; if this is ignored by focusing on stationary distributions alone this can lead to the loss of a significant amount of interesting information and possibly even qualitatively wrong conclusions. * Current technological breakthroughs in collecting data from the complex systems referred to above are yielding ever increasing temporal resolution. This in turn allows in depth analyses of the fundamental temporal aspects of the function of the system, if combined with strong theoretical methods. It is widely accepted that these dynamical aspects are crucial for understanding the function of biological and financial systems, warranting the development of techniques for studying them. In the past, the fields of machine learning and statistical physics have cross-fertilised each other significantly. E.g. the establishment of the relation between loopy belief propagation, message passing algorithms and the Bethe free energy formulation has stimulated a large amount of research in approximation techniques for inference and the corresponding equilibrium analysis of disordered systems in statistical physics. It is the goal of the proposed workshop to bring together researchers from the fields of machine learning and statistical physics in order to discuss the new challenges originating from dynamical data. Such data are modelled using a variety of approaches such as dynamic belief networks, continuous time analogues of these – as often used for disordered spin systems in statistical physics –, coupled stochastic differential equations for continuous random variables etc. The workshop will provide a forum for exploring possible synergies between the inference and learning approaches developed for the various models. The experience from joint advances in the equilibrium domain suggests that there is much unexplored scope for progress on dynamical data. Possible topics to be addressed will be: Inference on state dynamics: – efficient approximation of dynamics on a given network, filtering, smoothing – inference with hidden nodes – existing methods including dynamical belief propagation & expectation propagation, variational approximations, mean-field and Plefka approximations; relations between these, advantages, drawbacks – alternative approaches Learning model/network parameters: – with/without hidden nodes Learning network structure: – going beyond correlation information

Oct. 5, 2015

PhD positions in Marie-Curie European Training Network (Synaptic Dysfunction in Alzheimer Disease)

by Tomas Hromadka

A Marie Sklodowska Curie Actions sponsored European Training Network in Synaptic Dysfunction in Alzheimer Disease (SyDAD) is seeking 15 PhD students. ============ www.sydad.eu ============ RESEARCH SCHOOL SyDAD is an interdisciplinary PhD programme including an innovative research programme with cutting edge methodology, an excellent training programme, international exchanges and a translational and collaborative orientation. AIMS 1. To foster a new generation of researchers with an innovative mind-set and full understanding of the requirements of academia, pharmaceutical companies, the clinics and the societal challenges. 2. To, through a collaborative research programme, elucidate how the different pathways underlying synaptic dysfunction in Alzheimer Disease (AD) relate to each other, to identify novel pharmaceutical targets and to elaborate a drug discovery platform. THE DOCTORAL EDUCATION PROJECTS AND THE DUTIES OF THE STUDENT The doctoral students will use biochemical, cell biological, electrophysiological and in vivo methods as well as clinical material. The network has access to a wide repertoire of cutting-edge methodology including super-resolution microscopy, mass spectrometry, in vivo electrophysiology and optogenetics. The doctoral students will be seconded to other sites of the network during shorter periods to best utilize the resources of the network and participate in a common training programme at the different sites of the network. PROJECTS For more information about the projects, please see the SyDAD web site www.sydad.eu Karolinska Institutet, Stockholm, Sweden ESR 10: Targeting Cholesterol homeostasis and synaptic maturation. Contact: angel.cedazo-minguez(a)ki.se ESR 11: Synaptic proteome and Aβ interactome in AD brain and mouse models. Contact: susanne.frykman(a)ki.se ESR 13: EEG as a functional central biomarker in AD. Contact: vesna.jelic(a)ki.se ESR 14: Mitochondria stabilisers and synaptic function in AD. Contact: maria.ankarcrona(a)ki.se Apply to the projects at Karolinska Institutet at http://ki.se/en/about/jobs-at-karolinska-institutet University of Bordeaux, France ESR 2: Role of APP in presynaptic mechanisms. Contact: gael.barthet(a)u-bordeaux.fr ESR 7: Mitochondrial dysfunction in relation to synaptic function in mouse models of Alzheimer’s disease. Contact: Sandrine Pouvreau sandrine.pouvreau(a)u-bordeaux.fr ESR 12: Plasticity of local hippocampal circuits in mouse models of Alzheimer's disease: relation with episodic memory encoding. Contact: christophe.mulle(a)u-bordeaux.fr Apply to the projects at University of Bordeaux by e-mail: christophe.mulle(a)u-bordeaux.fr University of Milano, Italy ESR 1: Linking actin-dependent dendritic spine remodelling and ADAM10 activity in AD: the role of CAP2. Contact: monica.diluca(a)unimi.it ESR 9: A spine to nucleus signalling pathway in Alzheimer's disease. Contact: fabrizio.gardoni(a)unimi.it ESR 15: Development of cell permeable peptides capable of increasing ADAM10 activity. Contact: monica.diluca(a)unimi.it Apply to the projects at University of Milano by e-mail: monica.diluca(a)unimi.it and fabrizio.gardoni(a)unimi.it Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany ESR 3: Cascade linking Aß and tau-dependent toxicity to synapse loss. Contact: Mandelkow(a)dzne.de ESR 4: Loss-of-function genetic screening using CRISPR/Cas9. Contact: Daniele.Bano(a)dzne.de ESR 8. Synaptic plasticity and calcium remodelling. Contact: office-nicotera(a)dzne.de Apply to the projects at DZNE by e-mail: application(a)dzne.de Janssen Pharmaceuticals, Beerse, Belgium ESR 6: The roles of physiological and pathophysiological tau in synapse function and morphology. Contact: jpitaalm(a)ITS.JNJ.com Apply for the Janssen position here: http://jobs.jnj.com/s/u3z0MT Axon Neuroscience, Bratislava, Slovakia ESR 5: Rescue of truncated Tau-mediated synaptic dysfunction in vivo. Contact: novak(a)axon-neuroscience.eu For further information on the application process at Axon Neuroscience, please contact novak(a)axon-neuroscience.eu ENTRY REQUIREMENTS The entry requirements for each participating university will apply. ENTRY REQUIREMENTS OF MARIE SKLODOWSKA-CURIE ACTIONS The applicant should be a postgraduate researcher in the first four years (full-time equivalent) of their research activity, including the period of research training, who has not been awarded a doctoral degree. NOTE! Mobility rule: The researcher must not have resided or carried out his/her main activity (work, studies, etc) in the country of his/her host organisation for more than 12 months in the 3 years immediately prior to his/her recruitment. SKILLS AND PERSONAL QUALITIES - The applicants should have a genuine interest and solid education in Neuroscience at a master level or equivalent. - The applicants should have excellent practical skills in performing laboratory work. For specific requirements for the different projects, please contact the respective supervisor. - The applicants should have substantial experience in performing independent research projects, e g a Master thesis project - The applicants should have good collaborative skills and be open-minded. - The applicants should show good proficiency in written and spoken English equivalent to TOEFL or IELTS. APPLICATION PROCESS An application must contain the following documents in English: - A personal letter and curriculum vitae including references - A list of potential other project(s) within the SyDAD network the applicant applies for in prioritised order. - A copy of degree certificates and associated certificates. DEADLINE OF APPLICATION October 25th 2015

Oct. 2, 2015

Open-rank Tenure-track Position in (Computational) Neuroscience

by Erik De Schutter

The Okinawa Institute of Science and Technology [OIST] Graduate University (http://www.oist.jp <http://www.oist.jp/>) invites applications for a faculty position in neuroscience. OIST emphasizes interdisciplinary research and teaching. Current faculty covers multiple disciplines in biology, chemistry, mathematics and physics; including a strong presence in experimental and theoretical neuroscience. Appointments can be made as Assistant Professor (tenure track), Associate Professor (tenured) or Professor (tenured). This is part of a plan to hire 50 new faculty members by 2023. We seek applicants with outstanding scholarship, creativity, and interdisciplinary interests. Further information and details of the application procedure may be accessed at https://groups.oist.jp/facultypositions <https://groups.oist.jp/facultypositions> Inquiries should be directed to Professor Ken Peach, Dean of Faculty Affairs, faculty-recruiting(a)oist.jp <mailto:faculty-recruiting@oist.jp> Applications should be made online before 15th November 2015. OIST is a new, English-language graduate university offering a world-class research environment and has an international research community with faculty, students and staff from over 50 countries. Faculty receives research budgets and has access to top of the line equipment. The campus is located on a beautiful, subtropical island in Okinawa, Japan. OIST Graduate University is an equal opportunity educator and employer committed to increasing the diversity of its faculty, students and staff by having proactive policies in place. We provide a family-friendly working environment, including a bilingual child development center on campus. Applications from women and other underrepresented groups are strongly encouraged. See https:// <https://groups.oist.jp/ged>groups.oist.jp/ged <https://groups.oist.jp/ged>.

Oct. 1, 2015