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

CNS 2015: Abstract Submission and Registration opens
by Tam, Nicoladie 17 Jan '15

17 Jan '15

Organization for Computational Neurosciences (OCNS) 24th Annual Meeting Prague, Czech Republic July 18-23, 2015 The main meeting (July 19-21) will be preceded by a day of tutorials (July 18) and followed by two days of workshops (July 22-23). Invited Keynote Speakers: Jack Cowan, University of Chicago, USA Gustavo Deco, Universitat Pompeu Fabra, Spain Adrienne Fairhall, University of Washington, USA Wulfram Gerstner, EPFL, Switzerland Registration will open on January 14, 2015. Abstract submission will open on January 15, 2015 and close on February 22. Workshop proposals are now being accepted. Note that one of the authors has to register as sponsoring author for the main meeting before abstract submission is possible. In case the abstract is not accepted for presentation, the registration fee will be refunded. For up-to-date conference information, please visit http://www.cnsorg.org/cns-2015-prague ---------------------------------------- OCNS is the international member-based society for computational neuroscientists. Become a member to be eligible for travel awards and more. Visit our website for more information: http://www.cnsorg.org

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CoSMo 2015 summer school announcement
by Gunnar Blohm 16 Jan '15

16 Jan '15

*Fifth Annual Computational Sensory-Motor Neuroscience Summer School (CoSMo 2015)* Radboud University Nijmegen, The Netherlands June 28 - July 11, 2015 We would like to invite you to join us for the fifth annual Computational Sensory-Motor Neuroscience Summer School. The course is about experimental, computational and medical aspects of sensory-motor neuroscience with a focus on active learning. Covered topics include multi-sensory integration, motor learning & control, computational methods, sensory-motor transformations and neural coding / decoding. An important focus is on doing research as opposed to just hearing about it. Each teaching module will take up two days with morning lecture sessions. Afternoon sessions involve hands-on Matlab programming, simulation and data-analysis. Newly acquired computational tools can also be applied in 2-week evening group research projects. The course is aimed at students and post-doctoral fellows from diverse backgrounds including Life Sciences, Psychology, Computer Science, Physics, Mathematics and Engineering. Basic knowledge in calculus, linear algebra and Matlab is expected. Enrollment will be limited to 40 trainees. *Application deadline: Mar 15, 2015* For more information and to apply, please go to http://www.compneurosci.com/CoSMo/ The school is co-organized by Drs Gunnar Blohm, Paul Schrater, John van Opstal, Pieter Medendorp and Konrad Körding. This year, it receives funding from the EU FP7 Marie-Curie IDP Training Network /HealthPAC/, and the Perception, Action and Control research network of the Donders Institute at Radboud University Nijmegen. -- ------------------------------------------------------- Dr. Gunnar BLOHM Associate Professor in Computational Neuroscience Association for Canadian Neuroinformatics and Computational Neuroscience (CNCN) Centre for Neuroscience Studies, Departments of Biomedical and Molecular Sciences, Mathematics & Statistics, and Psychology, School of Computing, and Canadian Action and Perception Network (CAPnet) Queen’s University 18, Stuart Street Kingston, Ontario, Canada, K7L 3N6 Tel: (613) 533-3385 Fax: (613) 533-6840 Email: Gunnar.Blohm(a)QueensU.ca Web: http://www.compneurosci.com/

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New funding opportunity: Machine Intelligence from Cortical Networks (MICrONS) Program
by R. Jacob Vogelstein 16 Jan '15

16 Jan '15

I am pleased to announce the release of the Machine Intelligence from Cortical Networks (MICrONS) program Broad Agency Announcement (BAA). MICrONS seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain. The program is expressly designed as a dialogue between data science and neuroscience, in which participants will have the unique opportunity to pose biological questions with the greatest potential to advance theories of neural computation and obtain answers through carefully planned experimentation and data analysis. Over the course of the program, participants will use their improving understanding of the representations, transformations, and learning rules employed by the brain to create ever more capable neurally-derived machine learning algorithms. Ultimate computational goals for MICrONS include the ability to perform complex information processing tasks such as one-shot learning, unsupervised clustering, and scene parsing, aiming towards human-like proficiency. The program overview is copied below; the full text of the announcement (and any future versions) is available from the BAA link at http://www.iarpa.gov/index.php/research-programs/microns/microns-baa. All questions about the program and/or BAA must be submitted to dni-iarpa-baa-14-06(a)iarpa.gov by February 9, 2015. Full proposals must be submitted through the IARPA IDEAS <https://iarpa-ideas.gov> system by March 13, 2015. *Do not *send any questions or proposal submissions to me directly. Please disseminate this information widely. Offerors need not be U.S. citizens or residents to apply or receive funding. Thank you, Jacob R. Jacob Vogelstein, Ph.D. Program Manager ODNI/IARPA http://go.usa.gov/FPPJ -------------------------------------------------------- Introduction Despite significant progress in machine learning over the past few years, today’s state of the art algorithms are brittle and do not generalize well. In contrast, the brain is able to robustly separate and categorize signals in the presence of significant noise and non-linear transformations, and can extrapolate from single examples to entire classes of stimuli. This performance gap between software and wetware persists despite some correspondence between the architecture of the leading machine learning algorithms and their biological counterparts in the brain, presumably because the two still differ significantly in the details of operation. The MICrONS program is predicated on the notion that it will be possible to achieve major breakthroughs in machine learning if we can construct synthetic systems that not only resemble the high-level blueprints of the brain, but also employ lower-level computing modules derived from the specific computations performed by cortical circuits. Background Many contemporary theories of cortical computing suggest that the brain performs common sensory information processing tasks—such as detection and recognition of visual objects, sounds, and odors—with algorithms that progressively transform data through a series of operations, or “stages.” Each stage of processing is further theorized to occur within a discrete region of cortex. Although different theories suggest different mathematical bases for computation, it is commonly believed that neural algorithms employ data representations, transformations, and learning rules that are conserved across stages. <#14ae94b7b82a2eef_14ae94551cb4b667_14ae942f2d046d85_14acb3be21100ccb__ftn1> It should therefore be possible to apprehend the neural computations underlying information processing (at least within a given sensory modality <#14ae94b7b82a2eef_14ae94551cb4b667_14ae942f2d046d85_14acb3be21100ccb__ftn2>) by interrogating a small fraction of the entire cortex, so long as that fraction is judiciously selected to contain sufficient evidence of the representations, transformations, and learning rules of the algorithm(s) to which it contributes. Neuroscience has a long history of inspiring innovation in machine learning, starting with the seminal work of McCulloch and Pitts in 1943. This influence is evident even in today’s state of the art “deep learning” systems, which are loosely modeled on hierarchical visual processing systems in the primate brain. However, the rate of effective knowledge transfer between neuroscience and machine learning has been slow because of divergent scientific priorities, funding sources, knowledge repositories, and lexicons. As a result, very few of the ideas about neural computing that have emerged over the past few decades have been incorporated into modern machine learning algorithms. Previous attempts to foster collaboration between neuroscience and machine learning have been stymied in part by gaps in our knowledge about the brain. The majority of what is known about the brain today regards its operation at the “micro” scale (one or a few neurons) and the “macro” scale (hundreds of thousands or millions of neurons), and some of this information is indeed reflected in the design of leading artificial neural networks. In contrast, much less is known about the “mesoscale” cortical circuits (hundreds to tens of thousands of neurons) that implement the specific data representations, transformations, and learning rules of cortical information processing algorithms, and these are therefore absent from (or speculative in) existing machine learning solutions. It is likely that explicit knowledge and use of these computations is required to move beyond the current generation of “neurally-inspired” machine learning algorithms. Program Synopsis The MICrONS program aims to create novel machine learning algorithms that use neurally-inspired architectures *and* mathematical abstractions of the representations, transformations, and learning rules employed by the brain to achieve brain-like performance. To guide the construction of these algorithms, performers will conduct targeted neuroscience experiments that interrogate the operation of mesoscale cortical computing circuits, taking advantage of emerging tools for high-resolution structural and functional brain mapping. The program is designed to facilitate iterative refinement of algorithms based on a combination of practical, theoretical, and experimental outcomes: performers will use their experiences with the algorithms’ design and performance to reveal gaps in their understanding of cortical computation, and will collect specific neuroscience data to inform new algorithmic implementations that address these limitations. Ultimately, as performers incorporate these insights into successive versions of the machine learning algorithms, they will devise solutions that can perform complex information processing tasks aiming towards human-like proficiency. Program Structure MICrONS is organized in three phases, totaling five years in duration. During each phase, performers conduct targeted neuroanatomical and neurophysiological studies to inform their understanding of the cortical computations underlying sensory information processing and, concurrently, create neurally-derived machine learning algorithms that perform similar functions. Performers motivate their experimental and algorithmic designs by formulating and updating a conceptual model or “theoretical framework” for neural information processing in a given sensory modality. They use computational neural models (i.e., executable mathematic or algorithmic simulations of neurons and neural circuits) to establish a correspondence between the computations performed by biological wetware and the computations employed by their machine learning software. Each phase ends with an information processing challenge that assesses how well the new algorithms perform on increasingly challenging machine learning tasks: similarity discrimination in Phase 1, generalization and classification in Phase 2, and invariant recognition in Phase 3. Performers use the results of their experiments in each phase to guide their development of improved algorithms in the subsequent phase (in Phase 1, performers base their algorithms on the existing neuroscience literature). Technical Areas The MICrONS program comprises three Technical Areas (TAs). Although IARPA anticipates receiving a number of holistic proposals responding to all three TAs, it recognizes that some prospective offerors may have capabilities in only a subset of the overall program scope, and wishes to maximize its opportunity to leverage these capabilities. Therefore, offerors may choose to propose to one, two, or all three TAs. Because achieving MICrONS program goals will require significant collaboration across all three TAs, offerors who propose to only one or two TAs should be prepared to work closely with performers in the remaining TAs. The TAs in MICrONS are defined as follows: · TA1 – experimental design, theoretical neuroscience, computational neural modeling, machine learning, neurophysiological data collection, and data analysis; · TA2 – neuroanatomical data collection; and · TA3 – reconstruction of cortical circuits from neuroanatomical data and development of information technology systems to store, align, and access neural circuit reconstructions with the associated neurophysiological and neuroanatomical data. Success in the MICrONS program will require extensive communication and cooperation between performers in all three TAs within or across teams. For example, in TA2, performers must collect neuroanatomical data about the same brain regions *in the same brain specimens* that are used in TA1 for neurophysiological studies; in TA3, performers must reconstruct neural circuits from the data collected in TA2; and in TA1, performers must analyze the neural circuits generated in TA3 and use the resulting insights in formulating their machine learning algorithms and theoretical frameworks. All offerors are therefore required to include in their proposal a detailed management plan and a detailed description of how their proposed technical approach in one or more TAs is likely to impact the other TAs.

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Bernstein Conference 2015: Call for Workshops
by Seeger, Simone 16 Jan '15

16 Jan '15

The National Bernstein Network Computational Neuroscience invites proposals for Satellite Workshops directly preceding the Bernstein Conference 2015 in Heidelberg ********************************************************************************** Call for Workshop proposals: Workshops: September 14, 2015 (Main Bernstein Conference: September 15-17, 2015) Deadline of proposal submission: March 15, 2015 Notification of acceptance: April 20, 2015 Conference Registration starts: April 27, 2015 Early Registration Deadline: July 21, 2015 ********************************************************************************** The Bernstein Conference started out as the annual meeting of the National Bernstein Network Computational Neuroscience and has become the largest single-track Computational Neuroscience conference in Europe in recent years. Since 2013, the Bernstein Conference hosts pre-conference workshops, which have developed swiftly into a well-attended event. They supply a stage to debate topical research questions and challenges in Computational Neuroscience and related fields, different points of view and scientific approaches in an informal setting. Workshops addressing controversial issues, open problems, and comparisons of competing approaches are encouraged. SCHEDULE: September 14, 2015, 9:00 - 18:30. You may apply for either half-day or full-day workshops. Workshop costs: The Bernstein Conference does not provide financial support, but workshop organizers and speakers are offered free workshop registration and reduced fees for the main conference. For further information about the conference, please visit the conference website<http://www.bernstein-conference.de>. DETAILS FOR WORKSHOP PROPOSALS: The Workshop Proposal form can be downloaded here<http://www.nncn.de/de/bernstein-conference/2015/satellite-workshops/worksho…>. Deadline for submission of Workshop Proposals: March 15, 2015 We are looking forward to meeting you in Heidelberg! THE WORKSHOP PROGRAM COMMITTEE *** Simone Seeger, M.A. Administration Bernstein Center for Computational Neuroscience Zentralinstitut für Seelische Gesundheit Postfach 12 21 20, 68072 Mannheim J5, 68159 Mannheim Telefon: 0621/1703-1326 oder 06221/54-8310 Fax: 0621/1703-2915 E-Mail: Simone.Seeger(a)zi-mannheim.de<mailto:Simone.Seeger@zi-mannheim.de> Internet: http://www.bccn-heidelberg-mannheim.de<http://www.bccn-heidelberg-mannheim.de/>

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Call for Papers - IJCAI15 Workshop on Sensitivity Analysis and Robustness in Probabilistic Graphical Models
by Alessandro Antonucci 16 Jan '15

16 Jan '15

IJCAI15 Workshop on Sensitivity Analysis and Robustness in Probabilistic Graphical Models Buenos Aires, July 25-27, 2015 - http://ipg.idsia.ch/wijcai15/ FIRST CALL FOR PAPERS Probabilistic graphical models are important tools in machine learning and artificial intelligence for reasoning with uncertainty. They provide means to represent large multivariate domains compactly and to perform sophisticated learning and reasoning efficiently. Examples of probabilistic graphical models are Bayesian networks, Markov Random fields, chain and factor graphs, Gaussian graphical models, to name but a few. The quantification of these models usually requires sharp (i.e., precise) assessments of the model local potentials and might be subject to robustness issues. For instance, perturbations of some parameter values may lead to different decisions from those which would be achieved by the unperturbed model, suggesting that decisions are not reliable. Reliability might also be in question because of missing data and assumptions behind the process. The workshop invites submissions of papers on all aspects of sensitivity analysis and robustness in probabilistic graphical models. Contributions may have a theoretical focus and/or an applied focus. A non-exhaustive list of topics follows. - Local and/or global sensitivity analysis. - Parameter-based and/or decision-based sensitivity analysis. - Design of robust learning, inference and/or decision making approaches. - Robust analysis and design of robustness measurements. - Extensions of probabilistic graphical models. - Reliable qualitative learning and reasoning. - Robust treatment of missing data. - Imprecise probability and other theories related to sensitivity analysis. - Computational complexity, exact and approximate algorithms. Each submission will be reviewed by peers using a double-blind process (please use the third person in self citations and take all necessary care not to identify yourselves). Accepted papers will be published electronically in a volume of the JMLR Workshop and Conference Proceedings series. There will be no rebuttal phase, but contributions considered worth publishing and needing substantial revision might be subject to a second round of reviewing/evaluation. All accepted papers will be presented at the workshop. At least one of the paper's authors should register and attend the workshop to present the work. Submissions must be formatted according to style and template files available for the Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings - two-column version. The style files are available at http:/ipg.idsia.ch/wijcai15/sarpgm15.tar.gz Papers (including figures, tables, references, etc) are expected to have between 6 and 10 pages. IMPORTANT DATES Apr 27, 2015 - Deadline for submissions of contributions May 20, 2015 - Workshop paper acceptance notification May 30, 2015 - Deadline for workshop camera-ready copy (in case of minor revision; contributions needing major revision might need additional time - this will be arranged case by case) PC MEMBERS Alessandro Antonucci*, IDSIA, Switzerland. Alessio Benavoli, IDSIA, Switzerland. Cassio P. de Campos*, Queen's University Belfast, UK. Arthur Choi, University of California, Los Angeles, USA. Giorgio Corani*, IDSIA, Switzerland. Fabio Cozman, University of Sao Paulo, Brazil. Adnan Darwiche, University of California, Los Angeles, USA. Sebastien Destercke, Univ. de Technologie de Compiegne, France. Marek Druzdzel, University of Pittsburgh, USA. Johan Kwisthout, Radboud University Nijmegen, The Netherlands. Agnieszka Onisko, Bialystok University of Technology, Poland. Denis Maua, University of Sao Paulo, Brazil. Serafin Moral, Universidad de Granada, Spain. Silja Renooij, Universiteit Utrecht, The Netherlands. Matthias Troffaes, University of Durham, UK. (*: Workshop organizers.) More details about the submission procedure are available online. http://ipg.idsia.ch/wijcai15/ ++++++++++++++++++++++++++++++++++++++++ (We apologize in case you receive multiple copies of this announcement, but yet we hope to reach the greatest possible number of people. Finding a trade-off is not an easy task.) -- _________________________________ Alessandro Antonucci IDSIA Dalle Molle Institute for Artificial Intelligence Via Cantonale (Galleria 2) CH-6928, Manno-Lugano, CH mail: alessandro(a)idsia.ch skype: alessandro.antonucci tel: +41 916108515 web: www.idsia.ch/~alessandro _________________________________

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Connectionists: Call for Papers (Extended Deadline to 5 Feb 2015): IEEE IJCNN'2015 Special Session on: "Emerging Methodologies for Big Data Integration"
by Dr Amir Hussain 15 Jan '15

15 Jan '15

CALL FOR PAPERS IEEE IJCNN 2015 Special Session on *"*Emerging Methodologies for Big Data Integration*"* July 12 - 17, 2015, Killarney, Ireland ( http://www.ijcnn.org/ ) ************************************************************************************** Important Announcement *************************************************************************************** Due to numerous requests, the IJCNN has kindly agreed to extend all paper submission deadlines to February 5th, 2015. ************************************************************************************* NEW: IMPORTANT DATES (REVISED) EXTENDED DEADLINE for Paper submission: February 5th, 2015 Paper Decision notification: March 25th, 2015 Camera-ready submission: April 25th, 2015 Conference Dates: July 12 - 17th, 2015 *********************************************************** Over the years, huge quantities of data have been generated by large-scale scientific experiments (biomedical, “omic”, imaging, astronomical, etc.), big industrial companies and on the web. One of the main characteristics of such Big Data is that they are multi-view, i.e. there are multiple sources (in the “omics” sciences, experiments related to mRNA, miRNA etc.), relate the same patterns (in this case patients) or multi-domain (in biomedical applications for examples, “omics, imaging and clinical data). As a consequence, new methodologies based on neural networks, machine and statistical learning, computation Intelligence and others, have been proposed to integrate these kinds of big data and to elicit relevant information to infer novel models and correlations. The aim of the special session is to solicit new approaches to real world scientific and industrial big data integration, as well as applications of above mentioned Big Data methodologies. *Topics** Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics: Multi-view learning Multi-view clustering data fusion data integration multi-view data applications multi domain data applications THE DEADLINE FOR THE PAPER SUBMISSION TO THE SPECIAL SESSION IS THE SAME OF IJCNN 2015, January 15th 2015. All the submissions will be peer-reviewed with the same criteria used for other contributed papers. Perspective authors will submit their papers through the IJCNN2015 conference submission system at http://www.ijcnn.org/ Please make sure to select the Special Session "Emerging Methodologies for Big Data Integration " from the "S. SPECIAL SESSION TOPICS" name in the "Main Research topic" dropdown list; Templates and instructions for authors will be provided on the IJCNN webpage http://www.ijcnn.org/ All papers submitted to the special sessions will be subject to the same peer-review procedure as regular papers, accepted papers will be published in the conference proceedings. Further information about IJCNN 2015 can be fond at http://www.ijcnn.org/ and about the special session at http://neuronelab.unisa.it/emerging-methodologies-for-big-data-integration/ We look forward to seeing you soon in Kilarney! *********************************************************** **Organizers** - Amir Hussain Professor of Computing Science and founding Director of the Cognitive Signal-Image Processing and Control Systems Research (COSIPRA) Laboratory, University of Stirling, UK (E-mail: ahu(a)cs.stir.ac.uk http://cs.stir.ac.uk/~ahu) - Giovanni Montana Professor and Chair in Biostatistics and Bioinformatics, Biomedical Engineering Department, King’s College, London, UK (E-mail: giovanni.montana(a)kcl.ac.uk) - Francesco Carlo Morabito Professor and Chair of the Neurolab, Dipartimento DICEAM, Università Mediterranea di Reggio Calabria, Italy (E-mail: morabito(a)unirc.it) - Roberto TAGLIAFERRI Professor and Chair of the Neuronelab, Dipartimento di Informatica, Università di Salerno, Italy (E-mail: robtag(a)unisa.it) **Technical Program Committee (being continuously updated)** Elia Mario Biganzoli, Università di Milano, Italy Erik Cambria, NTU, Singapore Ciro Donalek, Caltech, CA, USA Anna Esposito, Seconda Università di Napoli, Italy Marcos Faundez-Zanuy, Escola Universitaria Politecnica de Mataro (Tecnocampus), Spain Alexander Gelbukh, National Polytechnic Institute, Mexico Dario Greco, FIOH, Finland Newton Howard, MIT Media Lab, USA Pietro Liò, University of Cambridge, UK Bin Luo, Anhui University, China Mufti Mahmud, Antwerp University, Belgium Riccardo Rizzo, CNR, Italy Jingpeng Li, University of Stirling, UK Domenico Ursino, Università Mediterranea di Reggio Calabria, Italy Alfredo Vellido, Universidad Politécnica de Cataluña, Spain Pierangelo Veltri, Università "Magna Graecia" di Catanzaro, Italy Jonathan Wu, University of Windsor, Canada Yunqing Xia, Tsinghua University, China Kang Li, Queen's University, Belfast, UK Dongbing Gu, Essex University, UK Vincent C. Müller, Anatolia College/ACT, Greece & Oxford University, UK Dongbin Zhao, Chinese Academy of Sciences, Beijing, China Paulo Lisboa, Liverpool John Moores University, UK ************************************************************************************** -- The University of Stirling has been ranked in the top 12 of UK universities for graduate employment*. 94% of our 2012 graduates were in work and/or further study within six months of graduation. *The Telegraph The University of Stirling is a charity registered in Scotland, number SC 011159.

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Postdoctoral and PhD positions in computational neuroscience at Harvard
by Haim Sompolinsky 15 Jan '15

15 Jan '15

Dear colleagues, I have several openings for research in computational neuroscience at the doctoral and postdoctoral levels. See the following announcement. Best, Haim Opportunities in Theoretical Neuroscience Doctoral and Postdoctoral Opportunities in Theoretical Neuroscience I am seeking doctoral and postdoctoral associates for research on theoretical neuroscience projects. Creativity, analytical and numerical skills, drive, and a background in physics, computational neuroscience, applied mathematics, or computer science are expected. Research topics span a broad range of topics dealing with the principles underlying the links between neuronal circuits' structure, dynamics, behavior and cognition. Positions are available for work in the Swartz theoretical neuroscience group at Harvard http://cbs.fas.harvard.edu/ <http://cbs.fas.harvard.edu/> . Please submit your application including CV, list of publications and names of three possible referees to Prof. Haim Sompolinsky: haim(a)fiz.huji.ac.il <mailto:haim@fiz.huji.ac.il>. -- Haim Sompolinsky The Hebrew University For research positions, see: http://neurophysics.huji.ac.il/Opportunities

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CFP: HRI 2015 Workshop on “Cognition: A Bridge between Robotics and Interaction”
by Yukie Nagai 14 Jan '15

14 Jan '15

============================================================ Workshop “Cognition: A Bridge between Robotics and Interaction”, at HRI 2015, Portland (OR) USA ============================================================ March 2, 2015 Submission deadline: January 20, 2015 Notification of acceptance: January 30, 2015 website: http://www.macs.hw.ac.uk/~kl360/HRI2015W/ ============================================================ INVITED SPEAKERS: - Prof. David Vernon, Sk?vde University - Prof. Andrew Meltzoff, University of Washington INVITED PANELISTS: - Prof. Giulio Sandini, Italian Institute of Technology - Prof. Minoru Asada, Osaka University A key feature of humans is the ability to anticipate what other agents are going to do and to plan accordingly a collaborative action. This skill, derived from being able to entertain models of other agents, allows for the compensation for intrinsic delays of human motor control and is a primary support to allow for efficient and fluid interaction. Moreover, the awareness that other humans are cognitive agents who combine sensory perception with internal models of the environment and others, enables easier mutual understanding and coordination. Cognition represents therefore an ideal link between different disciplines, as the field of Robotics and that of Interaction studies, performed by neuroscientists and psychologists. From a robotics perspective, the study of cognition is aimed at implementi ng cognitive architectures leading to efficient interaction with the environment and other agents. From the perspective of the human disciplines, robots could represent an ideal stimulus to study which are the fundamental robot properties necessary to make it perceived as a cognitive agent, enabling natural human-robot interaction. Ideally, the implementation of cognitive architectures may raise new interesting questions for psychologists, and the behavioral and neuroscientific results of the human-robot in teraction studies could validate or give new inputs for robotics engineers. The aim of this workshop will be to provide a venue for researchers of different disciplines to discuss the possible points of contact and to highlight the issues and the advantages of bridging different fields for the study of cognition for interaction. T his workshop will represent an ideal continuation of the discussion began at HRI 2014, in the workshop “HRI: a bridge between Robotics and Neuroscience” (http://www.macs.hw.ac.uk/~kl360/HRI2014W/index.html). LIST OF TOPICS ------------- - Cognitive Architecture - Development of Social Cognition - Interaction - Prediction - Embodiment - Self and Other FORMAT AND SUBMISSIONS ----------------------- The workshop will consist of invited keynotes, time for discussions and will also feature a poster session. Prospective participants are invited to submit full papers (up to 8 pages) or short papers (2 pages). Submissions will be accepted in PDF format only, using the HRI formatting guidelines (http://www.macs.hw.ac.uk/~kl360/HRI2015W/papers.html) and including author names. Authors should send their papers to hri2015workshop(a)gmail.com . All submissions will be peer-reviewed. Upon available time, selected contributions may have the opportunity to be presented in the oral session. The other selected contributions will be presented as posters during a dedicated session. The submission must include 1 answer to one of the following questions: - How should cognitive research be structured to yield results useful for robotics and HRI? - How can robotics have a direct influence on neuroscience and cognitive psychology aimed at interaction studies? - Which is the minimal level of cognition needed in a robot to be able to interact with a human? - Does a robot really need cognition to be perceived as a cognitive agent by a human? - Does inserting a cognitive agent into an interaction pose a risk to the human partners? - How important is the embodiment of a robot for the development of its cognitive architecture and its social cognition? Upon available time, those questions/answers will be used to "drive" a final discussion. IMPORTANT DATES ---------------- Submission deadline: January 20, 2015 Notification of acceptance: January 30, 2015 Workshop at HRI 2015: March 2, 2015 ORGANIZERS ----------- - Alessandra Sciutti Istituto Italiano di Tecnologia - Katrin Solveig Lohan Heriot-Watt University - Yukie Nagai Osaka University — Yukie Nagai, Ph.D. Specially Appointed Associate Professor, Osaka University Visiting Researcher, Bielefeld University yukie(a)ams.eng.osaka-u.ac.jp http://cnr.ams.eng.osaka-u.ac.jp/~yukie/

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INNS BigData 2015 San Francisco - New Conference! Calls for Papers, Special Sessions, Tutorials and Workshops!
by Asim Roy 14 Jan '15

14 Jan '15

Apologies for cross-posting. Note the plenary talk by Juergen Schmidhuber (Prof. Jürgen Schmidhuber<http://people.idsia.ch/~juergen/>) on Deep Learning. There will also be a tutorial and a workshop on Deep Learning by Juergen Schmidhuber and Dong Yu of Microsoft Research (Dong Yu<http://research.microsoft.com/en-us/people/dongyu/>, Microsoft Research<http://research.microsoft.com/en-us/>). Note the deadlines for submission of proposals for special sessions, tutorials and workshops. See you in San Francisco. <http://www.innsbigdata.org/> [http://innsbigdata.org/wp-content/uploads/2014/09/banner.jpg]<http://www.innsbigdata.org/> INNS Conference on Big Data 2015 New approaches to solving hard Big Data problems! 8 - 10 August 2015, San Francisco www.innsbigdata.org<http://www.innsbigdata.org/> The aim of the INNS BigData conference is to promote new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of Big Data Analytics to solve real-world problems (e.g. weather prediction, transportation, energy management). Please refer to our website for a more detailed list of topics. Being INNS' inaugural conference on the theme of big data, we are especially motivated to synthesize ideas, promote activities and generate broad interest in areas where neural networks have many unique advantages. We also have Twitter<https://twitter.com/inns_bigdata>, Facebook<https://www.facebook.com/innsbigdata15/> and Google+<https://plus.google.com/112891798437473029046> pages! ________________________________ [http://innsbigdata.org/wp-content/uploads/2014/09/new.gif]PLENARY TALK<http://innsbigdata.org/> [http://innsbigdata.org/wp-content/uploads/2014/11/juergen.jpg] DEEP LEARNING Prof. Jürgen Schmidhuber<http://people.idsia.ch/~juergen/>, Professor of Artificial Intelligence at the University of Lugano<http://www.inf.usi.ch/index.htm>, and the Swiss AI Lab IDSIA.<http://www.idsia.ch/> Since age 15 or so, Prof. Jürgen Schmidhuber’s main scientific ambition has been to build an optimal scientist through self-improving Artificial Intelligence (AI), then retire. He has pioneered self-improving general problem solvers since 1987, and Deep Learning Neural Networks (NNs) since 1991. The Long Short-Term Memory (LSTM) recurrent NNs (RNNs), developed by his research groups at the Swiss AI Lab IDSIA & USI & SUPSI & TU Munich, were the first RNNs to win official international contests. LSTM recently helped to improve connected handwriting recognition, speech recognition, machine translation, optical character recognition, image caption generation, and are now in use at Google, Microsoft, IBM, and many other companies. IDSIA’s Deep Learners were also the first to win object detection and image segmentation contests, and achieved the world’s first superhuman visual classification results, winning nine international competitions in machine learning & pattern recognition (more than any other team). Since 2009 he has been member of the European Academy of Sciences and Arts. He has published over 300 peer-reviewed papers, earned seven best paper/best video awards, and is recipient of the 2013 Helmholtz Award of the International Neural Networks Society. ________________________________ Important Dates:<http://innsbigdata.org/important-dates/> * Paper submission:<http://innsbigdata.org/paper-submission/> March 22, 2015. * Paper Decision Notification: May 22, 2015. * Camera Ready Submission of papers: June 8, 2015. Call for Special Sessions:<http://innsbigdata.org/special-sessions/> * Deadline: January 22, 2015 * Any proposal can be sent by e-mail to: INNSBigData2015SpecialSessions(a)gmail.com<mailto:INNSBigData2015SpecialSessions@gmail.com> Call for Tutorials<http://innsbigdata.org/tutorials/> and Workshops:<http://innsbigdata.org/workshops/> * Deadline: January 22, 2015 * Any questions can be sent to the Tutorials & Workshops Chairs: Marley Vellasco (PUC-Rio. Rio de Janeiro. Brazil)<mailto:Marley%20Vellasco%20(PUC-Rio.%20Rio%20de%20Janeiro.%20Brazil)%20%3cmarley@ele.puc-rio.br%3e> and Trevor Martin (Univ. of Bristol, UK)<mailto:Trevor%09Martin%20(Univ.%20of%20Bristol.%20UK)%20%3ctrevor.martin@bristol.ac.uk%3e>. ________________________________ [http://innsbigdata.org/wp-content/uploads/2014/09/new.gif]The Elsevier USD 2000 Big Data Best Paper Award:<http://innsbigdata.org/best-paper-award/> This award recognizes the best paper presented at the INNS Big Data conference. Both application and theoretical papers will be considered. It will be awarded by the Big Data Analytics Section of the International Neural Network Society and is sponsored by Elsevier. The Award consists of a plaque and a $2000 honorarium. ________________________________ Dr. Fen Zhao Talk<http://innsbigdata.org> Dr. Fen Zhao, a Staff Associate at the Office of the Assistant Director (OAD) for Computer & Information Science & Engineering (CISE) at the National Science Foundation, will give a talk on national big data R&D initiative and on building public-private partnerships around CISE's Big Data, next generation internet, and cybersecurity R&D portfolios. ________________________________ PLENARY SPEAKERS:<http://innsbigdata.org/> [http://innsbigdata.org/wp-content/uploads/2014/11/Bin-Yu.jpg] Prof. Bin Yu<https://www.stat.berkeley.edu/~binyu/Site/Welcome.html>, Chancellor´s Professor, University of California<http://www.universityofcalifornia.edu/>, Berkeley. Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley. She held faculty positions at UW-Madison and Yale University and was a Member of Technical Staff at Lucent Bell Labs. She was Chair of Department of Statistics at Berkeley from 2009 to 2012, and is a founding co-director of the Microsoft Joint Lab on Statistics and Information Technology at Peking University where she is also Chair of the scientific advisory committee of the Center for Statistical Sciences. She has published over 80 scientific papers in premier journals in statistics, machine learning, information theory, signal processing, remote sensing, neuroscience, network analysis, and bioinformatics. She is a Member of the U.S. National Academy of Sciences, and a Fellow of the American Academy of Arts and Sciences. [http://innsbigdata.org/wp-content/uploads/2014/11/Raghu.jpg] Prof. Raghu Ramakrishnan<http://pages.cs.wisc.edu/~raghu/>, Head of Cloud and Information Services Lab (CISL) and big data team, Microsoft<http://research.microsoft.com/en-us/events/fs2013/raghu-ramakrishnan_bigdat…> Raghu Ramakrishnan heads the Cloud and Information Services Lab (CISL) in the Data Platforms Group at Microsoft, and leads development for the Big Data team. From 1987 to 2006, he was a professor at University of Wisconsin-Madison, where he wrote the widely-used text “Database Management Systems” and led a wide range of research projects in database systems (e.g., the CORAL deductive database, the DEVise data visualization tool, SQL extensions to handle sequence data) and data mining (scalable clustering, mining over data streams). In 1999, he founded QUIQ, a company that introduced a cloud-based question-answering service. He joined Yahoo! in 2006 as a Yahoo! Fellow, and over the next six years served as Chief Scientist for the Audience (portal), Cloud and Search divisions, driving content recommendation algorithms (CORE), cloud data stores (PNUTS), and semantic search (“Web of Things”). Ramakrishnan has received several awards, including the ACM SIGKDD Innovations Award, the SIGMOD 10-year Test-of-Time Award, the IIT Madras Distinguished Alumnus Award, and the Packard Fellowship in Science and Engineering. [http://innsbigdata.org/wp-content/uploads/2014/11/brenda.jpg] Prof. Brenda Dietrich,<https://www-03.ibm.com/ibm/history/witexhibit/wit_fellows_dietrich.html> IBM Fellow and VP, Leads the Emerging Technologies Team for IBM Watson, IBM<http://www.ibm.com/ibm/ideasfromibm/us/ibm_fellows/> Brenda Dietrich is an IBM Fellow and Vice President. She joined IBM in 1984 and has worked in the area now called analytics for her entire career, applying data and computation to business decision processes throughout IBM. For over a decade she led the Mathematical Sciences function in the IBM Research division where she was responsible for both basic research on computational mathematics and for the development of novel applications of mathematics for both IBM and its clients. She has been the president of INFORMS, has served on the Board of Trustees of SIAM, and is a member of several university advisory boards. She holds more than a dozen patents, has co-authored numerous publications, and frequently speaks on analytics at conferences. She was elected to the National Academy of Engineering in 2014. She holds a BS in Mathematics from UNC and an MS and Ph.D. in OR/IE from Cornell. Her personal research includes manufacturing scheduling, services resource management, transportation logistics, integer programming, and combinatorial duality. She currently leads the emerging technologies team for IBM Watson, extending and applying IBM’s cognitive computing technology. ________________________________ [http://innsbigdata.org/wp-content/uploads/2014/09/new.gif]TUTORIALS & WORKSHOPS: TUTORIALS * Deep Learning - Profs. Juergen Schmidhuber<http://people.idsia.ch/~juergen/> (University of Lugano<http://www.inf.usi.ch/index.htm>, and the Swiss AI Lab IDSIA<http://www.idsia.ch/>) and Dong Yu<http://research.microsoft.com/en-us/people/dongyu/> (Microsoft Research<http://research.microsoft.com/en-us/>) * Introduction to How Brain Deals with Big Data - Juyang Weng<http://www.cse.msu.edu/~weng/> (Michigan State University<http://www.msu.edu/>) * Platforms and Algorithms for Big Data Analytics - Prof. Chandan K. Reddy<http://www.cs.wayne.edu/~reddy/> (Wayne State University<http://wayne.edu/>) * Big Data Analytics, Machine Learning Cognitive Algorithms and the Mind - Prof. Leonid I. Perlovsky<http://www.northeastern.edu/cos/psychology/people/faculty/> (Northeastern University<http://www.northeastern.edu/>) * Spiking Neural Networks and Neuromorphic Spatio-Temporal Data Machines - Prof. Nikola Kasabov <http://www.aut.ac.nz/profiles/nikola-kasabov> (Auckland University of Technology<http://www.aut.ac.nz/>) * Online Learning for Big Data Analytics - Prof. Irwin King<https://www.cse.cuhk.edu.hk/irwin.king.new/> (Chinese University of Hong Kong<http://www.cuhk.edu.hk/english/index.html>) WORKSHOPS * Deep Learning - Profs. Juergen Schmidhuber<http://people.idsia.ch/~juergen/> and Dong Yu<http://research.microsoft.com/en-us/people/dongyu/> * Neuromorphic Spatio-Temporal Big Data Machines - Prof. Nikola Kasabov <http://www.aut.ac.nz/profiles/nikola-kasabov> * Neural networks and wearable devices - Prof. Danilo Mandic<http://www.commsp.ee.ic.ac.uk/~mandic/> * Big Data and Power Systems - Profs. Dejan Sobajic and Kumar Venayagamoorthy * Crowd Behaviour and Big Data - Profs. Chrisina Jayne and Mehmed Kantardzic ________________________________ Neural Networks Special Issue: Neural Network Learning in Big Data<http://www.journals.elsevier.com/neural-networks/call-for-papers/special-is…> For this special issue of Neural Networks, we invite papers that address many of the challenges of learning from big data. In particular, we are interested in papers on efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of online learning to solve real-world big data problems (e.g. health care, transportation, and electric power and energy management). Manuscript submission due: January 15, 2015 Big Data Analytics Section @ INNS<http://www.inns.org/big-data-section> Considering the growing interest to process and analyse big data, the International Neural Network Society (INNS) has a new Section on Big Data Analytics (BDA) to help the neural network field position itself as a leading technology contributor to big data analytics. Anyone who is interested to know more is encouraged to visit the homepage of the INNS-BDA Section<http://www.inns.org/big-data-section>. ________________________________ We have an enthusiastic team working hard on the conference program and events. Start thinking about your paper submissions. Our Chairs for the [Special Sessions, Tutorials, and Workshops] are expecting your proposals soon - email them to discuss your ideas. Come to San Francisco next summer to take part in the future of BigData, and to have fun!! ________________________________ GENERAL CHAIRS:<http://innsbigdata.org/committees/> Asim Roy<https://webapp4.asu.edu/directory/person/9973> (email<mailto:Asim%20Roy.%20General%20Co-Chair.%20INNS%20BigData2015.%20Arizona%20StateU.%20USA%20%3cASIM.ROY@asu.edu%3e>) INNS BigData General Co-Chair Arizona State University, USA INNS Board of Governors Plamen Angelov<http://www.lancaster.ac.uk/staff/angelov/> (email<mailto:Plamen%20Angelov.%20General%20Co-Chair.%20INNS%20BigData2015.%20Lancaster%20U.%20UK%20%3cp.angelov@lancaster.ac.uk%3e>) INNS BigData General Co-Chair Lancaster University, UK Chair in Intelligent Systems ________________________________ Many thanks to our Sponsors: [http://www.inns.org/assets/site/neural.png]<http://www.inns.org/> [http://innsbigdata.org/wp-content/uploads/2014/10/elsevier-logo-300x300-150…]<http://www.elsevier.com/> To unsubscribe from this list, send an email to Jose Antonio Iglesias, INNS BigData 2015 Publicity Co-Chair, Carlos III Univ, Madrid, Spain<mailto:Jose%20Antonio%20Iglesias.%20INNS%20BigData%202015%20Publicity%20Co-Chair.%20Carlos%20III%20Univ.%20Madrid.%20Spain%20%3cBigData2015-INNS-SanFrancisco@BillHowell.ca%3e?subject=Remove%20my%20email&body=Click%20to%20send.%20This%20will%20remove%20your%20email%20address%20from%20the%20INNS%20mass%20email%20list.> with the phrase "Remove my email" in the Subject line.

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RLDM2015: Abstract submissions deadline in one month!
by Yael Niv 14 Jan '15

14 Jan '15

The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM2015) www.rldm.org<http://www.rldm.org/> June 7-10, The University of Alberta, Edmonton, Alberta, Canada ====================================================== Submissions to RLDM2015 are now being accepted at https://cmt.research.microsoft.com/RLDM2015 Deadline: 13 February 2015, midnight EST We invite extended abstracts for contributed poster presentations and oral presentations. We welcome submissions of original research related to “learning and decision making over time to achieve a goal”, coming from any discipline or disciplines, describing empirical results from human, animal or animat experiments, and/or theoretical work, simulations and modeling. Contributions should be aimed at an interdisciplinary audience, but not at the expense of technical excellence. This is an abstract-based meeting, with no published conference proceedings. As such, work that is intended for, or has been submitted to, other conferences or journals is also welcome, provided that the intent of communication to other disciplines is clear. Submissions should consist of a summary (max 2000 characters; text only), and an extended abstract of between one and four pages (including figures and references). LaTeX and RTF templates, and sample submissions, are available from http://rldm.org/rldm2015/submission-procedure/ Note: Only the summary will be made available in the (electronic) abstract booklets. The extended abstract will be used for reviewing, and will be available online only pending on authors’ separate explicit permission. Online availability will have no bearing on the review process and authors are encouraged to include new, unpublished, findings which they do not want to make publicly available. To submit your abstract please go to https://cmt.research.microsoft.com/RLDM2015 Submissions will be reviewed for relevance to the topic and for quality. Exceptional abstracts will be selected for oral presentations and for poster spotlight presentations. IMPORTANT DATES: Submissions open: 13 Dec 2015 Submissions close: 13 Feb 2015, 11:59pm EST Notification of acceptance: by March 28, 2015 (expedited reviewing for those needing an international visa can be requested) Early registration: 21 April 2015 Meeting: 7-10 June 2015, Edmonton, Alberta (*NEW* this year: Tutorials on the 7th) RLDM2015 Invited speakers: http://rldm.org/rldm2015/invited-speakers2015/ RLDM2015 Tutorials: http://rldm.org/rldm2015/tutorials/ RLDM2015 Programme Committee: http://rldm.org/rldm2015/committees/rldm2015-program-committee/ To ensure that you receive future announcements about RLDM2015 please join our mailing list at http://tinyurl.com/RLDMlist (you must log in to google to see the “join list” button, and choose “all email” from the options at the bottom).

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