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

postdoctoral position available

by Anil Seth

POSTDOC POSITION IN COGNITIVE NEUROSCIENCE OF TIME PERCEPTION AT SUSSEX UNIVERSITY Corresponding author: Anil Seth (A.K.Seth(a)sussex.ac.uk<mailto:A.K.Seth@sussex.ac.uk>) Deadline: January 30th, 2015 Further information: http://www.sussex.ac.uk/aboutus/jobs/950 3 year full time postdoctoral position at the Sackler Centre for Consciousness Science, University of Sussex. Applications are invited from highly motivated post-doctoral research scientists to join a newly funded multi-partner European project. TIMESTORM promotes time perception as a fundamental capacity of autonomous living biological and computational systems, that plays a key role in the development of intelligence. The project aims to explore the coupling of time and mind and implement for the first time artificial systems that consider the temporal aspects of cognition. The available position, within the Sackler Centre for Consciousness Science, will involve cognitive neuroscience research on the brain basis of time perception. ------------------------------------------- Anil K. Seth, D.Phil. University of Sussex, UK Professor of Cognitive and Computational Neuroscience Co-Director, Sackler Centre for Consciousness Science Editor-in-Chief, Neuroscience of Consciousness www.anilseth.com<http://www.anilseth.com> www.neurobanter.com<http://www.neurobanter.com> http://nc.oxfordjournals.org/ www.sussex.ac.uk/sackler/<http://www.sussex.ac.uk/sackler/> a.k.seth(a)sussex.ac.uk<mailto:a.k.seth@sussex.ac.uk> @anilkseth

Jan. 19, 2015

MCS 2015 : Call for Papers

by Dr. Schwenker

Apologies for multiple copies. ****** MCS 2015 Call for Papers ******** ********************************************************************** TWELFTH INTERNATIONAL CONFERENCE ON MULTIPLE CLASSIFIER SYSTEMS Reisensburg Castle (Günzburg, Germany), Ulm University, June 29 - July 1, 2015http://mcs.diee.unica.it ********************************************************************** Paper Submission: JANUARY 30, 2015 MCS 2015 is the twelfth edition of the well-established series of meetings providing the leading international forum for the discussion of issues in multiple classifier systems and ensemble methods. The aim of the workshop is to bring together researchers from diverse communities concerned with this topic, including pattern recognition, machine learning, neural networks, data mining and statistics. MCS 2015 will be held on June 29-July 1, 2015, at Reisensburg Castle (Günzburg, Germany) research center of the Ulm University. For up-to-date conference information, please visit:http://mcs.diee.unica.it Organizing Committee ==================== Friedhelm Schwenker, Ulm University, Germany Josef Kittler, University of Surrey, UK Fabio Roli, University of Cagliari, Italy -- Dr. Friedhelm Schwenker University of Ulm Institute of Neural Information Processing D-89069 Ulm, Germany phone: +49-731-50-24159 fax: +49-731-50-24156 email: friedhelm.schwenker(a)uni-ulm.de www: http://www.uni-ulm.de/in/neuroinformatik/mitarbeiter/f-schwenker.html

Jan. 18, 2015

Opportunities for Ph.D. Studentships in Computational Neuroscience

by Wong-Lin, Kongfatt

The Intelligent Systems Research Centre (ISRC) at the University of Ulster, UK, invites applications for 3-year Ph.D. studentships. A list of studentships offered for the academic year 2015-2016 and the projects’ details can be found at: http://www.compeng.ulster.ac.uk/rgs/showPhDProposals.php?ri=3. In particular, I have the available computational neuroscience projects available for 2015: 1. Computational modelling and analysis of spatiotemporal brain dynamics in decision making. 2. A neuro-inspired multisensory decision-making model with self-awareness for autonomous mobile robots. 3. Computational neuromodulation: neural circuit modelling. The computational neuroscience research at the ISRC focuses on both fundamental brain and behavioural sciences, and their applications, including clinical neuroscience and neuroengineering. The application process for the Ph.D. studentship is opened with a closing date for applications on the 27th February 2015. All studentships, which are highly competitive, are expected to start in September 2015, and include tuition fees and an annual maintenance allowance for EU and non-EU students. All applicants should hold a first or upper second class honours degree (or equivalent) in an appropriate subject, such as computer science, engineering, physics, mathematics or neuroscience. Applicants must be highly motivated and willing to pursue research and develop skills across disciplines. If you wish to apply for a studentship, please follow the instructions at: http://www.compeng.ulster.ac.uk/rgs/guideForApplicants.php. Unless indicated, successful students will be based primarily at the ISRC with opportunities to interact with other related ISRC research teams, research groups from the Biomedical Sciences Research Institute, the Centre for Stratified Medicine, and there may also be opportunities for spin-outs. A functional brain mapping facility has recently been established at the ISRC. The ISRC is situated in the city of Derry~Londonderry, which has received the City of Culture 2013 award. Please note that some studentship (DEL Awards) have restrictions on residence eligibility – see guidance notes for details. For further information, please contact me (k.wong-lin(a)ulster.ac.uk) ------------------- Dr. KongFatt Wong-Lin Computational Neuroscience Research Team Intelligent Systems Research Centre University of Ulster ________________________________ This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster(a)ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The University of Ulster was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA

Jan. 18, 2015

Journal of Mathematical Neuroscience - recent articles

by Stephen Coombes

Dear all, The Journal of Mathematical Neuroscience publishes research articles on the mathematical modeling and analysis of all areas of neuroscience. The current list of journal articles is available at http://www.mathematical-neuroscience.com/ : Uncertainty Propagation in Nerve Impulses Through the Action Potential Mechanism Torres Valderrama A, Witteveen J, Navarro M and Blom J http://www.mathematical-neuroscience.com/content/5/1/3<http://www.mathematical-neuroscience.com/content/4/1/13> Coarse-Grained Clustering Dynamics of Heterogeneously Coupled Neurons Moon SJ, Cook KA, Rajendran K, Kevrekidis IG, Cisternas J and Laing CR http://www.mathematical-neuroscience.com/content/5/1/2 Shifting Spike Times or Adding and Deleting Spikes—How Different Types of Noise Shape Signal Transmission in Neural Populations Voronenko SO, Stannat W and Lindner B http://www.mathematical-neuroscience.com/content/5/1/1 Numerical Bifurcation Theory for High-Dimensional Neural Models Laing CR http://www.mathematical-neuroscience.com/content/4/1/13 Adaptation and Fatigue Model for Neuron Networks and Large Time Asymptotics in a Nonlinear Fragmentation Equation Pakdaman K, Perthame B and Salort D http://www.mathematical-neuroscience.com/content/4/1/14 and see also the recent Special Issue on Uncertainty and the Brain http://www.mathematical-neuroscience.com/series/UB All articles are Open Access. Best regards, Steve ------------------------------------------------------- Stephen Coombes Professor of Applied Mathematics School of Mathematical Sciences Nottingham, UK Tel: 0115 846 7836 http://www.maths.nott.ac.uk/~sc/ ------------------------------------------------------- This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please send it back to me, and immediately delete it. Please do not use, copy or disclose the information contained in this message or in any attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham. This message has been checked for viruses but the contents of an attachment may still contain software viruses which could damage your computer system, you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation.

Jan. 17, 2015

CNS 2015: Abstract Submission and Registration opens

by Tam, Nicoladie

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

Jan. 17, 2015

CoSMo 2015 summer school announcement

by Gunnar Blohm

*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/

Jan. 16, 2015

New funding opportunity: Machine Intelligence from Cortical Networks (MICrONS) Program

by R. Jacob Vogelstein

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.

Jan. 16, 2015

Bernstein Conference 2015: Call for Workshops

by Seeger, Simone

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/>

Jan. 16, 2015

Call for Papers - IJCAI15 Workshop on Sensitivity Analysis and Robustness in Probabilistic Graphical Models

by Alessandro Antonucci

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 _________________________________

Jan. 16, 2015

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

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.

Jan. 15, 2015