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Postdoctoral positions in computational neuroscience at Wright State University
by Sherif Elbasiouny Jan. 12, 2015

Jan. 12, 2015

*_Postdoctoral positions in computational neuroscience_* Two postdoctoral Fellow positions in Computational Neuroscience are available immediately in the laboratory of Dr. Sherif Elbasiouny (/T//he Neuro Engineering, Rehabilitation, and Degeneration Lab/) at the Department of Neuroscience, Cell Biology, and Physiology, Wright State University, Dayton, Ohio. The research work involves the development of anatomically detailed computational models of neurons in the spinal cord to investigate the basis of their neuronal excitability. Dr. Elbasiouny’s lab is currently investigating the ionic mechanisms underlying motoneuron function for neurorehabilitation applications and in neurodegenerative diseases, such as amyotrophic lateral sclerosis. *Required*: The successful applicant should have: 1) a PhD in computational neuroscience, engineering, computer science, mathematics, or related areas, 2) previous research experience in computational neuroscience. *Desired*: Although not mandatory, it is it highly desirable if the applicant has: 1) experience in the development of anatomically detailed computational models of single cells, and 2) experience in using the NEURON simulation environment. The positions are available immediately. The salary package is competitive and will be based upon the applicant’s experience. Funding for these positions is available for couple of years with possible extension upon research progress. Qualified applicants should email Dr. Elbasiouny at sherif.elbasiouny(a)wright.edu <mailto:sherif.elbasiouny@wright.edu>with the following: 1) CV including a list of publications, 2) a brief statement of research interests, and 3) three recommendation letters emailed directly by the references to Dr. Elbasiouny.For full consideration, please apply before March 1, 2015. Wright State University is an Equal Opportunity/Affirmative Action Employer. It is the university's policy to prohibit discrimination and provide equal opportunity to all employees and applicants for employment, without regard to their race, sex (including gender identity/expression), color, religion, ancestry, national origin, age, disability, veteran status, military or sexual orientation. Regards, Sherif Elbasiouny -- Sherif M. Elbasiouny, PhD, PE Assistant Professor Departments of Neuroscience, Cell Biology, & Physiology (NCBP) and Biomedical, Industrial & Human Factors Engineering (BIE) Wright State University 143 Biological Sciences II (mail) 243 Biological Sciences II (lab) 251B Biological Sciences II (office) 3640 Col Glenn Hwy Dayton OH 45435 Office Phone: 937-775-2492 https://www.med.wright.edu/ncbp/elbasiouny

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MOOSE 3.0.1 "Gulab Jamun"
by U.S.Bhalla Jan. 11, 2015

Jan. 11, 2015

We announce the release of MOOSE 3.0.1 "Gulab Jamun" The Gulab Jamun release is the second in series 3 of MOOSE releases. Websites: http://moose.ncbs.res.in, http://sourceforge.net/projects/moose MOOSE is the Multiscale Object-Oriented Simulation Environment. It is designed to simulate neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, circuits, and large networks. MOOSE can operate at many levels of detail, from stochastic chemical computations, to multicompartment single-neuron models, to spiking neuron network models. MOOSE 3.0.1 is an evolutionary increment over 3.0.0:: - The GUI moves into beta with substantially more kinetic modeling features, as well as an early release of the neuronal modeling interface in the GUI. - Multiscale modeling updates including better cross-compartment reaction support, including stochastic reactions in subsets of the compartments. - General robustness and error reporting improvements for solvers. - HSolver updates to handle NMDA and derived classes of channels with Ca currents. - Better release packaging About the name: Gulab Jamun is a classic North Indian sweet consisting of deep brown spheres involving milk solids, cardamom and other spices, floating in a rich syrup. Best wishes and Happy New Year, The MOOSE team: Harsha Rani, Aviral Goel, Dilawar Singh, Aditya Gilra, Subhasis Ray, Upi Bhalla

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tenure-track faculty position - Assistant Professor - Systems biology and Alzheimer's disease
by Christopher Gaiteri Jan. 11, 2015

Jan. 11, 2015

tenure-track faculty position - Assistant Professor - Systems biology and Alzheimer's disease Make creative analytic contributions to understanding Alzheimer's disease at Rush University, a major center for aging and age-related disease research, located in Chicago, IL. Our Alzheimer's center offers an integrated clinical and research environment that has a constantly growing collection of multi-omic data on hundreds of individuals, at various ages and levels of cognitive function. These omics data can be studied against a backdrop of hundreds of cognitive and behavioral phenotypes that are tracked longitudinally in these subjects and thousands of others. We are searching for a tenure-track assistant professor to construct and contribute to systems biology models that enhance our understanding of age-related dysfunction and Alzheimer's disease. These models should lead to actionable, molecular or tissue-level predictions, which can be tested in experimental systems. Requirements: MD/PhD or PhD in Neuroscience, Mathematics, Systems Biology or related fields Experience with omics and big data, including any or ideally all of: structural and functional neuroimaging, proteomics, RNAseq, genetics and methylation Expertise in R, matlab and python Interests in network-based analysis of disease-related data Preference for: Publication history in graph theory, systems biology and neuroscience Familiarity with complex disease neurobiology Experience in design and implementation of large-scale cellular simulations Java, perl, parallel programming and batch execution systems Experience designing or performing high-throughput experiments Post-doctoral training The ideal candidate is free to develop an independent research program around aging, age-related disease and/or systems biology analysis. He/she will also collaborate with current faculty and develop and maintain external funding. Salary commensurate with relevant experience and skills. Instructions for applying: Send your CV to gaiteri(a)gmail.com AND ALSO include a paragraph on one of the following topics: 1. How will you identify key pathological mechanisms in a multi-omic disease setting, potentially in collaboration with experimental labs? 2. How will you develop multi-modal or multi-scale models of complex brain diseases? 3. What is the most under-appreciated statistical or mathematical technique that you expect could lead to insights in brain diseases?

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summer undergrad program in computational neuroscience
by Dave Touretzky Jan. 10, 2015

Jan. 10, 2015

Carnegie Mellon - University of Pittsburgh Joint Summer Undergraduate Program in Computational Neuroscience Undergraduates interested in receiving research training in computational neuroscience are encouraged to apply to an NIH-sponsored summer program at the Center for the Neural Basis of Cognition in Pittsburgh. The Center for the Neural Basis of Cognition is a joint interdisciplinary program of Carnegie Mellon University and the University of Pittsburgh. The 2015 program will most likely run from May 26 through July 31, 2015 (pending confirmation on dormitory availability). The final deadline for application is Feb 16, 2015. All participants must be United States citizens or permanent residents, must be enrolled at a 4-year accredited institution, and must be in their sophomore or junior year at the time of application. Any undergraduate may apply, but we are especially interested in attracting students with strong quantitative backgrounds with some experience in calculus, statistics and/or computer programming. Experience in neuroscience is not required. Students from groups underrepresented in the sciences are encouraged to apply. The core of the program is the opportunity to carry out an individual mentored research project working closely with a faculty mentor. Other aspects of the scientific program include: 12 faculty lectures on computational neuroscience at the beginning, followed by student presentations and discussion of articles from the scientific literature, presentations on career options and scientific ethics, and a concluding symposium in which students present their research. Application form is available at: http://www.cnbc.cmu.edu/summercompneuro Application can be returned via email or regular mail (see addresses below). In addition to the application, the following items are required for evaluation: * A brief (one page) essay about your interest and experience in neural computation. * Official transcript from the institution you are attending * Two letters from professional references. You should contact your recommenders and ask them to mail or email a letter directly to us. * SAT/ACT scores (do NOT have to be official; photocopies are acceptable) Documents should be mailed to: Computational Neuroscience Summer Program Center for the Neural Basis of Cognition Carnegie Mellon University 4400 Fifth Avenue Suite 115 Pittsburgh, PA 15213-2617 CNBC-summer-UG(a)andrew.cmu.edu Brief list of CMU-Pitt CNBC faculty working in computational neuroscience: John Anderson (Carnegie Mellon, Psychology) Aaron Batista (University of Pittsburgh, Bioengineering) Marlene Behrmann (Carnegie Mellon, Psychology) Marlene Cohen (University of Pittsburgh, Neuroscience) Carol Colby (University of Pittsburgh, Neuroscience) Steve Chase (Carnegie Mellon, ECE/Biomedical Engineering) Brent Doiron (University of Pittsburgh, Mathematics) William Eddy (Carnegie Mellon, Statistics) Bard Ermentrout (University of Pittsburgh, Mathematics) Julie Fiez (University of Pittsburgh, Psychology) Raj Gandhi (University of Pittsburgh, Department of Bioengineering) John Horn (University of Pittsburgh, Neurobiology) Robert Kass (Carnegie Mellon, Statistics) Charles Kemp (Carnegie Mellon, Psychology) Sandra Kuhlman (Carnegie Mellon, Biology) Tai Sing Lee (Carnegie Mellon, Computer Science) Tom Mitchell (Carnegie Mellon, Machine Learning) Carl Olson (Carnegie Mellon, Neural Basis of Cognition) Anne-Marie Oswald (University of Pittsburgh, Neuroscience) Monica Perez (University of Pittsburgh, Neurobiology & Rehabilitation) David Plaut (Carnegie Mellon, Psychology) Lynne Reder (Carnegie Mellon, Psychology) Erik Reichle (University of Pittsburgh, Psychology) Johnathan Rubin (University of Pittsburgh, Mathematics) Walt Schneider (University of Pittsburgh, Psychology) Andrew Schwartz (University of Pittsburgh, Bioengineering) Daniel Simons (University of Pittsburgh, Neurobiology) Matthew Smith (University of Pittsburgh, Ophthalmology) Peter Strick (University of Pittsburgh, Psychiatry) Michael Tarr (Carnegie Mellon, Psychology) Dave Touretzky (Carnegie Mellon, Computer Science) Robert Turner (University of Pittsburgh, Neurobiology) Nathan Urban (Carnegie Mellon, Biology) Valerie Ventura (Carnegie Mellon, Statistics) Timothy Verstynen (Carnegie Mellon, Psychology) Douglas Weber (University of Pittsburgh, Physical medicine and Rehabilitation) Byron Yu (Carnegie Mellon, ECE/Biomedical Engineering) A full list can be found at: http://www.cnbc.cmu.edu/

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Post doc/engineer position in direct brain control of muscle stimulators
by Dawn Taylor Jan. 9, 2015

Jan. 9, 2015

We currently have an opening for a postdoc or engineer for the vA-funded neuroprosthetics study described below. If interested, please send a CV to Dr. Dawn Taylor at dxt42(a)case.edu. The long-term goal of this project is to enable paralyzed individuals to use their brain signals to control their upper limb via implanted muscle stimulators. Most labs working on brain-controlled neuroprosthetics decode intended limb kinematics (e.g. velocity, joint angles, etc.) from the recorded brain signals. However, that approach still requires converting those kinematic commands into the appropriate stimulation patterns required to generate the desired limb motion. That conversion process has not been resolved for the upper limb due to the limb's complex dynamical nature and the fact that the limb is subject to unknown external forces during use. We bypass this obstacle by retraining the brain to control muscle stimulators directly. We have come up with some novel, but clinically feasible ways of mapping neural signals directly to muscle stimulators. Our methods can enable the user to have good control over both limb motion and stiffness. To demonstrate and refine our methods, we are training monkeys to control the movements of a realistic musculoskeletal model of a paralyzed limb activated via implanted muscle stimulators. The paralyzed limb simulator (developed by the lab of Robert Krisch) provides real-time visual feedback to the animal of the limb motion that would result from stimulating the paralyzed muscles based on the animal's neural signals decoded in real time. The use of this real-time paralyzed arm simulator allows us to test and refine our process of brain-controlled muscle stimulation in monkeys without actually having to paralyze any animals. -- Dawn M. Taylor, Ph.D. Assistant Professor of Neuroscience, Cleveland Clinic Researcher Scientist, Cleveland VA Medical Center, Functional Electrical Stimulation Center Assistant Professor of Biomedical Engineering & of Molecular Medicine, Case Western Reserve University The Cleveland Clinic Lerner Research Institute 9500 Euclid Ave. / NC30 Cleveland, OH 44195 email: dxt42(a)case.edu or taylord8(a)ccf.org Phone: (216) 636-0140 Fax: (216) 778-4259

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[jobs] Post Doc position in Humanoid Robotics Research
by Patricia Hazel Shaw [phs] Jan. 9, 2015

Jan. 9, 2015

We Apologise for multiple cross postings ========================================================================= Post-Doctoral Research Associate vacancy Three-year fixed-term Robotics Research Group, Department of Computer Science, Aberystwyth University, Wales, UK £32,277 – £37,384 Applicants are invited to join the Robotics Laboratory for a new project (entitled Developmental Algorithms for Robotics”). The project is focusing on developmental robotic learning based on an iCub humanoid robot. This is a three year EPSRC funded project running at Aberystwyth University, with the support of an International Scientific Advisory Board containing leading developmental psychologists. We wish to appoint two Post-Doctoral Research Associates who will develop and implement models based on the psychological literature, and conduct experiments on our iCub humanoid robot. One successful applicant will be responsible for understanding psychological literature and generating models based on the literature, whilst the other successful applicant will be responsible for software implementation and experimentation. This invitation is for the Scientific Modelling post, details below. For informal enquiries contact Patricia Shaw: phs(a)aber.ac.uk +44 (0)1970-622432 Ref: IMPACS.14.22  Closing Date: 5 February 2015  Interview Date: Week commencing 16 February 2015 For information and application forms please go to www.aber.ac.uk/en/hr/jobs/vacancies-external/ ========================================================================= The Project: “Developmental Algorithms for Robotics” The project aims to investigate a mechanism for robots, building on top of basic spatial sensorimotor competencies, that drives the autonomous development of new behaviours and self learning about novel events for which they have no prior experience. This will involve (1) expanding existing motor-babbling activity into object play behaviour, (2) formulating play behaviour as a viable mechanism for autonomous learning about unknown environments, (3) establishing an extended schema concept as a mechanism for integrating memory, experience generalization and action generation, (4) exploring action perception from the agent's experience, and (5) close monitoring of the work with psychological data and expert psychologists. The project will closely follow current knowledge on infant development and aim to reproduce behaviour as reported in the psychological literature. In particular the focus will be on three areas of core knowledge: (1) Object understanding, (2) Interaction with animate objects, and (3) Tool use. The work will be supported by collaboration with leading developmental psychologists on the project scientific advisory panel. This will produce models and mechanisms that can be implemented and tested experimentally against psychological benchmarks. The models will be validated with challenging demonstrators based on a single humanoid robotic platform (iCub). As a main outcome, the project will advance the understanding and application of intrinsic motivations in autonomous learning systems and robots. This overall goal will be achieved with the support of an International Scientific Advisory Board, consists of: Kevin O'Regan, Jacqueline Fagard, Merideth Gattis, David Whitebread, Giorgio Metta, QinetiQ and Lego. The Appointment (PDRA) – Scientific modeller Applications are invited for the post of Post-Doctoral Research Associate on this project at Aberystwyth University. The successful applicant will have a relevant background, experience of research (as demonstrated by PhD and/or publications), and proven abilities in mathematical/scientific modelling. The main tasks will involve analysing the psychological literature to develop models and experiments for testing on an iCub humanoid robot. This will be supported by another PDRA, and PhD student, who will have special responsibility for implementing the models whilst maintaining and supporting the robot. Other tasks include writing scientific documents and papers, documenting experiments and writing reports, supervising a PhD student, responsibility for day-to-day management of project needs, and collaborating with the scientific advisory panel and other researchers. Regular European travel will be involved. This post is available on a three-year fixed-term contract, funded by the EPSRC. Salary will be on the IA scale for Research Staff in the range: £32,277 - £37,384 (depending upon qualifications and experience). We expect to appoint for a start date in the first quarter of 2015.

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Postdoctoral and PhD positions, Personal Robotics lab, Imperial College London
by Demiris, Yiannis Jan. 8, 2015

Jan. 8, 2015

Dear colleagues, two research positions (either at the postdoctoral or the PhD level) in machine learning for user-modelling and human-robot Interaction are available at the Personal Robotics Laboratory of the Department of Electrical and Electronic Engineering at Imperial College London. Successful applicants will work under the supervision of Dr Yiannis Demiris (www.demiris.info) in the context of the new EU H2020 project PAL (Personal Assistants for healthy Lifestyle, 2015-2019), which aims to develop personalised robotic systems and avatars to assist diabetic (T1DM) children and their caregivers. The PAL positions are available from 1st of March 2015 until the end of the project at the end of Feb 2019 subject to renewal. Additional PhD positions through the A*STAR-Imperial partnership programme (project “Robot Learning by Demonstration for Heterogeneous Bimanual Collaboration Tasks”), the Chinese Scholarship Council programme, and the Imperial PhD scholarship scheme, among others, are also available. For all positions, applicants should have an excellent background in mathematics, machine learning and software engineering, and should be committed to applying their research to real systems interacting with people in challenging environments. The positions offer an excellent working environment in one of the world's top research universities, in one of the most exciting cities in the world. The Department of Electrical and Electrical Engineering at Imperial was ranked as the top EE department in the UK in the recent REF 2014, while its Personal Robotics Laboratory offers an energetic, friendly intellectual environment with state of the art facilities http://www.imperial.ac.uk/PersonalRobotics For further information and links to application material: http://www.imperial.ac.uk/personalrobotics/join_us With best wishes, Yiannis ---- Dr Yiannis Demiris, FIET, FBCS, FRSS Reader (Associate Professor) in Personal Robotics, Department of Electrical and Electronic Engineering, Rm 1014, Imperial College London, South Kensington Campus, Exhibition Road, London, SW7 2BT, UK Tel: +44-(0)2075946300, FaxL +44-(0)2075946274 Personal webpage: http://www.iis.ee.ic.ac.uk/yiannis Laboratory webpage: http://www.imperial.ac.uk/PersonalRobotics - Visiting Scholar, Harvard University School of Engineering and Applied Sciences (SEAS), Maxwell Dworkin Building MD-336, 33 Oxford Street, Cambridge, MA 02138, USA

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Research Topic "Metastable dynamics of neural ensembles"
by Emili Balaguer-Ballester Jan. 6, 2015

Jan. 6, 2015

Research Topic "Metastable dynamics of neural ensembles" http://journal.frontiersin.org/ResearchTopic/1955 A classical view on neural computation is that it can be characterized in terms of deterministic convergence to fixed-point-type attractor states (representing, e.g., memory patterns in Hopfield 1982) or limit-cycle-like sequential transitions among states (representing e.g. motor or syntactical sequences). Is this still a valid model of how brain dynamics implements cognition? The idea that neuro-computational dynamics is more or less deterministically driven by convergence to simple attractor states has recently been challenged both empirically and by computational work. In this Research Topic of Frontiers in Systems Neuroscience we welcome experimental studies and modelling contributions addressing the question of stable vs. transient neural population dynamics, and the potential role of noise and trial-to trial variability in neural computation. Major topics are, but are not restricted to: -Attracting and meta-stable dynamics of neural ensembles, both from empirical and computational modelling perspectives. -Coding by non-stationary and transient states in neural recordings -Spontaneous cortical activity dynamics -Metastable dynamics during cognitive processing -Oscillatory emergent patterns and propagation of waves in an excitable network -Trial-to-trial variability Please see more information in http://journal.frontiersin.org/ResearchTopic/1955. Deadline is on the 31st of March. BU is a Disability Two Ticks Employer and has signed up to the Mindful Employer charter. Information about the accessibility of University buildings can be found on the BU DisabledGo webpages This email is intended only for the person to whom it is addressed and may contain confidential information. If you have received this email in error, please notify the sender and delete this email, which must not be copied, distributed or disclosed to any other person. Any views or opinions presented are solely those of the author and do not necessarily represent those of Bournemouth University or its subsidiary companies. Nor can any contract be formed on behalf of the University or its subsidiary companies via email.

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Special Session on Transfer Learning - International Work Conference on Artificial Neural Networks, 10-12 June, 2015
by Jorge M. Santos Jan. 6, 2015

Jan. 6, 2015

Please consider to contribute to the Special Session on Transfer Learning International Work Conference on Artificial Neural Networks, 10-12 June, 2015 - http://iwann.ugr.es/2015 Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. The TL approach has gained significant interest in the Machine Learning (ML) community since it paves the way to devise intelligent learning models that can easily be tailored to many different domains of applicability. The following aspects have recently contributed to the emergence of TL: Generalization Theory: TL often produces algorithms with good generalization capability for different problems; Efficient TL algorithms: TL provides learning models that can be applied with far less computational effort than standard ML methods; Unlabeled data: TL can be advantageous since unlabeled data can have severe implications in some fields of research, such as in the biomedical field. Some examples of topics for this special session: Big Data with Deep Neural Networks; Generalization Bounds; Domain Adaptation or Covariate Shift; Algorithms for TL; New advancements in TL; Real-world applications. Deadline: 6 February 2015 Organizers Luís M. Silva, Dep. of Mathematics, University of Aveiro, Portugal - lmas(a)ua.pt Jorge M. Santos, Dep. of Mathematics, School of Engineering, Polytechnic of Porto, Portugal - jms(a)isep.ipp.pt

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CFP (Deadline, 1 Feb 2015): IEEE Computational Intelligence Magazine (CIM) Special Issue: “Computational Intelligence for Changing Environments"
by Dr Amir Hussain Jan. 6, 2015

Jan. 6, 2015

CALL FOR PAPERS (Deadline: 1 Feb 2015) - With advance apologies for any cross postings! IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (CIM) (http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10207) SPECIAL ISSUE (Nov 2015) ON "Computational Intelligence for Changing Environments" (http://www.cs.stir.ac.uk/~ahu/IEEE-CIM-CICE2015.pdf) AIMS AND SCOPE: Over the past decade or so, computational intelligence techniques have been highly successful for solving big data challenges in changing environments. In particular, there has been growing interest in so called biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors. Examples include the hierarchy of the brain neocortex and neural circuits, which have resulted in biologically-inspired features for encoding, deep neural networks for classification, and spiking neural networks for general modelling. To ensure that these models are generalizable to unseen data, it is common to assume that the training and test data are independently sampled from an identical distribution, known as the sample i.i.d. assumption. In dynamic and non- stationary environments, the distribution of data changes over time, resulting in the phenomenon of ‘concept drift’ (also known as population drift or concept shift), which is a generalization of covariance shift in statistics. Over the last five years, transfer learning and multitask learning have been used to tackle this problem. Fundamental analyses using probably approximately correct (PAC) and Rademacher complexity frameworks have explained why appropriate incorporation of context and concept drift can improve generalizability in changing environments. It is possible to use human-level processing power to tackle concept drift in changing environments. Concept drift is a real-world problem, usually associated with online and concept learning, where the relationships between input data and target variables dynamically change over time. Traditional learning schemes do not adequately address this issue, either because they are offline or because they avoid dynamic learning. However, BIL seems to possess properties that would be helpful for solving concept drift problems in changing environments. Intuitively, the human capacity to deal with concept drift is innate to cognitive processes, and the learning problems susceptible to concept drift seem to share some of the dynamic demands placed on plastic neural areas in the brain. Using improved biological models in neural networks can provide insight into cognitive computational phenomena. However, a main outstanding issue in using computational intelligence for changing environments and domain adaptation is how to build complex networks, or how networks should be connected to the features, samples, and distribution drifts. Manual design and building of these networks are beyond current human capabilities. Recently, computational intelligence methods has been used to address concept drift in changing environments, with promising results. A Hebbian learning model has been used to handle random, as well as correlated, concept drift. Neural networks have been used for concept drift detection, and the influence of latent variables on concept drift in a neural network has been studied. In another study, a timing-dependent synapse model has been applied to concept drift. These works mainly apply biologically-plausible computational models to concept drift problems. Although these results are still in their infancy, they open up new possibilities to achieve brain-like intelligence for solving concept drift problems in changing environments. Taking the current state of research in computational intelligence for changing environments into account, the objective of this special issue is to collate this research to help unify the concepts and terminology of computational intelligence in changing environments, and to survey state-of-the-art computational intelligence methodologies and the key techniques investigated to date. Therefore, this special issue invites submissions on the most recent developments in computational intelligence for changing environments, algorithms and architectures, theoretical foundations, and representations, & their application to real-world problems. We also welcome timely surveys & review papers. TOPICS OF INTEREST include (but are not limited to): • Computational intelligence methodologies and implementation for changing environments •Transfer learning, Multitask learning, Domain adaption •Incremental Learning architectures, Unsupervised and semi-supervised learning architectures •Incremental Knowledge augmentation, Representation learning and disentangling •Incremental Adaptive Neuro-fuzzy systems •Incremental and single-pass data mining •Incremental Neural Clustering & Regression •Incremental Adaptive decision systems •Incremental Feature selection and reduction •Incremental Constructive Learning •Novelty detection in Incremental learning SUBMISSION PROCESS The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via: https://easychair.org/conferences/?conf=ieeecimcdbil2015 IMPORTANT (REVISED) DATES (for November 2015 Issue) 1st Feb, 2015: Submission of Manuscripts 15th April, 2015: Notification of Review Results 15th May, 2015: Submission of Revised Manuscripts 15th June, 2015: Submission of Final Manuscripts GUEST EDITORS Professor Amir Hussain, University of Stirling, Stirling FK9 4LA, Scotland, UK Email: ahu(a)cs.stir.ac.uk http://cs.stir.ac.uk/~ahu/ Professor Dacheng Tao, University of Technology, Sydney, 235 Jones Street, Ultimo, NSW 2007, Australia Email: dacheng.tao(a)uts.edu.au Professor Jonathan Wu University of Windsor, 401 Sunset Avenue, Windsor, ON, Canada Email: jwu(a)uwindsor.ca Professor Dongbin Zhao Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China E-mail: dongbin.zhao(a)gmail.com ----- A PDF copy of the CFP is attached with this email for forwarding to interested colleagues. It is also available for download from: http://www.cs.stir.ac.uk/~ahu/IEEE-CIM-CICE2015.pdf For more information on the IEEE CIM, see: http://cis.ieee.org/ieee-computational-intelligence-magazine.html -- 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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