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Systems and Computational Neuroscience Down Under (SCiNDU)
by Geoffrey Goodhill March 1, 2015

March 1, 2015

SCiNDU: SYSTEMS AND COMPUTATIONAL NEUROSCIENCE DOWN UNDER qbi.uq.edu.au/scindu<http://qbi.uq.edu.au/scindu> December 15-17th, 2015, Queensland Brain Institute, The University of Queensland, Brisbane, Australia This conference will bring together international leaders in understanding the computational principles underlying how neural circuits decode sensory information, make decisions, and learn from experience. Abstract submissions for poster presentation are welcome. Deadline for abstracts and early registration is Oct 16th 2015. Some abstracts will be selected for short talks. Confirmed invited speakers include: Ehsan Arabzadeh (ANU) Mark Bear (MIT) Michael Breakspear (QIMR) Allen Cheung (QBI) Yang Dan (UC Berkeley) Peter Dayan (UCL) Geoffrey Goodhill (QBI) Zach Mainen (Champalimaud) Jason Mattingley (QBI) Linda Richards (QBI) Peter Robinson (Sydney) Marcello Rosa (Monash) Pankaj Sah (QBI) Mandyam Srinivasan (QBI) Greg Stuart (ANU) Stephen Williams (QBI) Li Zhaoping (UCL) On December 15th the conference will be preceded by tutorials including: Mark Bear: Experience-dependent synaptic plasticity Peter Dayan: Neural reinforcement learning Jason Mattingley: Brain stimulation, attention and plasticity Li Zhaoping: Vision, efficient coding and salience We gratefully acknowledge financial support from the Queensland Brain Institute and the Australian Research Council Centre of Excellence for Integrative Brain Function. Professor Geoffrey J Goodhill Queensland Brain Institute and School of Mathematics & Physics University of Queensland St Lucia, QLD 4072, Australia Email: g.goodhill(a)uq.edu.au<mailto:g.goodhill@uq.edu.au> http://www.qbi.uq.edu.au/professor-geoffrey-goodhill

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Reminder: MBL Methods in Computational Neuroscience Course 2015: applications due March 5
by Mark Goldman Feb. 28, 2015

Feb. 28, 2015

Applications are open for the Methods in Computational Neuroscience course at the Marine Biology Laboratory in Woods Hole, MA. The course will run from July 29 to August 26, 2014, and the online application form can be found at: http://ws2.mbl.edu/studentapp/studentapp.asp?CourseID=MCN. The course application deadline is March 5. The course covers a range of topics in computational neuroscience including neuronal biophysics, neural coding & information processing, circuit dynamics, learning & memory, motor control, and cognitive processing & disease. In addition, numerous tutorials and problem sets will cover a broad range of computational and mathematical modeling methods. The course strongly emphasizes the collaboration between theory and experiment in solving neuroscience problems, and lectures will be given by a mixture of theorists and experimentalists. The final weeks of the course are primarily reserved for development and work on projects that students design in collaboration with the resident faculty. Further information can be found on the MCN website: http://www.mbl.edu/mcn/ 2015 Course Directors: Michale Fee, MIT Mark Goldman, UC Davis 2015 Confirmed Faculty: Larry Abbott, Columbia University Steve Baccus, Stanford University Jeff Beck, Duke University William Bialek, Princeton University Dmitri Chklovskii, HHMI Janelia Farm Peter Dayan, University College London Bard Ermentrout, University of Pittsburgh Adrienne Fairhall, University of Washington Ila Fiete, UT Austin Loren Frank, UCSF Michael Frank, Brown University Surya Ganguli, Stanford University John Huguenard, Stanford University David Kleinfeld, UC San Diego Nancy Kopell, Boston University John Lisman, Brandeis University Eve Marder, Brandeis University Bartlett Mel, University of Southern California Jonathan Pillow, Princeton University Maneesh Sahani, University College London Terry Sejnowski, Salk Institute Michael Shadlen, Columbia University Josh Shaevitz, Princeton University Sara Solla, Northwestern University Haim Sompolinsky, Hebrew University David Tank, Princeton University Josh Tenenbaum, MIT Srini Turaga, Janelia Farm Xiao-Jing Wang, NYU Daniel Wolpert, Cambridge University Ryohei Yasuda, Duke University

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a neural architecture for autonomous spatial and object attention, invariant object category learning and recognition, and Where’s Waldo search with freely moving eyes
by Stephen Grossberg Feb. 27, 2015

Feb. 27, 2015

The following series of articles progressively develop a neural architecture for autonomous spatial and object attention, invariant object category learning and recognition, and Where’s Waldo search with freely moving eyes: ARTSCAN View-invariant object category learning and recognition with freely moving eyes in a 2D scene Fazl, A., Grossberg, S., and Mingolla, E. (2009). View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds. Cognitive Psychology, 58, 1-48. http://cns.bu.edu/~steve/FazGroMin2008.pdf pARTSCAN View-, position-, and size-invariant object category learning and recognition with freely moving eyes in a 2D scene Cao, Y., Grossberg, S., and Markowitz, J. (2011). How does the brain rapidly learn and reorganize view- and positionally-invariant object representations in inferior temporal cortex? Neural Networks, 24, 1050-1061. http://cns.bu.edu/~steve/NN2853.pdf dARTSCAN ARTSCAN with transient and sustained distributed attention and prefrontal priming in a 2D scene Foley, N.C., Grossberg, S. and Mingolla, E. (2012). Neural dynamics of object-based multifocal visual spatial attention and priming: Object cueing, useful-field-of-view, and crowding. Cognitive Psychology, 65, 77-117. http://cns.bu.edu/~steve/FolGroMin2012.pdf ARTSCAN SEARCH Solution of Where’s Waldo Problem: finding a valued object in a 2D scene Chang, H.-C., Grossberg, S., and Cao, Y. (2014) Where's Waldo? How perceptual cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene. Frontiers in Integrative Neuroscience, doi: 10.3389/fnint.2014.0043. http://journal.frontiersin.org/Journal/10.3389/fnint.2014.00043/full 3D ARTSCAN ARTSCAN of a 3D scene whose 3D perceptual representations remain stable as the eyes move Grossberg, S., Srinivasan, K., and Yazdanbakhsh, A. (2014). Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements. Frontiers in Psychology: Perception Science, doi: 10.3389/fpsyg.2014.01457. http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.01457/full Stephen Grossberg Wang Professor of Cognitive and Neural Systems Professor of Mathematics, Psychology, and Biomedical Engineering Director, Center for Adaptive Systems http://www.cns.bu.edu/about/cas.html http://cns.bu.edu/~steve steve(a)bu.edu

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ECMLPKDD 2015 : Call for Papers, Tutorials and Workshops
by ECMLPKDD 2015 Feb. 27, 2015

Feb. 27, 2015

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) will take place in Porto, Portugal, from September 7th to 11th, 2015 (http://www.ecmlpkdd2015.org) This event is the leading European scientific event on machine learning and data mining and builds upon a very successful series of 25 ECML and 18 PKDD conferences, which have been jointly organized for the past 14 years. ECMLPKDD 2015 will host three tracks, tutorials and a set of workshops. Therefore, we invite all researchers and practitioners from different communities to submit papers and/or present tutorial and workshop proposals. ************************* CALL FOR PAPERS ************************* JOURNAL TRACK ********************* Articles for this track are submitted all year long directly to either Machine Learning or Data Mining and Knowledge Discovery, and are reviewed like regular journal articles. Accepted articles appear in full in the journal and the authors are given a presentation slot at the conference. Articles deemed insufficiently mature for journal publication may be accepted for inclusion in the proceedings. Submissions to the journal track will be managed by the Guest Editorial Board. Paper Submission: Cut-off dates for the bi-weekly batches are 18 Jan, 1 Feb, 15 Fev, 1 Mar, 15 Mar, 29 Mar, 12 Apr, 26 Apr of 2015 Web Page: http://www.ecmlpkdd2015.org/submission/journal-track RESEARCH PROCEEDINGS TRACK ******************************************* The research proceedings track, which is organized in the traditional way. Accepted papers will be published in the Lecture Notes in Artificial Intelligence (LNCS/LNAI) of Springer, after reviewing by the programme committee. Abstract Submission Deadline: March 26, 2015 Paper Submission Deadline: April 2, 2015 Paper Acceptance Notification: June 1, 2015 Paper Camera Ready Submission: June 15, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/research-proceedings-track INDUSTRIAL, GOVERNMENTAL & NON-GOVERNMENTAL PROCEEDINGS TRACK **************************************************************************** ************************** The NEW industrial, governmental & non-governmental (NGO) proceedings track is independent and distinct from the Research Track. Submissions to this track should solve real-world problems and focus on engineering systems, applications, and challenges. Accepted papers will be published in the Lecture Notes in Artificial Intelligence (LNCS/LNAI) of Springer, after reviewing by the programme committee. Abstract Submission Deadline: March 26, 2015 Paper Submission Deadline: April 2, 2015 Paper Acceptance Notification: June 1, 2015 Paper Camera Ready Submission: June 15, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/industrial-proceedings-track ***************************************************************** CALL FOR TUTORIAL AND WORKSHOP PROPOSALS ***************************************************************** TUTORIALS ************** The tutorials are intended to provide a comprehensive introduction to established or emerging research topics of interest for the machine learning and the data mining community. These topics include related research fields or applications. The ideal tutorial should attract a wide audience. It should be broad enough to provide a basic introduction to the chosen research area, but it should also cover the most important topics in depth. We welcome half day workshop proposals. Proposal Deadline: March 2, 2015 Proposal Acceptance Notification: March 23, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/call-for-tutorials WORKSHOPS **************** The workshops will be on relevant and current topics in Machine Learning and Data Mining. The scope of the proposal should be consistent with the conference themes as described in the ECMLPKDD 2015 Call for Papers (http://www.ecmlpkdd2015.org/submission) Interdisciplinary workshops that bring together researchers and practitioners from different communities are especially welcome. We encourage workshops that bridge the gap between theoretical advances and important and/or innovative applications of machine learning and data mining. We welcome both full and half day workshop proposals. Proposal Deadline: March 2, 2015 Proposal Acceptance Notification: March 23, 2015 Workshop Websites and Call for Papers Online: March 27, 2015 Workshop Proceedings (Camera-ready): August 3, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/call-for-workshop-proposals Hope to see you all soon in Porto, Portugal!!! The publicity chairs of the ECMLPKDD 2015, Carlos Abreu Ferreira Ricardo Campos --- Este e-mail foi verificado em termos de vírus pelo software antivírus Avast. http://www.avast.com

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Postdoctoral position in Computational Neuroscience at IUPUI
by Yaroslav Molkov Feb. 27, 2015

Feb. 27, 2015

A postdoctoral/research associate position in computational neuroscience is available immediately in the NIH funded project focusing on multiscale computational modeling of interactions between respiratory central pattern generator and sympathetic nervous system as it relates to neurogenic hypertension. This collaborative project is performed by the groups of Yaroslav Molkov (Dept. of Mathematical Science, Indiana University – Purdue University, Indianapolis, IN, USA), Ana Abdala (University of Bristol, UK) and Daniel Zoccal (Sao Paulo State University, Brazil). Excessive sympathetic activity plays a crucial role in the development and maintenance of hypertension. Such autonomic dysfunction is observed in hypertensive patients with obstructive sleep apnea (OSA). Chronic exposure to intermittent hypoxia (CIH) that occurs in OSA is the main factor leading to sympathetic overactivity and hypertension. A CIH-driven increase in sympathetic output is largely dependent on the emergence of active expiratory pattern. The respiratory central pattern generator (rCPG) is composed of two interacting oscillators. The first occupies Bötzinger/pre-Bötzinger complexes and generates self-sustained respiratory rhythm controlling the diaphragm to provide inspiration. The second oscillator, the parafacial respiratory group (pFRG), resides in the retrotrapezoid nucleus (RTN). The RTN/pFRG oscillations emerge in hypoxic and hypercapnic conditions, and drive motor output to abdominal muscles for active (forced) expiration. Interactions of these respiratory circuits with the sympathetic neurons of the rostral and caudal ventrolateral medulla evoke respiratory-related oscillations in sympathetic efferent drive. Exposure to CIH leads to alterations in excitability of RTN/pFRG neuronal population and/or modifications in synaptic connections between respiratory oscillators and sympathetic neurons which ultimately results in the elevated baseline sympathetic activity and arterial pressure. This interdisciplinary project aims to reveal the mechanisms that couple breathing and control of blood pressure in the brain in health and disease, and for the first time translate them into a realistic computational model. Such a model will have the unprecedented potential for generating effective non-pharmacological means of controlling blood pressure and breathing via implantable biofeedback devices (e.g. vagus nerve stimulation) and non-invasive devices for guided control of autonomic function (e.g. device-guided paced respiration). Furthermore, it will provide a robust scientific substrate for evaluating the usefulness and safety of alternative medicine interventions, such as controlled breathing practices, for lowering blood pressure and improving heart rate variability. Applicants must have a PhD degree in Neuroscience, Applied Mathematics, Physics, or a related discipline and have excellent programming skills (C++, Matlab, UNIX). Previous experience in computational neuronal modeling is a plus. Interested candidates are encouraged to contact Dr. Molkov ( ymolkov(a)iupui.edu) by email. To apply, please send an email along with (1) curriculum vitae; (2) a cover letter describing previous research experience and interests; (3) the names and contact information of three references. Application processing will begin immediately and the search will remain open until the position is filled. Best Regards, Yaroslav Molkov, Ph.D. Assistant Professor Indiana University - Purdue University Indianapolis Department of Mathematical Sciences 402 N. Blackford St, LD 270, Indianapolis, IN 46202 Office: LD 224U Phone: (317) 274-6934 Fax: (317) 274-3460 Email: ymolkov(a)iupui.edu

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Postdoctoral positions available in Sophie Deneve's team, Group for Neural Theory, Ecole Normale supérieure, Paris
by Sophie Deneve Feb. 26, 2015

Feb. 26, 2015

Two postdoctoral positions are available in the group of Sophie Deneve, Ecole Normale supérieure Paris. Positions are for one year, renewable one year, and comes with competitive salary and generous equipment and travel funds. The team is part of the “Group for neural theory” ( http://iec-lnc.ens.fr/group-for-neural-theory/) and surrounded by top-level theoretical and experimental labs. Candidates are expected to have a solid training in the field of computational neuroscience, proven quantitative and programming skills and a strong interest in integrative/cognitive neuroscience. Starting dates are flexible, but should preferentially occur before September 2015. Possible projects include: Starting from the hypothesis that biological networks are optimally tuned to maximize coding efficiency and robustness, we will design new tools to analyze their dynamics and function. We will identify functionally relevant, low-dimensional structures in large neural datasets (spontaneous and stimulus-driven multi-unit activity in retina, visual cortex, and/or in Zebrafish whole brain recordings). Sensory neurons are often described in terms by their receptive fields. On the other hand, “generative models” (and more generally, efficient coding) assume that neurons collectively try to predict their stimuli. In such scenarios, prior assumptions, metabolic constrains and context deeply reshape neural selectivity. We will consider implications for learning, attentional modulation and/or hierarchical neural processing. Candidates should send their application to sophie.deneve(a)ens.fr by *March 31, 2015*. Please mention “postdoctoral candidate” in the subject of the email. Application should take the form of a single PDF file, containing a CV, motivation letter, names and email addresses of 2 to 3 referees. No official letter of recommendations are required, as the referee will be contacted directly. Best wishes, -- Dr Sophie Deneve Group for Neural Theory Laboratoire de Neurosciences cognitives ENS-INSERM 29, rue d'Ulm, 75005 Paris, France Tel. (+33) (0)1 44 32 26 35

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New Paper on Network Dynamics of the Human Brain
by Xerxes D. Arsiwalla Feb. 26, 2015

Feb. 26, 2015

Dear Colleagues, For your interest, our paper on dynamics of the human connectome is now available open access. http://journal.frontiersin.org/article/10.3389/fninf.2015.00002/abstract Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction Arsiwalla XD, Zucca R, Betella A, Martinez E, Dalmazzo D, Omedas P, Deco G and Verschure PFMJ (2015) Front. Neuroinform. 9:02. doi: 10.3389/fninf.2015.00002 Abstract BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

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Workshop announcement: HBP Hippocamp CA1
by Szabolcs Káli Feb. 26, 2015

Feb. 26, 2015

Dear Colleagues, Registration is now open for the workshop "HBP Hippocamp CA1: Collaborative and Integrative Modeling of Hippocampal Area CA1", to be held March 31st - April 1st, 2015 at UCL School of Pharmacy in London, supported by the Human Brain Project (www.humanbrainproject.eu) In short, the aims of the workshop are two-fold. First, to engage the larger community of experimentalists and modelers working on hippocampus, and highlight existing modeling efforts and strategic datasets for modeling hippocampal area CA1. Second, to define and bootstrap an inclusive community-driven model and data-integration process to achieve open pre-competitive reference models of area CA1 (and, ultimately, the rest of the hippocampus), which are well documented, validated, and released at regular intervals (supported in part by IT infrastructure funded by HBP). Involvement from the community interested in characterization and modeling of the hippocampus is highly encouraged. To keep the meeting focused on the task, participation will be limited to ~30 people, so registration is required. Please consult the meeting website at http://neuralensemble.org/meetings/HippocampCA1/ for registration and further details. Organizing committee: Jo Falck (UCL), Szabolcs Káli (Hungarian Academy of Sciences), Sigrun Lange (UCL), Audrey Mercer (UCL), Eilif Muller (EPFL), Armando Romani (EPFL) and Alex Thomson (UCL). Best regards, Szabolcs Káli Laboratory of Cerebral Cortex Research Institute of Experimental Medicine Hungarian Academy of Sciences H-1083 Budapest Szigony utca 43. Phone: +36-1-2109413 Fax: +36-1-2109412 E-mail: kali.szabolcs(a)koki.mta.hu www.koki.mta.hu

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Tenth Advanced Statistics and Data Mining Summer School (Madrid, June 29th - July 10th, 2015)
by asdm＠fi.upm.es Feb. 26, 2015

Feb. 26, 2015

Dear colleagues, The Technical University of Madrid (UPM) will once more organize the 'Advanced Statistics and Data Mining' summer school. The summer school will be held in Boadilla del Monte, near Madrid, from June 29th to July 10th. This year's edition comprises 12 week-long courses (15 lecture hours each), given during two weeks (six courses each week). Attendees may register in each course independently. No restrictions, besides those imposed by timetables, apply on the number or choice of courses. Early registration is now *OPEN*. Extended information on course programmes, price, venue, accommodation and transport is available at the school's website: http://www.dia.fi.upm.es/ASDM Please, forward this information to your colleagues, students, and whoever you think may find it interesting. Best regards, Pedro Larranaga, Concha Bielza, Bojan Mihaljevic and Laura Anton-Sanchez. -- School coordinators. *** List of courses and brief description *** * Week 1 (June 29th - July 3rd, 2015) * 1st session: 9:45-12:45 Course 1: Bayesian Networks (15 h) Basics of Bayesian networks. Inference in Bayesian networks. Learning Bayesian networks from data. Real applications. Practical demonstration: GeNIe, Weka, Bayesia, R. Course 2: Time Series(15 h) Basic concepts in time series. Descriptive methods for time series. Linear models for time series. Extensions. Practical demonstration: R. 2nd session: 13:45-16:45 Course 3: Supervised Pattern Recognition (15 h) Introduction. Assessing the performance of supervised classification algorithms. Preprocessing. Classification techniques. Combining multiple classifiers. Comparing supervised classification algorithms. Course 4: Bayesian Inference (15 h) Introduction: Bayesian basics. Conjugate models. MCMC and other simulation methods. Regression and Hierarchical models. Model selection. Practical demonstration: R and WinBugs. 3rd session: 17:00 - 20:00 Course 5: Neural Networks and Deep Learning (15 h) Introduction. Training algorithms. Learning and Optimization. MLPs in practice. Deep Networks. Course 6: Unsupervised Pattern Recognition (15 h) Introduction to clustering. Data exploration and preparation. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Conclusions and final advise. Practical session: R. * Week 2 (July 6th - July 10th, 2015) * 1st session: 9:45-12:45 Course 7: Statistical Inference(15 h) Introduction. Some basic statistical test. Multiple testing. Introduction to bootstrap methods. Introduction to Robust Statistics. Practical demonstration: R. Course 8: Bayesian Classifiers (15 h) Discrete predictors. Gaussian Bayesian networks-based classifiers. Other Bayesian classifiers. Bayesian classifiers for: positive and unlabeled data, semi-supervised learning, data streams, temporal data. Practical session: R. 2nd session: 13:45-16:45 Course 9: Text Mining (15 h) Introduction. Fundamentals. Language Modeling. String Processing. Text Classification. Information Extraction. Practical session: GATE (optionally, python). Course 10: Feature Subset Selection (15 h) Introduction. Filter approaches. Embedded methods. Wrapper methods. Advanced topics. Practical session: R and Weka. 3rd session: 17:00-20:00 Course 11: Support Vector Machines and Convex Optimization (15 h) Introduction. SVM models. SVM learning algorithms. Convex non differentiable optimization. Course 12: Hidden Markov Models (15 h) Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models. Semicontinuous Hidden Markov Models. Continuous Hidden Markov Models. Unit selection and clustering. Speaker and Environment Adaptation for HMMs. Other applications of HMMs. Practical session: HTK.

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Call for Papers ( Frontiers in Computational Neuroscience): Special Issue on Neuropsychology through the lenses of Computational Modelling
by Eirini Mavritsaki Feb. 26, 2015

Feb. 26, 2015

Call for Papers Special Issue in Frontiers in Computational Neuroscience 'Neuropsychology through the lenses of Computational Modelling' Extended Deadline for Abstract Submission 16th of March Computational modelling may provide an increasingly important tool for furthering neuropsychology and understanding brain impairments – but there remain many issues: how can different types of lesion be best modelled? What are the differences between damage to grey matter and to fibre tracts? What the effects of learning within damaged systems or systems where there is an imbalance in neurotransmitters? For this reason a special issue that brings together state-of-the-art papers on the computational modelling of neuropsychological disorders, with papers covering different levels of modelling (from spiking neurons to higher-level connectionist modelling), using imaging as well as behavioural data, and addressing a number of different disorders (attention, language, memory etc.) is expended to contribute significantly to the field of Neuropsychology and Computational Neuroscience. In collaboration with Frontiers in Computational Neuroscience, we are organizing a Research Topic titled "Neuropsychology through the lenses of computational modelling”, hosted by Eirini Mavritsaki, Glyn Humphreys. As host editor, I would like to encourage you to contribute to this topic. Frontiers, a Swiss open-access publisher, recently partnered with Nature Publishing Group to expand its researcher-driven Open Science platform. Frontiers articles are rigorously peer-reviewed, can be disseminated freely and are widely read by your colleagues and by the broader scientific and medical research communities. The idea behind a research topic is to create an organized, comprehensive collection of several contributions, as well as a forum for discussion and debate. Contributions can be articles describing original research, methods, hypothesis & theory, opinions, etc. We have created a homepage on the Frontiers website (Frontiers in Computational Neuroscience) where all articles will appear after peer-review and where participants in the topic will be able to hold relevant discussions: http://www.frontiersin.org/Computational_Neuroscience/researchtopics/Neurop…. Frontiers will also compile an e-book, as soon as all contributing articles are published, that can be used in classes, be sent to foundations that fund your research, to journalists and press agencies, or to any number of other organizations. As such, a manuscript accepted for publication incurs a publishing fee, which varies depending on the article type. Research Topic manuscripts receive a significant discount on publishing fees. Please take a look at this fee table: http://www.frontiersin.org/about/PublishingFees. Once published, your articles will remain free to access for all readers, and will be indexed in PubMed and other academic archives. As an author in Frontiers, you retain the copyright to your own papers and figures. I would be delighted if you considered participating in this Research Topic. Should you choose to participate, please confirm by sending me a quick email and then your abstract no later than Feb 16, 2015 using the following link: http://www.frontiersin.org/Computational_Neuroscience/researchtopics/Neurop… Please note that the deadline for manuscript submission is on: Jun 30, 2015 Since I am using the Frontiers system to manage this topic, I would really appreciate if you could also please indicate your decision by clicking on one of the links below. <<Variable for accept/decline links – do not edit!>> With best regards, Eirini Mavritsaki Guest Associate Editor, Frontiers in Computational Neuroscience www.frontiersin.org ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Eirini Mavritsaki, Ph.D., CPsychol Co-Director of the Centre for Applied Psychological Research (CAP Research) Business Development Coordinator for Social Sciences Senior Lecturer in Cognitive Psychology Faculty of Business Law and Social Sciences Birmingham City University D 3.17 Dawson Building City North Campus Perry Bar, Birmingham B42 2SU eirini.mavritsaki(a)bcu.ac.uk<mailto:eirini.mavritsaki@bcu.ac.uk> 0121 331 6361 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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