- Comp-neuro - lists.cnsorg.org

Second call for abstracts: Integrated Systems Neuroscience Workshop, March 23-24th, Manchester, UK
by Mark Humphries Jan. 20, 2015

Jan. 20, 2015

We announce a 2-day workshop on "Integrated Systems Neuroscience", at the University of Manchester (UK), March 23-24th 2015. Poster abstract submission deadline: 30th January 2015. Registration deadline: February 27th 2015 Systems neuroscience has been thrust centre-stage by the extraordinary advances in technology for recording and manipulating neural circuits at single-cell resolution. Yet with this rapid increase in data yield has come formidable challenges in analysing and understanding experimental results. Consequently, a tight integration between experimental and computational neuroscience approaches is increasingly necessary for tackling these challenges. The goal of this workshop is to present the state-of-the-art in integrated systems and computational approaches to key neural circuits, to demonstrate their power and potential. Each session of the programme will comprise a pair of talks presenting complementary experimental and computational work on the same circuit theme. A poster session with wine reception will be held on the first evening. Speakers include: Rafal Bogacz (Oxford) Matteo Carandini (UCL) Peter Dayan (Gatsby Computational Neuroscience Unit, UCL) Ken Harris (UCL) Peter Magill (MRC Brain Network Dynamics Unit, Oxford) Daniel O'Connor (John Hopkins University) Michael Orger (Champalimaud Institute, Lisbon) Srdjan Ostojic (Ecole Normale Superieure, Paris) Alex Thiele (University of Newcastle) Gasper Tkacik (IST Austria) For more information, including the current programme, please visit the website: http://www.isn2015.ls.manchester.ac.uk/ Posters: We are accepting submission of abstracts for posters. A maximum of 30 posters will be accepted; the top 20 will have their registration fee waived. For details on submission see: http://www.isn2015.ls.manchester.ac.uk/abstracts/ Abstract submission deadline: January 30th 2015 Registration: http://www.isn2015.ls.manchester.ac.uk/registration/ Registration deadline: February 27th 2015 Organisers: Mark Humphries & Rasmus Petersen (University of Manchester) Sponsors: we gratefully acknowledge the support of the Medical Research Council and Company of Biologists. Dr Mark Humphries MRC Senior non-Clinical Research Fellow AV Hill Building Faculty of Life Sciences University of Manchester http://www.systemsneurophysiologylab.ls.manchester.ac.uk/

1 0

Feb 15 Application Deadline - 2015 Summer School on Large-Scale Brain Modelling
by Chris Eliasmith Jan. 19, 2015

Jan. 19, 2015

[All details about this school can be found online at <http://www.nengo.ca/summerschool>http://www.nengo.ca/summerschool] The Centre for Theoretical Neuroscience at the University of Waterloo is inviting applications for our 2nd annual summer school on large-scale brain modelling. This two-week school will teach participants how to use the Nengo simulation package to build state-of-the-art cognitive and neural models. Nengo has been used to build what is currently the world's largest functional brain model, Spaun <http://www.nengo.ca/build-a-brain/spaunvideos>[1], and provides users with a versatile and powerful environment for simulating cognitive and neural systems. We welcome applications from all interested graduate students, research associates, postdocs, professors, and industry professionals. No specific training in the use of modelling software is required, but we encourage applications from active researchers with a relevant background in psychology, neuroscience, cognitive science, engineering, computer science, or a related field. For a look at last year's summer school, please see this short video: http://goo.gl/BXJ3x5 [1] Eliasmith, C., Stewart T. C., Choo X., Bekolay T., DeWolf T., Tang Y., Rasmussen, D. (2012). A large-scale model of the functioning brain. Science. Vol. 338 no. 6111 pp. 1202-1205. DOI: 10.1126/science.1225266. [<http://nengo.ca/publications/spaunsciencepaper>http://nengo.ca/publications/spaunsciencepaper] ***Application Deadline: February 15, 2015*** Format Participants are encouraged to bring their own ideas for projects, which may focus on testing hypotheses, modelling neural or cognitive data, implementing specific behavioural functions with neurons, expanding past models, or provide a proof-of-concept of various neural mechanisms. More generally, participants will have the opportunity to: - build perceptual, motor, and cognitive models with spiking neurons - model anatomical, electrophysiological, cognitive, and behavioural data - use a variety of single cell models within a large-scale model - integrate machine learning methods into biologically oriented models - use Nengo with your favorite simulator, e.g. Brian, NEST, Neuron, etc. - interface Nengo with various kinds of neuromorphic hardware - interface Nengo with cameras and robotic systems - implement modern nonlinear control methods in neural models - and much more... Hands-on tutorials, work on individual or group projects, and talks from invited faculty members will make up the bulk of day-to-day activities. There will be a weekend break on June 13-14, and fun activities scheduled for evenings throughout. A project demonstration event will be held on the last day of the school, with prizes for strong projects! Date and Location: June 7th to June 19th, 2015 at the University of Waterloo, Ontario, Canada. Applications: Please visit <http://www.nengo.ca/summerschool>http://www.nengo.ca/summerschool, where you can find more information regarding costs, travel, lodging, along with an application form listing required materials. If you have any questions about the school or the application process, please contact Peter Blouw (pblouw(a)uwaterloo.ca <mailto:pblouw@uwaterloo.ca>)

1 0

PhD Student in computational neuroscience/pain research - Technische Universität München
by Markus Ploner Jan. 19, 2015

Jan. 19, 2015

PhD Student in computational neuroscience/pain research Department of Neurology, Technische Universität München, Munich, Germany Applications are invited for a PhD Student position at the Department of Neurology, Technische Universität München, to work on the cerebral representation of pain by using EEG. The project will focus on the neurophysiological correlates of pain in healthy human subjects and patients suffering from chronic pain disorders. Major experimental methods include EEG time-frequency analysis, source analysis and connectivity analysis. The candidate will join a research group dedicated to the multimodal investigation of the cerebral representation of pain (http://www.painlabmunich.de <http://www.painlabmunich.de/>) which is part of the TUM-Neuroimaging Center (TUM-NIC; http://www.tumnic.mri.tum.de <http://www.tumnic.mri.tum.de/>). TUM-NIC hosts state-of-the-art neuroimaging facilities and offers training in major neuroimaging techniques. Applicants should have a background in computer science, statistics, physics, engineering, neuroscience, medicine, psychology, or other relevant disciplines. Prior experience in MATLAB programming is mandatory. Skills for sophisticated analysis of EEG data (e.g. information theory, machine learning techniques, mediation analysis) are highly desirable. Candidates have the possibility to integrate in the PhD program Medical Life Science and Technology (http://www.phd.med.tum.de <http://www.phd.med.tum.de/>) or the Graduate School of Systemic Neurosciences (http://www.gsn.uni-muenchen.de/index.html <http://www.gsn.uni-muenchen.de/index.html>), which offer interdisciplinary high-level training for students with different backgrounds. Salary will be commensurate with the German TVöD salary scale (EG13). Applications will be considered until the position is filled. Candidates may contact Dr. Markus Ploner for more detailed information or directly e-mail their application (ploner(a)lrz.tum.de <mailto:ploner@lrz.tum.de>), including letter of motivation, CV and letters of recommendation. Markus Ploner MD Heisenberg Professor of Human Pain Research Department of Neurology Technische Universität München Munich, Germany ploner(a)lrz.tum.de

1 0

postdoctoral position available
by Anil Seth Jan. 19, 2015

Jan. 19, 2015

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

1 0

MCS 2015 : Call for Papers
by Dr. Schwenker Jan. 18, 2015

Jan. 18, 2015

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

1 0

Opportunities for Ph.D. Studentships in Computational Neuroscience
by Wong-Lin, Kongfatt Jan. 18, 2015

Jan. 18, 2015

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

1 0

Journal of Mathematical Neuroscience - recent articles
by Stephen Coombes Jan. 17, 2015

Jan. 17, 2015

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.

1 0

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

Jan. 17, 2015

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

1 0

CoSMo 2015 summer school announcement
by Gunnar Blohm Jan. 16, 2015

Jan. 16, 2015

*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/

1 0

New funding opportunity: Machine Intelligence from Cortical Networks (MICrONS) Program
by R. Jacob Vogelstein Jan. 16, 2015

Jan. 16, 2015

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.

1 0