October 2021 - Comp-neuro - lists.cnsorg.org

Canadian Symposium for Computational Neuroscience
by Wilten Nicola 12 Oct '21

12 Oct '21

*Campus Alberta Neuroscience is pleased to announce the first ever Canadian Symposium for Computational Neuroscience, being held October 26-27 2021. This national symposium will be held virtually and will connect trainees, researchers, and industry representatives from across the country to share knowledge, research, and innovation. The major themes of the symposium will be: Biological Computation, Network Neuroscience, Dimensionality Reduction, and Information Decoding from Neural Networks. These topics will be supplemented with a workshop on machine learning provided by the Alberta Machine Intelligence Institute and a keynote from Geoffrey Hinton. You can find more information here: https://www.albertaneuro.ca/education/#CSCN <https://www.albertaneuro.ca/education/#CSCN>*

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Post-doctoral Research Associate in Computational Neuroscience (Computational Modelling of Decision Making)
by Wong-Lin, Kongfatt 12 Oct '21

12 Oct '21

Applications are invited for a US/NIH-Ireland funded Post-doctoral Research Associate post at the Intelligent Systems Research Centre in Ulster University, UK. The successful candidate will develop and apply advanced computational modelling techniques to understand brain and behavioural data across primate species, and to apply biologically based neural network modelling to elucidate the mechanisms underlying various forms of decision making. The duration of the post is 27 months, with possible extension. The personnel will work with Dr. KongFatt Wong-Lin at Ulster University, while collaborating closely with international collaborators in the USA and the Republic of Ireland, namely, Prof. Michael Shadlen at Columbia University (USA), Prof. Stephan Bickel at Northwell-Hofstra School of Medicine (USA), Prof. Redmond O'Connell at Trinity College Dublin (Ireland), and Prof. Simon Kelly at University College Dublin (Ireland). All applicants should hold a degree in in Computational Neuroscience, Computational Biology, Neuroscience, Computing, Engineering, Mathematics, Data Science, Physical Sciences, Biology, or a cognate area. Apply online: https://www.jobs.ac.uk/job/CJU115/research-associate-in-computational-neuro… Closing date for receipt of completed applications: 24th October 2021. Job Ref: 008566. If you have questions regarding this post, please contact: Dr. KongFatt Wong-Lin, email: k.wong-lin(a)ulster.ac.uk . ------------------- Dr. KongFatt Wong-Lin Reader Intelligent Systems Research Centre School of Computing, Engineering and Intelligent Systems Ulster University, UK https://www.ulster.ac.uk/staff/k-wong-lin 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 Ulster University 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

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Postdoctoral Positions at UT Austin
by Franco Pestilli 11 Oct '21

11 Oct '21

Dear Colleagues, I would appreciate sharing the announcement below. Postdoctoral positions are available in the Laboratory of Dr. Franco Pestilli <https://liberalarts.utexas.edu/psychology/faculty/fp4834> in the Department of Psychology <https://liberalarts.utexas.edu/psychology/>, Center for Perceptual Systems <https://liberalarts.utexas.edu/cps/> and Center for Theoretical and Computational Neuroscience <https://ctcn.utexas.edu/> at the University of Texas at Austin <https://www.utexas.edu/>. The positions are funded by NIH and NSF awards and associated with projects integrating the Brain Imaging Data Structure <https://bids.neuroimaging.io/>, brainlife.io, OpenNeuro.org with EEG, MEG and MRI data. The projects are in collaboration with: - Russ Poldrack, Stanford University <https://profiles.stanford.edu/russell-poldrack> - Ted Satterthwaite, University of Pennsylvania <https://www.med.upenn.edu/bbl/faculty-tsatterthwaithe.html> - Ariel Rokem, The University of Washington, Seattle <https://escience.washington.edu/people/ariel-rokem/> - Oscar Esteban, University of Lausanne <https://wp.unil.ch/connectomics/team/oscar-esteban/> - Cyril Pernet, Copenhagen University Hospital <https://cpernet.github.io/> - Robert Smith, Florey Institute of Neuroscience <https://florey.edu.au/science-research/scientist-directory/dr-robert-smith> - Eugene Duff, Imperial College London <https://www.imperial.ac.uk/people/e.duff> - Aina Puce, Indiana University <https://psych.indiana.edu/directory/faculty/puce-aina.html> - Nathalie George, Sorbonne Université <https://institutducerveau-icm.org/fr/george-nathalie/> - Guiomar Niso, Universidad Politécnica de Madrid <https://guiomarniso.com/> A few words describing the projects: - *The Brain Imaging Data Structure for Connectivity.* Expanding the usability of the Brain Imaging Data Structure standard by developing the formats necessary to describe advanced data derivatives related to brain connections (diffusion imaging, tractography, tract-profiles, tractometry, functional networks, and network models). - *Integrating EEG and MEG data-processing Apps on brainlife.io <http://brainlife.io>. *Develop powerful web services to Integrate EEG and MEG data on brainlife.io so to allow standardization, reutilization and automated visualization of data. - *Deep and Shallow Machine-Learning for neuroimaging data analysis and augmentation using brainlife.io <http://brainlife.io>. *Work on projects related to the application of Machine-Learning algorithms to validate data and process open datasets from OpenNeuro.org using *software-as-service* reproducible processing pipelines on brainlife.io For more information please email: pestilli(a)utexas.edu Best regards, *FRANCO PESTILLI*, *Ph.D.* | *Associate Professor* Department of Psychology <https://liberalarts.utexas.edu/psychology/index.php> | College of Liberal Arts <https://liberalarts.utexas.edu/> | The University of Texas at Austin <https://www.utexas.edu/> he/him | pestilli(a)utexas.edu | web <https://liberalarts.utexas.edu/psychology/faculty/fp4834> | GitHub <https://github.com/francopestilli> | brainlife.io

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New Approaches to 3D Vision, 1st-4th Nov 2021, Royal Society online meeting
by Linton, Paul 11 Oct '21

11 Oct '21

New Approaches to 3D Vision, 1st-4th November 2021, hosted by the Royal Society This Royal Society online meeting brings together researchers from computer vision, animal vision, and human vision to explore recent developments in 3D vision. Speakers reflect both academia and industry, with representatives from DeepMind, Facebook Reality Labs, Google, and Microsoft Research. Date/Time: 1st-4th November 2021, 3-6pm (3-6:30pm on the 4th), UK Time Please note that the USA/UK time difference is 1hr less than usual Website: https://royalsociety.org/science-events-and-lectures/2021/11/3d-vision/ Registration (Free): https://www.eventbrite.co.uk/e/new-approaches-to-3d-vision-registration-165… DAY ONE (1st Nov) - Seeing Beyond SLAM Chair: Andrew Fitzgibbon (Microsoft) Session One: Neural Scene Representation SM Ali Eslami (DeepMind): “Neural priors, neural encoders and neural renderers” Ida Momennejad (Microsoft Research): “Multi-scale predictive representations and human-like RL” Session Two: Perception-Action Loop Sergey Levine (UC Berkeley and Google): TBC Andrew Glennerster (University of Reading): “Understanding 3D vision as a policy network” DAY TWO (2nd Nov) - Animals in Action Chair: Matteo Carandini (University College London) Session One: Locating Prey and Rewards Jenny Read (Newcastle University): “Stupid stereoscopic algorithms that still work” Aman Saleem (University College London): “Visual processing in the brain during navigation” Session Two: Navigation in 3D Space Kate Jeffery (University College London): “The cognitive map of 3D space: not as metric as we thought?” Gily Ginosar (Weizmann Institute of Science): “Locally ordered representation of 3D space in the entorhinal cortex” DAY THREE (3rd Nov) - Experiencing Space Chair: Mar Gonzalez-Franco (Microsoft Research) Session One: Theories of Visual Space Dhanraj Vishwanath (University of St Andrews): “Tripartite encoding of visual 3D space” Paul Linton (City, University of London): “New approaches to visual scale and visual shape” Session Two: Challenges for Virtual Reality Sarah Creem-Regehr (University of Utah): “Perception and Action in Virtual and Augmented Reality” Douglas Lanman (Facebook Reality Labs): TBC DAY FOUR (4th Nov) - Grasping the World Chair: Jody Culham (Western University) Session One: One Visual Stream or Two? Fulvio Domini (Brown University): “A novel non-probabilistic model of 3D cue integration explains both perception and action” Irene Sperandio (University of Trento): “Dissociations between perception and action in size-distance scaling” Session Two: 3D Space and Visual Impairment Ione Fine (University of Washington): “Do you hear what I see? How do early blind individuals experience object motion?” Ewa Niechwiej-Szwedo (University of Waterloo): “The role of binocular vision in the development of visuomotor control and performance of fine motor skills” Session Three: Panel Discussion Andrew Fitzgibbon (Microsoft), Matteo Carandini (University College London), Mar Gonzalez-Franco (Microsoft Research), and Jody Culham (Western University), share their thoughts on future directions for 3D vision. Organisers: Michael Morgan FRS (City, University of London), Paul Linton (City, University of London), Jenny Read (Newcastle University), Dhanraj Vishwanath (University of St Andrews), Sarah Creem-Regehr (University of Utah), Fulvio Domini (Brown University)

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Postdoctoral Fellow/Research Associate Position in Computational Neuroscience at Drexel University College of Medicine
by Ilya Rybak 08 Oct '21

08 Oct '21

Postdoctoral Fellow/Research Associate Position in Computational Neuroscience at Drexel University College of Medicine Two Postdoctoral Fellow/Research Associate positions are available in the Computational Neuroscience Center (https://drexel.edu/medicine/about/departments/neurobiology-anatomy/research…) and the lab for Theoretical and Computational Neuroscience (https://drexel.edu/medicine/about/departments/neurobiology-anatomy/research…) at the Department of Neurobiology and Anatomy, Drexel University (https://drexel.edu/medicine/about/departments/neurobiology-anatomy/) The qualified postdoc/research associate will join a team of computational neuroscientists focusing on studying and modeling different aspects of neural control of locomotion, including the organization and operation of spinal and supraspinal/brainstem neural circuits and their interactions with animal biomechanics during locomotion and steering. The modeling studies are performed in close, interactive collaboration with several leading laboratories in the USA, Canada, and France focusing on complementary experimental studies. The projects are supported by several grants from NIH and NSF. The studies involve the development of realistic computer models of neural circuits in the mammalian spinal cord and brainstem involved in control of locomotion and steering. The models will be integrated with biomechanical models of animals (mouse, rat, cat) and used (tested) for the control of quadrupedal robots imitating animal locomotion in open space. The successful candidate will closely collaborate with other computational and experimental neuroscientists: they will use experimental data to implement and refine the model and use the model to derive predictions that will then be tested experimentally by our collaborators. Essential Functions: * Work with existing and develop new computational models of spinal locomotor circuits * Integrate the neural and biomechanical models to simulate locomotor behavior * Use numerical optimization to optimize the integrated neuromechanical models * Use the models to derive experimentally testable predictions * Closely collaborate with experimental neuroscientists * Analyze kinematic and electrophysiological data * Write and submit research manuscripts * Present novel findings at national and international conferences Required Qualification include: * PhD in computer science, computational neuroscience, biomedical engineering, or a related field * Excellent programming skills (e.g., Python, C++) * Good written and verbal skills in English Previous experience in either (a) modeling of biological neurons and neural networks (spiking and/or rate based), and/or (b) modeling of biomechanics, and/or (c) machine learning is desirable. The qualified candidate will benefit from joining a well-funded research group that works in a dynamic, collaborative, and interdisciplinary environment within the Department of Neurobiology and Anatomy at Drexel University College of Medicine in Philadelphia, PA. The Department provides an outstanding scientific environment for multidisciplinary training. Interactions and collaborations between labs and centers and between other departments are encouraged. Each position will be initially for one year with possible extension to 3-4 years. Salary will be commensurate with experience. To apply, please email a current CV and contact information for 3 references to Dr. Ilya Rybak (rybak(a)drexel.edu<mailto:rybak@drexel.edu>) The positions will remain posted until filled. ================================= Ilya A. Rybak, Ph.D. Professor, Department of Neurobiology and Anatomy Drexel University College of Medicine 2900 Queen Lane Philadelphia, PA 19129, USA http://rybak-et-al.net

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Postdoc position in computational systems neuroscience, Voigts Lab at Janelia
by Jakob Voigts 07 Oct '21

07 Oct '21

The Voigts Lab at HHMI Janelia Research Campus is recruiting postdocs. We'll work on how neocortex builds and uses models of the world to give animals complex cognitive abilities. The lab will specifically attempt to go beyond repeated stimulus-response associations, instead using complex, naturalistic spatial behaviors to study flexible cognition. We'll be able to throw significant technology at the problem of combining sufficient measurements with complex naturalistic behavior, but more importantly we'll have to overcome the challenge of modeling, analyzing, and ultimately learning from data when there are few or no trials to average over. We will use experimental approaches, such as designing tasks where mice make decisions based on incomplete knowledge of their environment, as well as developing new computational techniques to test circuit mechanisms in the context of unconstrained behaviours. We're part of the new Mechanistic Cognitive Neuroscience research area at Janelia, where there are unique resources for this type of work. We'll work closely with experimental, theoretical, methods, and machine learning researchers in and outside of Janelia to make this research program possible, collaborative, and fun. To apply, please send a cover letter, brief statement of research interests, and CV to jvoigts(a)mit.edu. Lab website: https://www.voigtslab.org/hiring Twitter ad: https://twitter.com/jvoigts/status/1443196881958363143

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Postdoc in Machine learning applied to brain activity data
by Erik Fransén 07 Oct '21

07 Oct '21

Postdoc in Machine learning applied to brain activity data We are seeking a candidate with a PhD in one of the following areas Machine Learning, Computational Neuroscience, Computer Science or Physics. The project will entail analysis of neural data. We are currently analyzing data from Parkinson’s patients (eye-tracking, MEG) and extracting features to be used for disease diagnostics and prediction. The candidate will work in close collaboration with other postdocs and PIs in the consortium. dBRAIN is an interdisciplinary initiative to better understand neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s disease. We combine brain imaging, machine learning, topological data analysis and computational modelling of biological neural networks at multiple scales to identify causal links among disease biomarkers and disease symptoms. This understanding should improve diagnosis, prediction of the disease progression and suggest better therapies. For more information about the position and to apply, visit the web site: https://www.kth.se/en/api/2.61673/what:job/jobID:432976/where:4/ Deadline for application: 01.Nov.2021 11:59 PM CET Prof. Erik Fransén Dept. of Computational Science and Technology School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm, Sweden https://www.kth.se/profile/erikf https://scholar.google.se/citations?user=VFtGuvcAAAAJ&hl=sv

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PhD position in theoretical neuroscience in Berlin
by Tilo Schwalger 06 Oct '21

06 Oct '21

Dear all, we are looking for a PhD candidate in theoretical neuroscience on the topic "Dynamics and variability of structured spiking neural networks". Although the focus is on the theory side, the project also includes the analysis of neuronal population data and associated problems of data assimilation. Methods will be developed within the frameworks of stochastic processes, statistical physics and nonlinear dynamics. The PhD position in my group will be part of the vibrant computational neuroscience community at the Bernstein Center for Computational Neuroscience Berlin and the Institute of Mathematics (Stochastics and Financial Mathematics) of TU Berlin. The successful candidate should have a degree in mathematics or physics, keen interest in computational neuroscience, expertise in analytical calculations, programming skills (C++ or C, Python or Julia, LaTeX), and excellent command of the English language, and good communication skills. Funding is provided for five years. Applications, including a letter of motivation, a CV, a current copy of academic transcripts, and a list of at least two potential referees should be sent by email to me: schwalger(a)math.tu-berlin.de The deadline for applications is October 15th 2021, however, later applications might also be considered. More information is available under: https://tub.stellenticket.de/en/offers/106202/?locale=en Kind regards, Tilo Schwalger -- Tilo Schwalger, PhD Assistant Professor Bernstein Center for Computational Neuroscience, Berlin Philippstr. 13, Haus 6, 10115 Berlin Room: 202, phone: 0049(0)302093 6788 Technische Universität Berlin Institute of Mathematics Straße des 17. Juni 136, 10623 Berlin Room: 726, phone: +49 (0)30 314-22698 http://page.math.tu-berlin.de/~schwalge tilo.schwalger(a)bccn-berlin.de

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Postdoc in Machine Learning at KTH, Stockholm, Sweden
by Tony Lindeberg 06 Oct '21

06 Oct '21

Postdoc in Machine Learning Job description The division of Computational Science and Technology at KTH Royal Institute of Technology in Stockholm, Sweden is seeking a Postdoc in Machine Learning to handle scale-dependent image information in deep networks. In our research, we develop deep networks for processing image data that handle scaling transformations and other image transformations in a theoretically well-founded manner. Our research in this area comprises both theoretical modelling of the influence of image transformations on different architectures for deep networks and the experimental evaluation of such networks on benchmark datasets to explore their properties. The work also comprises the creation of new benchmark datasets, to enable characterization of properties of deep networks that are not covered by existing datasets. For examples of our previous work in this area, see https://www.kth.se/profile/tony/page/deep-networks Within the scope of this postdoc position, you are expected to work on and contribute to the research frontier regarding scale-covariant or scale-equivariant deep networks and/or deep networks parameterized in terms of Gaussian derivatives, on specific research topics that we choose together. The selected candidate will work closely together with the project leader Tony Lindeberg. For further information and information about to how to apply, see https://kth.varbi.com/en/what:job/jobID:432053/ Application deadline: November 1, 2021 [cid:FD2B374F-C472-4895-ADA7-B3CD5419FD22@csc.kth.se] Tony Lindeberg Professor of Computer Science — Computational Vision KTH Royal Institute of Technology Computational Brain Science Lab Division of Computational Science and Technology, CST SE-100 44 Stockholm, Sweden Phone: +46 8 790 6205 https://www.kth.se/profile/tony/

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Open position in Modeling at the Allen Institute
by Anton Arkhipov 06 Oct '21

06 Oct '21

Are you excited about building bio-realistic, large-scale models of brain circuits? Do you enjoy collaborative, team science environment? Do you want to contribute to understanding fundamental mechanisms of brain function? If yes, this job opening might be for you! Check out this Scientist position in the lab of Anton Arkhipov at the Allen Institute in Seattle: https://alleninstitute.hrmdirect.com/employment/job-opening.php?req=1770679 The Scientist will build bio-realistic models and simulate the entrainment of neuronal populations by periodic sensory stimulation at different frequencies. This NIH BRAIN Initiative-sponsored project will leverage our expertise in modeling and unique data on cortex composition and connectivity at the Allen Institute, as well as experimental data from our collaborators at the group of Dr. Li-Huei Tsai at MIT. Relevant resources and publications: * https://portal.brain-map.org/explore/models/mv1-all-layers * https://portal.brain-map.org/explore/connectivity * Iaccarino et al., Nature (2016); Adaikkan et al., Neuron (2019); Martorell et al., Cell (2019) The Allen Institute believes that team science significantly benefits from the participation of diverse voices, experiences and backgrounds. High-quality science can only be produced when it includes different perspectives. We are committed to increasing diversity across every team and encourage people from all backgrounds to apply for this role. Anton Arkhipov Associate Investigator T: 206.548.8414 E: antona(a)alleninstitute.org<mailto:antona@alleninstitute.org> alleninstitute.org<https://alleninstitute.org> brain-map.org

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