- Comp-neuro - lists.cnsorg.org

Simons Foundation Bridge to Independence (BTI) Award programs

by Alyssa Picchini Schaffer

The Simons Foundation is invested in supporting the next generation of researchers. Our Bridge to Independence (BTI) Award programs promote talented early-career scientists by facilitating their transition to research independence and providing grant funding at the start of their professorships. We are offering such awards through two of our programs: the Simons Collaboration on the Global Brain (SCGB), and the Simons Foundation Autism Research Initiative (SFARI). These requests for applications (RFAs) are aimed at Ph.D. and M.D. – holding scientists who are currently in training positions and intend to seek tenure-track faculty positions over the next two years (i.e., 2021-2023). Successful applicants will receive a commitment of $495,000 over three years, activated upon assumption of a tenure-track professorship. Visit bit.ly/3i3GNeA for more information on both programs. __________________________________ Alyssa Picchini Schaffer, PhD Senior Scientist & Administrative Director Neuroscience Collaborations *SIMONS FOUNDATION* 160 Fifth Avenue New York, NY 10010 646.654.0066 *main * 646.751.1277 *direct * 717.818.8281 *mobile*

Jan. 28, 2021

Undergraduate computational neuroscience research position

by Neymotin, Samuel (NKI)

A new Army Research Office (ARO) undergraduate research apprenticeship program (URAP) position is available for the summer of 2021 at the Nathan Kline Institute for Psychiatric Research in Orangeburg, NY (https://www.nki.rfmh.org/) Research will be conducted under the mentorship of Samuel Neymotin, Haroon Anwar, and Salvador Dura-Bernal to help achieve the goals of an ARO grant (PI: Neymotin) that aims to train neocortical circuit models to play video games using biologically-plausible learning rules, and to compare the models’ performance against commonly used deep reinforcement learning algorithms. Students will have the opportunity to learn cutting edge methods in computational neuroscience, machine learning, and Python software development, and potentially publish their results. Candidates will be considered based on their experience in computational neuroscience, machine learning, and Python programming. Candidates must be located within commuting distance to Nathan Kline Institute during the apprenticeship period. For consideration, please click the link to “Apply Now” at https://www.usaeop.com/program/undergraduate-apprenticeships/ Make sure to use code U803 (Nathan Kline Institute) and apply by the deadline (March 15, 2021). Letters of recommendation are optional. For questions, email Samuel.Neymotin(a)nki.rfmh.org<mailto:Samuel.Neymotin@nki.rfmh.org> For more information about the URAP visit: http://www.usaeop.com/<https://protect2.fireeye.com/v1/url?k=1b10bbdd-47344dbc-1b1242e8-0cc47a6d17…> ________________________________ IMPORTANT NOTICE: This e-mail is meant only for the use of the intended recipient. It may contain confidential information which is legally privileged or otherwise protected by law. If you received this e-mail in error or from someone who was not authorized to send it to you, you are strictly prohibited from reviewing, using, disseminating, distributing or copying the e-mail. PLEASE NOTIFY US IMMEDIATELY OF THE ERROR BY RETURN E-MAIL AND DELETE THIS MESSAGE FROM YOUR SYSTEM. Thank you for your cooperation.

Jan. 27, 2021

[jobs] machine learning research scientist at NIMH

by Francisco Pereira

### HIRING: machine learning research scientist The Machine Learning Team at the National Institute of Mental Health (NIMH) in Bethesda, MD, has an open position for a machine learning research scientist. The NIMH is the leading federal agency for research on mental disorders and neuroscience, and part of the National Institutes of Health (NIH). ### About the NIMH Machine Learning Team Our mission is to help NIMH scientists use machine learning methods to address a diverse set of research problems in clinical and cognitive psychology and neuroscience. These range from identifying biomarkers for aiding diagnoses to creating and testing models of mental processes in healthy subjects. Our overarching goal is to use machine learning to improve every aspect of the scientific effort. We work with many different data types, including very large brain imaging datasets from various imaging modalities, behavioral data, and picture and text corpora. We have excellent computational resources, both of our own (tens of high-end GPUs for deep learning, several large servers) and shared within the NIH (a cluster with hundreds of thousands of CPUs, and hundreds of GPUs). As a machine learning research group, we develop new methods and publish in the main machine learning conferences (e.g. NeurIPS and ICLR), as well as in psychology and neuroscience journals. Many of our problems require devising research approaches that combine imaging and non-imaging data, and leveraging structured knowledge resources (databases, scientific literature, etc) to generate explanations and hypotheses. You can find more about our work and publications at https://cmn.nimh.nih.gov/mlt ### About the position We are seeking candidates who are capable of combining machine learning, statistical, and domain-specific computational tools to solve practical data analysis challenges (e.g. designing experiments, generating and testing statistical hypotheses, training and interpreting predictive models, and developing novel models and methods). Additionally, candidates should be capable of visualizing and communicating findings to a broad scientific audience, as well as explaining the details of relevant methods to researchers in a variety of domains. Desirable experience that is not required, but will be considered very favorably: - neuroimaging data processing and analysis (any MRI modality, as well as MEG and EEG) - other types of neural data (e.g. neural recording, calcium imaging) - modelling of human and animal learning and decision-making - Bayesian statistical modelling Finally, you should have demonstrable experience programming in languages currently used in data-intensive, scientific computing, such as Python, MATLAB or R. Experience with handling large datasets in high performance computing settings is also very valuable. Although this position requires a Ph.D. in a STEM discipline, we will consider applicants from a variety of backgrounds, as their research experience is the most important factor. This is an ideal position for someone who wants to establish a research career in method development and applications driven by scientific and clinical needs. Given our access to a variety of collaborators and large or unique datasets, there is ample opportunity to match research interests with novel research problems. We also maintain collaborations outside of the NIH, driven by our own research interests. If you would like to be considered for this position, please send francisco.pereira(a)nih.gov a CV, with your email serving as cover letter. We especially encourage applications from women and members of underrepresented groups. If you already have a research statement, please feel free to send that as well. There is no need for reference letters at this stage. Other inquiries are also welcome. Thank you for your attention and interest! ReplyForward <https://drive.google.com/u/0/settings/storage?hl=en&utm_medium=web&utm_sour…> <https://www.google.com/intl/en/policies/terms/> <https://www.google.com/intl/en/policies/privacy/> <https://www.google.com/gmail/about/policy/>

Jan. 27, 2021

[CFP] ICLR 2021 Workshop on "How Can Findings About The Brain Improve AI Systems?"

by Mariya Toneva

ICLR 2021 - Workshop on "How Can Findings About The Brain Improve AI Systems?" Submission deadline: February 26, 2021 Workshop website: https://iclrbrain2ai.github.io/ Contact: brain2ai.2021(a)gmail.com The brain comprises billions of neurons organized into an intricate network of highly specialized functional areas. This biological cognitive system can efficiently process vast amounts of multi-modal data to perceive and react to its ever-changing environment. Unlike current AI systems, it does not struggle with domain adaptation, few-shot learning, or commonsense reasoning. Inspiration from neuroscience has benefited AI in the past: modern convolutional networks mimic the deep, nested information flow in visual cortex, and hippocampal replay of previous experiences has brought about experience replay in reinforcement learning. Recent work at the intersection of neuroscience and AI has made progress in directly integrating neuroscientific data with AI systems and has led to learned representations that are more robust to label corruptions, allow for better generalization in some language tasks, and provide new ways to interpret and evaluate what domain-relevant information is learned by deep neural networks. In this workshop, we aim to examine the extent to which insights about the brain can lead to better AI. We will consider the following (non-exhaustive) list of topics for contribution: - Integration of neuroscientific data at different granularities (neural recordings, neuroimaging etc.) to train AI systems - Brain-inspired inductive biases and ML architectures for AI systems - Neuroscientific data as an evaluation metric for AI systems - Mechanistic insight into cognitive functions (vision, language, decision making, attention, etc.) - Limitations of current AI systems in tasks at which humans excel (compositionality, commonsense reasoning, continual learning, few-shot learning, causality, etc.) - Cross-modality and cross-species comparisons of neuroscientific data with respect to benefits to AI - Datasets that can facilitate the transfer of neuroscientific insight to AI Submit at: https://cmt3.research.microsoft.com/BRAIN2AI2021/Submission/Index The workshop will feature a diverse set of topics led by the following invited speakers and panelists (in alphabetical order): 1. Anima Anandkumar, California Institute of Technology 2. David Cox, Harvard University 3. Kenji Doya, Okinawa Institute of Science and Technology Graduate University 4. Allyson Ettinger, University of Chicago 5. Alona Fyshe, University of Alberta 6. Jack Gallant, University of California, Berkeley 7. Andrea Martin, Max Planck Institute for Psycholinguistics 8. Kimberly Statchenfeld, DeepMind 9. Josh Tenenbaum, MIT Formatting Instructions: All submissions must be in PDF format. Submissions are limited to four content pages, including all figures and tables; additional pages containing only references are allowed. You must format your submission using the ICLR 2021 LaTeX style files. Submissions that meaningfully violate the ICLR style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review. All submissions should be anonymous. The review process is double-blind. We also welcome recently (within the last year) published papers that are within the scope of the workshop (without re-formatting). Already-published papers do not have to be anonymous. They are eligible for a spotlight session and will only have a very light review process. Virtual format: The workshop will be geared towards more dynamic and interactive discussion focused on Q&A sessions. Accepted abstracts and papers will result in, at minimum, a pre-recorded spotlight talk. All presenters will be given live virtual “coffee break” rooms during all breaks in the conference schedule to engage in small group discussions with participants. Discussions around talks will be organized in live virtual “breakout” rooms with similar topics and themes so that workshop attendees and presenters can interact in a less formal, discussion-centric setting. Important Dates: Paper submission deadline: 11:59 PM EST, Feb 26, 2021 Author notification: 11:59 PM EST, Mar 26, 2021 Camera-ready papers due: 11:59 PM EST, Apr 15, 2021 Workshop: May 8, 2021

Jan. 26, 2021

2 NEUROSCIENCE / DATA SCIENCE POSITIONS (Deadline: 11 Feb 2021)

by Prata, Diana

2 NEUROSCIENCE / DATA SCIENCE RESEARCH POSITIONS IN LAB AT UNIVERSITY OF LISBON (DPRATALAB.WORDPRESS.COM <http://dpratalab.wordpress.com/>) (Deadline: 11 Feb 2021) 1 RESEARCH POSITION IN BIOMEDICAL DATA SCIENCE Work plan: Statistical analysis of retrospective neuroimaging and clinical data, using artificial intelligence (machine learning) for the development and validation of diagnostic neuroimaging biomarkers, with high potential of clinical applicability; coordination with Portuguese and UK research, IT and medical teams within an academic-entrepreneurial join-venture, with the goal of helping clinical decision-making in neurodegenerative illnesses. Up to 2 years duration. Details in: http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=130697 <http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=130697> 1 RESEARCH POSITION IN HUMAN NEUROSCIENCE Work plan: Statistical analysis of varied psychophysiological data (EEG and/or MRI and/or pupillometry) and write up of scientific articles in clinical study with pharmacological intervention of oxytocin, with the goal of characterizing the role of oxytocin in human brain function in health and in psychosis. Up to 1.5 years duration. Details in: http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=130890 <http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=130890> Diana Prata, Phd Principal Investigator & Lecturer, Institute of Biophysics and Biomedical Engineering, FCUL Visiting Lecturer, IoPPN, King's College London Associate Investigator, CIS, ISCTE-IUL (+351)966595936; https://dpratalab.wordpress.com/ <https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdpratalab…>, https://neurocolab.wordpress.com/ <https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fneurocola…>.

Jan. 25, 2021

Postdoc position in multimodal imaging of brain dynamics

by Laura Lewis

Dear colleagues, We have an open postdoctoral position in multimodal imaging of brain states, please feel free to circulate the ad below. *Postdoctoral position in multimodal imaging of brain dynamics* We have an opening for a postdoctoral associate to lead multimodal neuroimaging studies of brain function in Laura Lewis' lab in the Department of Biomedical Engineering at Boston University ( http://www.lewisneurolab.org) This position offers the opportunity to develop multimodal neuroimaging and analysis techniques, and study the neural basis and cognitive consequences of sleep and arousal states. We use fast fMRI, simultaneous EEG-fMRI, computational modeling, ultra-high field (7 Tesla) imaging, and other multimodal approaches to measure and analyze brain function across diverse states. Areas of focus include: - fast fMRI/EEG studies of brain dynamics - thalamic and brainstem networks across vigilance states - neural basis of sleep and sleep deprivation - sleep and mood states in major depression - neurovascular coupling and cerebrospinal fluid physiology - sleep and brain function in aging The lab is a fast-paced and interdisciplinary environment that supports its members in achieving their long-term research and career goals, and has strong collaborations throughout the Boston-area computational neuroscience and neuroimaging communities. The position may involve conducting neuroimaging studies at both Boston University and at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital. For more information, see http://www.lewisneurolab.org/positions . This position requires a Ph.D. in neuroscience, engineering, computer science, psychology, or related fields. To apply, please email C.V., a statement describing research experience and future interests and goals, and names of three references to Laura Lewis, ldlewis(a)bu.edu thanks, Laura Lewis Assistant Professor of Biomedical Engineering Boston University https://www.lewisneurolab.org/

Jan. 25, 2021

[2nd BCI-UC] Voting and abstract submission closes this Friday

by Moritz Grosse-Wentrup

Dear colleagues, We have extended the deadline for abstract submission for the 2nd Brain-Computer Interface Un-Conference until this Friday at 11:59 pm CET: https://bciunconference.univie.ac.at/2nd-bci-uc/ The deadline for voting on submitted abstracts is extended accordingly. The BCI-UC committee: Moritz Grosse-Wentrup Anja Meunier Philipp Raggam Jiachen Xu -- Univ.-Prof. Dr.-Ing. Moritz Grosse-Wentrup Research Group Neuroinformatics Faculty of Computer Science University of Vienna Hörlgasse 6, A-1090 Wien, Austria moritz.grosse-wentrup(a)univie.ac.at +43-1-4277-79610 http://neural.engineering/

Jan. 25, 2021

CFP 30th International Conference on Artificial Neural Networks ICANN 2021, Sept 14-17, Bratislava, SK

by Paolo Masulli ENNS

30th International Conference on Artificial Neural Networks ICANN 2021 https://e-nns.org/icann2021 Bratislava, Slovakia 14 - 17 September 2021 ============================================================= The International Conference on Artificial Neural Networks (ICANN) is the annual flagship conference of the European Neural Network Society (ENNS). In 2021 the Faculty of Mathematics, Physics and Informatics (FMPI), Comenius University in Bratislava, together with the Slovak Society for Cognitive Science, organize the 30th ICANN Conference from the 14th to the 17th of September 2021 in Bratislava, Slovakia. *Possibility of online/hybrid event:* in case the organization of a physical event will not be possible because of COVID-19 travel/gathering restrictions, the conference will happen online. * **CONFERENCE TOPICS* ICANN 2021 is a dual-track conference featuring tracks in Brain Inspired Computing and Machine Learning and Artificial Neural Networks, with strong cross-disciplinary interactions and applications. All research fields dealing with Neural Networks will be present at the conference. A non-exhaustive list of topics includes: Machine Learning: Deep Learning, Neural Network Theory, Neural Network Models, Graphical Models, Bayesian Networks, Kernel Methods, Generative Models, Information Theoretic Learning, Reinforcement Learning, Relational Learning, Dynamical Models, Recurrent Networks. Brain Inspired Computing: Cognitive models, Computational Neuroscience, Self-organisation, Reinforcement Learning, Neural Control and Planning, Hybrid Neural-Symbolic Architectures, Neural Dynamics, Cognitive Neuroscience, Brain Informatics, Perception and action. Neural Applications for: Bioinformatics, Biomedicine, Intelligent Robotics, Neurorobotics, Language Processing, Image Processing, Sensor Fusion, Pattern Recognition, Data Mining, Neural Agents, Brain-Computer Interaction, Neural Hardware, Evolutionary Neural Networks. *CALL FOR CONTRIBUTED SCIENTIFIC COMMUNICATIONS* All scientific communications presented at ICANN 2021 will be reviewed and scientifically evaluated by a panel of experts. The conference will feature two categories of communications: - oral communications (15'+5') - poster communications. *Call for Special Sessions* ICANN 2021 organizers cordially invite internationally recognised experts to organise Special sessions and workshops within the general scope of the conference. Special Session Proposal request should be sent to icann2021(a)fmph.uniba.sk *CALL FOR PAPERS* Authors willing to present original contributions in either oral or poster category may submit: - A full paper of maximum 12 pages (including references) to be published in Springer-Verlag Lecture Notes in Computer Science (LNCS) series with individual DOI. - An extended abstract of maximum 4 pages to be published in Springer-Verlag Lecture Notes in Computer Science (LNCS) series, without indexing. The number of oral slots is limited. In case the number of requested oral presentations is larger than the available slots the ICANN scientific committee will select which papers will be reassigned to a poster session. This selection will be based on the coherence of the programme and is totally independent of the category of submission. Submission of communications will be on the conference website: https://e-nns.org/icann2021/ *IMPORTANT DATES* Deadline for special sessions proposals: 1 Mar 2021 Opening of contribution submissions: 15 Feb 2021 Deadline for full paper and extended abstract submission: 15 Mar 2021 Notification of acceptance: 1 May 2021 Camera-ready paper and registration opening: 1 June 2021 Deadline for early registration at discount rate: 30 June 2021 *BEST PAPER AWARDS* ENNS will sponsor a maximum of four best paper awards. All awards will be presented during the final ceremony. *TRAVEL GRANTS* The European Neural Network Society sponsors a number of Student Travel Grants covering part of the costs for attending ICANN. Details will be provided on the conference website. *ORGANISATION* General Chairs: Igor Farkaš (FMPI, Comenius University in Bratislava, Slovakia) Ľubica Beňušková (FMPI, Comenius University in Bratislava, Slovakia) Organising Committee Chairs: Kristína Malinovská (FMPI, Comenius University in Bratislava, Slovakia), Alessandra Lintas (ENNS Lausanne, Switzerland) Honorary Chairs: Stefan Wermter, University in Hamburg, Germany (ENNS President) Věra Kůrková, Czech Academy of Sciences, Prague, Czechia (past ENNS President) Communication Chair: Paolo Masulli (Technical University of Denmark, ENNS)

Jan. 22, 2021

Registration Deadline is approaching!

by Mary

Dear community, As we are approaching deadline (January 30) for registering for "EEG/ERPs Recording and Analysis: A multifaceted approach to EEG signals" Online Course, here is a reminder for the interested researchers who haven’t been registered yet. The "EEG/ERPs Recording and Analysis: A multifaceted approach to EEG signals" Online Course is going to be held by Dr. Raphaël Fargier, PhD in Cognitive Neuroscience, Université Claude Bernard Lyon 1, France, who is a researcher in cognitive neuroscience with 10 years of experience and his research is focused on semantic cognition and the neurobiology of language. Through the course you will learn about the basics of EEG signal and how to record it. You will learn different approaches to EEG signal processing, that are more or less commonly adopted in cognitive neuroscience, including the study of Event-Related Potentials, Microstates/Topographic patterns, and Brain oscillations. You will learn about EEG signal preprocessing and how to compute ERPs, Microstates and Time-frequency maps in relationship with the constraints linked to experimental manipulations and designs. There will be a heavy focus on critical thinking about the choices of analyses as a function of research questions, challenges depending on experimental designs, and parameters to pay attention to. The variety and appropriateness of statistical methods currently used to validate (or not) hypotheses about cognitive processes will also be discussed. All of that will be evidenced through the presentation and discussion of case studies in electrophysiology of language. At the end of the course, you will know the strengths and weaknesses of these tools, and will be able to decide which measure is best adapted to your own scientific investigations about cognition. Prospective overview of the sessions 1. Basics of EEG signal and EEG recordings * Overview of the theory behind neural signals * Contrasting neuroimaging methods * Presentation of different EEG recording systems and their classical features * Tips on EEG recordings * Principles of experimental designs using EEG in cognitive neuroscience (events, timing etc) 2. EEG pre-processing Goal: Mastering the successive pre-processing steps including principles and challenges of: * Artifact rejection and correction (e.g. ICA; 3D Spline electrode interpolation) * Filtering (e.g. parameters, band widths etc) * Re-referencing (e.g. mastoid, average reference, Laplacian) * single-to-noise and number of trials 3. ERPs * Presentation of classical ERPs as markers of cognitive processes(e.g. N1/P2, P1, N400, etc.) * Computing single-subject and group-average ERPs: Pre-stimulus baseline correction (rationale, parameters, tips) Methods used to extract measures including amplitudes, latencies and topographies of ERPs Methods for statistical evaluation of differences across experimental manipulations * Differences between stimulus-aligned and response-aligned epochs, and associated challenges for signal processing 4. Spatio-temporal segmentation * Origin of microstates * Rationale of Spatio-temporal segmentation (StS) and presentation of successive steps of analyses (e.g. Topography consistency test, Tanova) * Presentation of two ways to compute StS (different toolboxes) and the respective parameters to take care of * Extraction of measures, and statistical evaluations of differences across experimental manipulations via fitting (e.g. global explained variance, duration, presence, etc.) * Single-trials and mixed models 5. Time-frequency analyses * Rationale of oscillatory dynamics in EEG signal (evoked vs. induced activity) * Challenges in pre-processing to obtain time-frequency maps as compared to ERPs * Different methods used for time-frequency decomposition * Challenges of baseline normalization (the case of response-aligned epochs) * The various measures that can be extracted from time-frequency maps and the variety of approaches for statistical validation You can watch the introductory webinar to this course using the linke below: https://sciencebeam.com/raphael-fargier-webinar/ For more information and registration click on the link below: https://sciencebeam.com/eeg-erps-recording-and-analyses/ Don’t miss this opportunity!

Jan. 21, 2021

PhD/Postdoc at the Group for Neural Theory, ENS, Paris

by CAYCO GAJIC Natasha

Dear all, A PhD or postdoctoral position is available in Alex Cayco Gajic's team in the Group Neural Theory, at the ENS, in Paris, France (http://iec-lnc.ens.fr/group-for-neural-theory/ [1]). The project will use recurrent neural networks to understand cerebello-cortical dynamics during motor learning. A component of the project can be to develop analysis tools for broader use in systems neuroscience. The Group for Neural Theory is a highly interactive and dynamic environment situated in central Paris, and embedded within the strong Parisian theoretical neuroscience community. The ideal candidate should have a strong quantitative background, e.g. in computational neuroscience, physics, applied maths, or computer science. Previous experience in neuroscience and/or machine learning is preferred but not required. Starting dates are flexible. Candidates should send a letter of motivation (2 pages max), the contact information of 2 to 3 referees and their CVs to natasha.cayco.gajic at ens.fr [2]. Please feel free to email me with any informal inquiries. Best, Alex https://sites.google.com/view/caycogajic/home Links: ------ [1] http://iec-lnc.ens.fr/group-for-neural-theory/ [2] http://ens.fr/

Jan. 21, 2021