- Comp-neuro - lists.cnsorg.org

Two postdoc positions in neuroAI
by Rui Ponte Costa Aug. 2, 2023

Aug. 2, 2023

Dear all, We are seeking to appoint two Postdoctoral Research Scientists to join the Neural and Machine Learning Group part of the world-class Oxford Neuroscience and the Oxford Neurotheory initiative. You will carry out research at the intersection between computational neuroscience and machine learning, closely informed by experimental observations. More details here: shorturl.at/hkwG3<https://t.co/d7ugbOLFDV> and of our group: neuralml.github.io<https://neuralml.github.io/> Best wishes, Rui Ponte Costa Group Leader, Neural and Machine Learning group<https://neuralml.github.io/> Centre for Neural Circuits and Behaviour | Oxford Neurotheory Department of Physiology, Anatomy and Genetics Department of Experimental Psychology (honorary member) University of Oxford, UK

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[Job][PhD] at Inria Bordeaux, FR: From Reservoir Transformers to BrainGPT
by Xavier Hinaut Aug. 2, 2023

Aug. 2, 2023

PhD position available at Inria, Bordeaux, France: "From Reservoir Transformers to BrainGPT" # Keywords Transformers; Reservoir Computing; Computational Neuroscience # Duration & start date 3 years starting 1st October 2023 (estimated) # Contact & Application Informal contact/questions and application: email to xavier dot hinaut at inria dot fr Application & More info: https://recrutement.inria.fr/public/classic/en/offres/2023-06611 All positions available: https://github.com/neuronalX/phd-and-postdoc-positions Deadline: Applications will be considered on a rolling basis. A candidate will be selected as soon as a suitable profile is found. # Context This PhD thesis is part of the BrainGPT "Inria Exploratory Action" project. In the wake of the emergence of large-scale language models such as ChatGPT, the BrainGPT project is at the forefront of research in Artificial Intelligence and Computational Neuroscience. These models, although remarkably powerful, do not reflect the way in which our brains process and learn language. BrainGPT is rising to the challenge by focusing on the development of models that are more faithful to human cognitive functioning, inspired by data on brain activity during listening or reading. The ambition is to create more efficient models that are less dependent on intensive calculations and massive volumes of data. BrainGPT will open up new perspectives on our understanding of language and cognition. # Project and Work description The rapid rise in performance of language models, as demonstrated by the recent appeal of ChatGPT, is undeniable. However, the computational cost and environmental impact associated with such models are often overlooked [1]. These models rely on Transformers [2], which facilitate unsupervised learning on a large volume of data. These same models are used to predict brain activity from functional magnetic resonance imaging (fMRI) or magnetoencephalography (MEG), an application our team also exploits [3]. The main ambition of the BrainGPT project is to combine the explainability of mechanistic models with the predictive power of Transformers to analyze brain imaging data. Today, on the one hand we have explanatory but less predictive mechanistic models, such as those based on Reservoir Computing, and on the other hand, high-performance predictive models, but not explanatory, like Transformers. Our goal is to combine the best of these two approaches, to develop more efficient ("sample efficient") models inspired by Transformers, which more faithfully reflect the how the brain works, while improving the predictive power of mechanistic models. Towards this ambition, the BrainGPT project seeks to identify the key mechanisms that allow Transformers to predict brain activity. Furthermore, our project strives to build models that are more biologically plausible than Transformers, incorporating the most relevant components for predicting brain activity, while integrating constraints derived from human cognition studies. The long-term objectives of the BrainGPT project are as follows: - Making Transformers more biologically plausible, which could improve the prediction of brain activity by imaging (fMRI, MEG, etc.). - Proposing new perspectives and computing paradigms that do not rely exclusively on gradient backpropagation, given its high computational and energy cost. - Reducing the energy footprint of Transformers by minimizing the computational costs associated with their learning. In summary, the thesis will mainly consist of developing new bio-inspired models inspired by the mechanisms, learning methods, and emerging behaviors of Large Language Models (LLMs) and Transformers. Subsequently, in collaboration with our collaborators, these models will be tested to assess their ability to predict brain activity from imaging data. [1] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?🦜. In Proc. of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623). [2] Vaswani, A. et al. (2017) Attention is all you need. In Proc. of Advances in neural information processing systems. [3] Oota, S. R., Trouvain, N., Alexandre, F., & Hinaut, X. (2023, August). MEG Encoding using Word Context Semantics in Listening Stories. In Proc. of INTERSPEECH 2023. # Skills Ideal Candidate Profile: - Holds an engineering or scientific degree and/or a PhD in digital sciences (computer science, automation, signal processing). - Has a first professional experience (6 months of internship or more) in Machine Learning and Python development. Especially in one or more of the following: Recurrent Neural Networks (in particular Reservoir Computing), Transformers, Large Language Models. - Possesses strong expertise in the scientific Python software and scientific stack (numpy/scipy). - Demonstrates a solid grasp of linear algebra concepts. - Proficiency in technical English is crucial, as it enables efficient collaboration with our international partners and effective presentations at conferences. - Has proven experience with version management, familiarity with Git, and proficiency in using the GitHub platform. # Advisor & Location Xavier Hinaut Inria Bordeaux & Institute for Neurodegenerative diseases (Pellegrin Hospital Campus, Bordeaux). Best regards, Xavier Hinaut Inria Research Scientist www.xavierhinaut.com -- +33 5 33 51 48 01 Mnemosyne team, Inria, Bordeaux, France -- https://team.inria.fr/mnemosyne & LaBRI, Bordeaux University -- https://www4.labri.fr/en/formal-methods-and-models & IMN (Neurodegeneratives Diseases Institute) -- http://www.imn-bordeaux.org/en --- Our Reservoir Computing library: https://github.com/reservoirpy/reservoirpy

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[JOBS] Tenure Track Assistant Professor Intelligent Systems @ Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, the Netherlands.
by Kerstin Bunte Aug. 2, 2023

Aug. 2, 2023

Tenure Track Assistant Professor Intelligent Systems https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0… Closing date = 17-9-2023 We offer a Tenure Track Assistant Professor position with a research profile in Intelligent Systems within the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, The Netherlands. Intelligent Systems aim to give computers and cyber-physical systems the abilities to perceive, analyze and learn from data, make decisions, and enhance human capabilities. The research area has experienced tremendous growth in recent years, especially due to the increase in incorporation of intelligent systems into nearly every aspect of our lives. The societal relevance of sustainability, efficiency, robustness, understanding and guarantees of algorithms is gaining more and more importance, not only in software but also in combination with cyber-physical systems. Relevant aspects are: the theory of machine learning, artificial intelligence and the mathematics of computing (ACM Computing Classification System, 2012 Revision). For this position, possible research directions include (but are not limited to) the theory and application of novel machine learning and artificial intelligence methods with an emphasis on solving pressing open questions, such as efficiency, transparency and interpretability, fairness, the integration of multi-modal and complex heterogeneous data, causal inference, continuous machine learning, generative and dynamic systems. Complex problems demand an increasing amount of interdisciplinary and cross-domain knowledge. Therefore, backgrounds in information theory, probabilistic representations, causality and computational geometry are also welcome to apply. As Assistant Professor with a focus on research, you will: - set up and develop your own research line and research group - supervise PhD students - acquire external funding - promote the societal relevance of your research - teach in and contribute to the development of the degree programs of the School of Science and Engineering, especially courses with computing science content - contribute to the organization of the faculty, for example by participating in working groups and committees, in the domains of teaching, research and management. The position will be embedded in the Intelligent Systems research unit. Our research areas include biologically inspired computational modeling, the theory of machine learning and algorithmic development, as well as image processing with mathematical morphology, machine learning and AI. National and international research collaborations span a multitude of application domains, including medical sciences and health care, natural sciences (astronomy, bioinformatics), remote sensing, computer vision, robotics, smart industry and engineering. About the University of Groningen Since its foundation in 1614, the University of Groningen has enjoyed an international reputation as a dynamic and innovative center of higher education offering high-quality teaching and research. Balanced study and career paths in a wide variety of disciplines encourage currently more than 37,000 students and researchers to develop their own individual talents. Belonging to the best research universities in Europe and the top 100 universities in the world, the University of Groningen is truly an international place of knowledge. More information can be found at: https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0… Kind regards, Kerstin -- Kerstin Bunte, Associate Professor | Rosalind Franklin Fellow Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence Faculty of Science and Engineering University of Groningen P.O. Box 407, 9700 AK Groningen The Netherlands Email: k.bunte(a)rug.nl web: http://www.cs.rug.nl/~kbunte/

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Advanced Electrophysiology Post-doc Position Available - Interventional Psychiatry Program, Minneapolis, MN, USA
by Hiba Sheheitli Aug. 1, 2023

Aug. 1, 2023

*About the Job* Dr. Ziad Nahas (Interventional Psychiatry Lab) in the University of Minnesota Department of Psychiatry and Behavioral Sciences is seeking an outstanding candidate for a postdoctoral position to conduct and analyze the effects of neuromodulation on brain activity in mood disorders. Candidates should be passionate about advancing knowledge in the area of translational research of depressive disorders and other mental health conditions with a focus on invasive and non-invasive brain stimulation treatments. The position is available June 1, 2023, and funding is available for at least two years. *Qualifications* Required Qualifications: ●PhD in relevant field (neuroscience, psychology, cognitive science) ●Strong programming and/or data analysis skills. MATLAB or Python is essential. C/C++ (and derivatives), LabVIEW, and scripting languages could be useful. Prior experience with data acquisition hardware including behavioral as well as EEG would be helpful. Proficiency with Microsoft Office software ●Experience with neurophysiology (human or animal), psychophysics and/or brain stimulation is ideal. ●Excellent verbal and written communication skills, with specific documentation of those skills through published work and oral scientific presentations. ●Demonstrated ability to work independently and to independently overcome complex technical challenges. ●Interpersonal skills appropriate for collaboration in a very multidisciplinary and highly diverse environment. Preferred Qualifications: •Previous experience with psychiatric patient populations, the biological mechanisms of mental illness, and/or medical device development. •Signal processing and/or computer science background. •Previous experience with human functional imaging or electrophysiology. *About the Department* Dr. Ziad Nahas’s Lab is located primarily at the Interventional Psychiatry Program’s St. Louis Park clinic, with additional study activity at UMN and M Health Fairview sites on the UMN campus. Our work focuses on advancing translational research of mood dysregulation and depressive disorders. Specific interests and areas of expertise include: ● Improving patient care of treatment resistant disorders using neuromodulation techniques. ● Functional neuroimaging (fMRI), electroencephalography (EEG) and brain stimulation modalities: Transcranial magnetic stimulation (TMS), transcranial alternating current stimulation (tACS), vagus nerve stimulation (VNS), prefrontal cortical stimulation (PCS), deep brain stimulation (DBS), electroconvulsive therapy (ECT) and focal electrically administered seizure therapy (FEAST). ● Increasing equitable access to neuromodulation interventions for patient care. To learn more about Dr Ziad Nahas and the Department of Psychiatry & Behavioral Sciences, please visit: https://med.umn.edu/bio/psychiatry/ziad-nahas *Apply here: * https://hr.umn.edu/Jobs/Find-Job <https://hr.umn.edu/Jobs/Find-Job> (Click on External Candidates and search for 355003 to find the Job Posting)

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NeurIPS Workshop "Information-Theoretic Principles in Cognitive Systems"
by Rava A. da Silveira July 30, 2023

July 30, 2023

NeurIPS Workshop: "Information-Theoretic Principles in Cognitive Systems" CALL FOR PAPERS Website (with submission information): https://sites.google.com/view/infocog-neurips-2023/call-for-papers Contact: infocog.neurips2023(a)gmail.com Important Dates: * Paper submission deadline: September 20, 2023 * Author notification: October 27, 2023 * Camera ready deadline: November 27, 2023 The second InfoCog@NeurIPS workshop aims to further the understanding of information flow in cognitive processes and neural networks models of cognition by bringing together researchers from multiple disciplines, including machine learning, cognitive science, neuroscience, and information theory. This is the second workshop of this kind in NeurIPS. This year the workshop will continue last year’s community-building momentum and will additionally focus on advancing two main goals: First, providing a fruitful platform for discussing formulations of storage and processing of information in human and artificial cognitive systems, via information-theoretic measures. Specifically, we aim to further integrate information theory researchers in such discussions, deepening the mathematical knowledge and tools shared across disciplines. [NZ1] Second, facilitating research on addressing the challenges of computing information theoretic quantities, specifically for the needs of cognitive science and machine learning. We invite short submissions (up to 4 pages – NeurIPS format) that present original work related to information theory and cognitive systems, and particularly encourage submissions of collaborative interdisciplinary work. Examples of specific topics of interest include but are not limited to: * Novel information-theoretic approaches to cognitive functions (e.g., perception, decision making, language, social reasoning, etc.) * Method and approaches for the validation of information theoretic formalisms in human and artificial cognition * Novel methods and approaches for the computation and/or estimation of information theoretic quantities and their applications to human and artificial cognition * Challenges and limitations of the use of information theory in studying cognitive systems * Application of information theory to training human-aligned artificial agents, i.e., agents that can better communicate and cooperate with humans All accepted papers will be presented in a poster session during the workshop, and a number of papers will also be selected for an oral presentation. We expect to have limited resources to offer financial aid to selected participants. More details will follow upon acceptance.

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Postdoc in computational modeling at Stanford Cognitive & Systems Neuroscience Lab
by Mai-Phuong K Bo July 27, 2023

July 27, 2023

The Stanford Cognitive and Systems Neuroscience Laboratory (scsnl.stanford.edu) invites applications for a postdoctoral fellowship in human brain network analysis and modeling. We seek candidates with strong research backgrounds in one or more of the following areas: resting-state and task-evoked fMRI connectivity, structural HARDI-based connectivity, neurophysiological modeling of brain networks, Bayesian modeling, machine learning, and deep neural networks. Candidates must have strong computational skills and a PhD in computational neuroscience, computer science, physics, biomedical engineering or related fields. This is an exciting opportunity to work on multiple NIH- and Stanford-funded projects using multimodal (fMRI, optogenetic, DTI, EEG) brain imaging techniques in multiple clinical populations and normative groups. The successful candidate will develop a vigorous research program that contributes to, and complements, ongoing research studies. The candidate will have access to state-of-the-art brain imaging and HPC/GPU facilities.  Please email a CV, statement of research interests and relevant background, and have three letters of reference sent to Prof. Vinod Menon at  scsnl.stanford+postdoc(a)gmail.com<mailto:scsnl.stanford@gmail.com> Mai-Phuong Bo (She/Her) Lab & Recruitment Manager Stanford Cognitive & Systems Neuroscience Lab Department of Psychiatry & Behavioral Sciences Email: maipbo(a)stanford.edu [cid:93b74359-266e-493c-9def-d9fb37f515df]

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PhD position at the TU Delft at the crossroads of machine learning and hardware design
by justin＠dauwels.com July 27, 2023

July 27, 2023

Dear colleagues, We are looking for a highly motivated Ph.D. candidate with a background in machine learning and/or in electrical engineering for a project at the TU Delft at the crossroads of machine learning and hardware design. Building autonomous agents that can reliably compute and take decisions in noisy and uncontrolled environments is among the top research areas in today's artificial intelligence (AI). Yet, doing so within constrained power budgets for battery-operated edge devices is currently an open challenge. Indeed, while current cutting-edge deep-learning approaches can now reach acceptable performance in such environments, they are still subject to adversarial attacks and need a backend of GPU clusters, thereby requiring 4 to 6 orders of magnitude more power than is allowed for by the edge power budgets. On both ends of the spectrum of learning algorithms are the error backpropagation algorithm, i.e. the workhorse of modern deep learning, and local Hebbian learning rules, which are inspired by the brain's synaptic plasticity mechanisms. The former offers excellent performance but its energy/memory footprint is incompatible with low-power edge devices, while the latter allows for low-cost hardware implementations but can hardly be deployed beyond toy problems. In this PhD project, you will tackle this challenge by: * developing a theoretical framework that will allow exploring and generating, in a use-case-driven fashion, emerging learning algorithms that combine the strengths of local brain-inspired learning and backprop, * with a hardware/algorithm co-design approach, developing custom neuromorphic silicon prototypes (digital, then mixed-signal) for the proposed learning rules * investigating the deployment of these adaptive prototypes in resource-constrained use cases at the edge, such as brain implants for seizure detection. This project is a collaboration between Dr. Charlotte Frenkel (neuromorphic hardware, hardware/algorithm co-design, brain-inspired machine learning) and Dr. Justin Dauwels (Bayesian machine learning, computational neuroscience, biosignal processing). About the Department of Microelectronics at TU Delft: https://microelectronics.tudelft.nl/ About the CogSys research lab: https://ei.et.tudelft.nl/Research/theme.php?id=63 The expected starting date is 01/11/2023 and deadline for application is 01/09/2023. For more information and to apply, please visit this website <https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details? jobId=13043&jobTitle=PhD%20Position%20in%20Bio-plausible%20Local%20Learning% 20Rules%20for%20Adaptive%20Neuromorphic%20Hardware> . Greetings, Justin. _______________________________________________________________ Justin Dauwels j.h.g.dauwels(a)tudelft.nl <mailto:j.h.g.dauwels@tudelft.nl> Associate Professor http://cas.tudelft.nl/ Fac. EEMCS Section Circuits and Systems Mekelweg 4 2628 CD Delft, The Netherlands _____________________________________________________________________

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SUBMIT YOUR WORK TO COMPLEX NETWORKS 2023 BEFORE SEPT. 02, 2023
by Hocine Cherifi July 26, 2023

July 26, 2023

*12* *th **International Conference on Complex Networks & Their Applications* Menton Riviera <https://www.menton-riviera-merveilles.co.uk/>, France November 28 - 30, 2023 COMPLEX NETWORKS 2023 <https://complexnetworks.org/> You are cordially invited to submit your contribution until *September 02, 2023*. *SPEAKERS* · Michael Bronstein <https://www.cs.ox.ac.uk/people/michael.bronstein/> University of Oxford UK · Kathleen Carley <https://www.cylab.cmu.edu/directory/bios/carley-kathleen.html> Carnegie Mellon University USA · Manlio De Domenico <https://manliodedomenico.com/> University of Padua Italy · Danai Koutra <http://web.eecs.umich.edu/~dkoutra/> University of Michigan USA · Romualdo Pastor-Satorras <http://www.fen.upc.edu/~romu/> Univ. Politècnica de Catalunya Spain · Tao Zhou <https://scholar.google.com/citations?user=MXgWgmEAAAAJ&hl=en> USTC China *TUTORIALS* · Tiago de Paula Peixoto <https://skewed.de/tiago> CEU Vienna Austria · Maria Liakata <https://www.turing.ac.uk/people/researchers/maria-liakata> Queen Mary University of London UK *PUBLICATION* Full papers (not previously published up to 12 pages) and Extended Abstracts (about published or unpublished research up to 4 pages) are welcome. · *Papers *will be included in the conference *proceedings edited by Springer* · *Extended abstracts* will be published in the *Book of Abstracts (with ISBN)* Extended versions will be invited for publication in *special issues of international journals:* o Applied Network Science <https://appliednetsci.springeropen.com/> edited by Springer o Advances in Complex Systems <https://www.worldscientific.com/toc/acs/24/02> edited by World Scientific o Complex Systems <https://www.complex-systems.com/about/> o Entropy <https://www.mdpi.com/journal/entropy> edited by MDPI o PLOS <https://journals.plos.org/plosone/> one <https://journals.plos.org/plosone/> o Social Network Analysis and Mining <https://link.springer.com/journal/13278> edited by Springer *SUBMISSION* https://complexnetworks.org/submission/ *TOPICS* *Topics include, but are not limited to: * o Models of Complex Networks o Structural Network Properties and Analysis o Epidemics o Community Structure o Motifs o Network Mining o Network embedding o Machine learning with graphs o Dynamics and Evolution Patterns of Complex Networks o Link Prediction o Multilayer Networks o Controllability o Synchronization o Visual Representation o Large-scale Graph Analytics o Social Reputation, Influence, and Trust o Information Spreading in Social Media o Rumour and Viral Marketing in Social Networks o Recommendation Systems o Financial and Economic Networks o Mobility o Biological and Technological Networks o Mobile call Networks o Bioinformatics o Earth Sciences Applications o Resilience and Robustness of Complex Networks o Networks for Physical Infrastructures o Networks, Smart Cities and Smart Grids o Political networks o Supply chain networks o Complex networks and information systems o Complex networks and CPS/IoT o Graph signal processing o Cognitive Network Science o Network Medicine o Network Neuroscience o Quantifying success through network analysis o Temporal and spatial networks o Historical Networks o Graph-Based Natural Language Processing *GENERAL CHAIRS* Hocine Cherifi (University of Burgundy, France) Luis M. Rocha (Binghamton University, USA) Join us at COMPLEX NETWORKS 2023 <https://www.complexnetworks.org/> *-------------------------* Hocine CHERIFI University of Burgundy Franche-Comté Laboratoire* I*nterdisciplinaire *C*arnot de *B*ourgogne - ICB UMR 6303 CNRS Editor in Chief Applied Network Science <https://appliednetsci.springeropen.com/> Plos Complex Systems <https://plos.org/complex-systems-research-journal/#:~:text=PLOS%20Complex%2….> Editorial Board member PLOS One <https://journals.plos.org/plosone/>, IEEE ACCESS <https://ieeeaccess.ieee.org/?http%3A%2F%2Fieeeaccess_ieee_org%2F>, Scientific Reports <https://www.nature.com/srep/>, Journal of Imaging <https://www.mdpi.com/journal/jimaging>, Quality and Quantity <https://www.springer.com/journal/11135/>, Computational Social Networks <https://computationalsocialnetworks.springeropen.com/>, Complex Systems <https://www.complex-systems.com/>

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Call for Abstract Submission FRoBio: Freiburg Robotics and Biology Conference
by Timo Oess July 24, 2023

July 24, 2023

Dear all, We are pleased to announce the FRoBio: Freiburg Robotics and Biology Conference - Bringing together nature and technology. The conference will take place on 22nd of November until 24th of November 2023 at the University of Freiburg, Germany. The event brings together international experts of different disciplines, such as biology, chemistry, medicine, materials science, nanotechnology, electrical engineering, mechanical engineering, optics, and robotics to share so far untackled research questions or current challenges, novel methods and approaches that are applicable for multiple fields or interdisciplinary research. Abstract submission is now open for in-person participants and the deadline is September, 8th 2023. The registration for online participants will be open in October 2023. For further information on the conference and In-Person / Online Registration please visit the conference website. <https://www.frias.uni-freiburg.de/de/veranstaltungen/nachwuchskonferenzen/F…> We are looking forward to seeing you at FRoBio in November 2023! Best regards, The Organizing Committee

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Apply to the Allen Institute's OpenScope program
by Anton Arkhipov July 24, 2023

July 24, 2023

Dear Colleagues, Applications are now accepted for the Allen Institute's OpenScope program. Please apply! The OpenScope program, supported by the NIH BRAIN grant U24NS113646, provides the neuroscience community access to the Allen Brain Observatories. The program was inspired by astronomical observatories, which allow a community of scientists to access platforms too large or expensive for most laboratories. Through OpenScope, anyone can propose experiments for Allen Institute staff to run on Allen Institute pipelines. If selected, the proposed experiments are performed with the Allen Institute's verified, reproducible, and open protocols for cutting-edge behavioral training, in vivo Neuropixels electrophysiology, or two-photon calcium imaging. Data produced from the experiments is then shared with applicants, and, eventually, the broader community. How to apply? The program uses a double-blinded proposal review. Individuals or teams draft and submit a proposal, with an applicant commitment to perform data analysis and pursue publication. * RFP released July 10, 2023 * 2pg Letter of Intent Due: September 12th 2023 * 6pg Full Proposal Due: November 7th 2023 * For more details: https://alleninstitute.org/division/mindscope/openscope/ * If any questions or to discuss projects, please reach out to openscope(a)alleninstitute.org<mailto:openscope@alleninstitute.org> New for 2023: Community Proposal Research Questions and Experimental Methods are proposed and voted on by the community in an open, collaborative process. The resulting data is immediately available to the community for anyone to analyze and publish. * Posted on Allen Brain Map Community Forum: https://community.brain-map.org/ * Opportunity announced in June 2023 * Research Question Phase: June-July 2023 * Methods & Analysis Phase: July-September 2023 * Final Vote by the community in November 2023 during SFN Best wishes, Anton. Anton Arkhipov Investigator Allen Institute, Seattle, WA

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