- Comp-neuro - lists.cnsorg.org

Postdoc positions at Harvard

by Cengiz Pehlevan

Dear colleagues, I want to point to a few postdoctoral fellow positions at Harvard for candidates in computational/theoretical neuroscience: 1. The George F. Carrier Postdoctoral Fellowship: https://academicpositions.harvard.edu/postings/11642 <https://academicpositions.harvard.edu/postings/11642> 2. Postdoctoral Fellow in Theoretical Neuroscience and/or Theory of Neural Computation: https://academicpositions.harvard.edu/postings/11671 <https://academicpositions.harvard.edu/postings/11671> 3. The Harvard Data Science Postdoctoral Fellows: https://datascience.harvard.edu/data-science-postdoctoral-fellows <https://datascience.harvard.edu/data-science-postdoctoral-fellows> 4. Postdoctoral fellow in the Foundations of Machine Learning: https://academicpositions.harvard.edu/postings/11762 <https://academicpositions.harvard.edu/postings/11762> Please feel free to write to me with questions. Best wishes, Cengiz

Oct. 25, 2022

INCF/OCNS SoftwareWG Dev session: Denis Alevi: Brian2CUDA: November 3, 2022, 1600 UTC over Zoom

by Ankur Sinha

Dear all, Please join us for our next dev session on Brian2CUDA: https://ocns.github.io/SoftwareWG/2022/10/18/dev-session-denis-alevi-brian2… Denis Alevi will introduce the Brian2CUDA tool in this session, and discuss its development. We will also have a discussion on GPU based simulation in neuroscience after the presentation. - Date: Thursday, November 3, 2022, 1600 UTC (Click here to see your local time: https://www.timeanddate.com/worldclock/fixedtime.html?msg=Dev+session%3A+De…) - Location (Zoom, login required): https://ucl.zoom.us/j/95692778384?pwd=VldIQ3hPTU1zczNpYjQxSSt4Z25xdz09 The abstract for the talk is below: Graphics processing units (GPUs) are widely available and have been used with great success to accelerate scientific computing in the last decade. These advances, however, are often not available to researchers interested in simulating spiking neural networks, but lacking the technical knowledge to write the necessary low-level code. Writing low-level code is not necessary when using the popular Brian simulator, which provides a framework to generate efficient CPU code from high-level model definitions in Python. Here, we present Brian2CUDA, an open-source software that extends the Brian simulator with a GPU backend. Our implementation generates efficient code for the numerical integration of neuronal states and for the propagation of synaptic events on GPUs, making use of their massively parallel arithmetic capabilities. We benchmark the performance improvements of our software for several model types and find that it can accelerate simulations by up to three orders of magnitude compared to Brian’s CPU backend. Currently, Brian2CUDA is the only package that supports Brian’s full feature set on GPUs, including arbitrary neuron and synapse models, plasticity rules, and heterogeneous delays. When comparing its performance with Brian2GeNN, another GPU-based backend for the Brian simulator with fewer features, we find that Brian2CUDA gives comparable speedups, while being typically slower for small and faster for large networks. By combining the flexibility of the Brian simulator with the simulation speed of GPUs, Brian2CUDA enables researchers to efficiently simulate spiking neural networks with minimal effort and thereby makes the advancements of GPU computing available to a larger audience of neuroscientists. References: - Publication: Brian2CUDA: flexible and efficient simulation of spiking neural network models on GPUs: https://www.frontiersin.org/articles/10.3389/fninf.2022.883700/abstract - Documentation: https://brian2cuda.readthedocs.io/en/latest/ - Source code: https://github.com/brian-team/brian2cuda We hope to see you there. On behalf of the INCF/OCNS Software working group, -- Thanks, Regards, Ankur Sinha (He / Him / His) | https://ankursinha.in Research Fellow at the Silver Lab, University College London | http://silverlab.org/ Free/Open source community volunteer at the NeuroFedora project | https://neuro.fedoraproject.org Time zone: Europe/London

Oct. 25, 2022

Upcoming Faculty Opening in Theoretical Neuroscience

by Lin, I-Chun

The Gatsby Computational Neuroscience Unit intends to recruit one Principal Investigator in Theoretical Neuroscience at any academic rank (UCL grades 8 to 10; Lecturer/Group Leader-level to Professorial). We seek candidates who focus on the mathematical underpinnings of adaptive intelligent behaviour in animals or develop mathematical tools and models to understand how neural circuits and systems function. We are particularly interested in researchers who are well placed to take advantage of collaborations within the unit; with our sister institute, the Sainsbury Wellcome Centre for Neural Circuits and Behaviour; and elsewhere at UCL. Gatsby faculty members pursue and develop their own lines of research within the broad remit of the unit’s focus on the mathematical principles of learning, perception and action in brains and machines. For an overview of our current work, please see www.ucl.ac.uk/gatsby/research-0<http://www.ucl.ac.uk/gatsby/research-0>. We maintain a singular and cohesive research culture, emphasising interaction and collaboration both within the unit and beyond. Our faculty members teach and supervise at graduate level, primarily as part of the unit’s international PhD programme. We encourage informal enquiries about this upcoming position. Interest from women and those from an ethnic minority background is particularly welcome, as they are under-represented within UCL at these levels. Please direct enquiries to I-Chun Lin (Scientific Programme Manager, i.lin at ucl.ac.uk). We will be best placed to respond to enquiries that include a short CV and a brief sketch of research interests. www.ucl.ac.uk/gatsby/vacancies<http://www.ucl.ac.uk/gatsby/vacancies> -- I-Chun Lin, PhD Scientific Programme Manager Gatsby Computational Neuroscience Unit, UCL www.ucl.ac.uk/gatsby<http://www.ucl.ac.uk/gatsby> | @GatsbyUCL<https://twitter.com/GatsbyUCL>

Oct. 25, 2022

Three 3-year positions in teaching (general AI) in Groningen, NL

by Herbert Jaeger

The AI Department in the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence (https://www.rug.nl/research/bernoulli/) at the University of Groningen (https://www.rug.nl/) offers three 3-year positions for teaching in general AI, with competitive salaries, in a most welcoming and high-ranked working environment (top-100 university, https://www.rug.nl/about-ug/profile/facts-and-figures/position-internationa…) The application deadline is November 7, 2022. Formal prerequisites are a PhD degree; outstanding holders of a MSc degree will also be considered. Details can be found at https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0… - Herbert Jaeger -- Dr. Herbert Jaeger Professor of Computing in Cognitive Materials Rijksuniversiteit Groningen Faculty of Science and Engineering - CogniGron Bernoulliborg Nijenborgh 9, 9747 AG Groningen office: Bernoulliborg 402 phone: +31 (0) 631 921588 COVID home office phone: +49 (0) 4209 930403 web: www.ai.rug.nl/minds/

Oct. 24, 2022

PhD openings in computational neuroscience, cognitive science, and Mixed Reality

by Sergio Solinas

The Theoretical and Computational Neuroscience Laboratory of Paolo Enrico and Sergio Solinas invites applications for a *Doctoral Student* position at UNISS, University of Sassari, ITALY SARDINIA. Research questions span a range of topics in theoretical/computational neuroscience, cognitive science, EEG recordings involving data analysis, brain function theory, neural network simulations using NEURON or NEST and neural activity visualization using mixed reality approaches. This research project is inscribed within the EBRAINS-ITALY PNRR funding schema for Research Infrastructures. Candidates with backgrounds in one or more than one of the following fields are welcome to apply: mathematics, statistics, artificial intelligence, physics, computer science, engineering, biology, and psychology. Experience with data analysis, proficiency with numerical methods, and familiarity with neuroscience topics and mathematical and statistical methods are desirable. Equally desirable is a curiosity about brain function, a spirit of intellectual adventure, a good social attitude, and sharing and reuse of knowledge. The application will open soon. *Please write to both enrico(a)uniss.it <enrico(a)uniss.it> & smgsolinas(a)uniss.it <smgsolinas(a)uniss.it> for details and updates.* If you wish to apply, please send the following information in one single PDF: 1. a short letter of motivation; 2. statement of research interests, limited to two pages; 3. curriculum vitæ. In all email correspondence, please include the mention “APPLICATION-PHD” in the subject header. The University of Sassari <https://en.uniss.it/> was funded in 1562 and is an active research <https://en.uniss.it/research/research-centres> institution. The city of Sassari <http://turismosassari.it/en/explore/sassari-curiosities/item/535-sassari-on…>a located in the north of Sardinia <https://www.sardegnaturismo.it/en>. The climate <https://weatherspark.com/y/58783/Average-Weather-in-Sassari-Italy-Year-Round> is among the best you can find in Europe. The life cost is relatively cheap if compared to Italy's mainland. The island of Sardinia has national and international connection flights from its three airports: Alghero <https://www.aeroportodialghero.it/>, Olbia <https://www.geasar.it/>, and Cagliari <http://www.sogaer.it/it>.

Oct. 24, 2022

Python Package: SER model

by Fabrizio Damicelli

Dear Computational Neuroscience Community, I would like to point you to a potentially useful Python Package that might be of interest for your research, especially in the realm dynamical models and spreading dynamics on networks. "ser": S(usceptible)E(xcited)R(efractory) model on graphs, a dynamical model of spreading excitations. Code Repository: https://github.com/fabridamicelli/ser As of today, a few people already trust it and the package registers >11K total downloads, ~500/month (pypi.org) and has been used already in a few research projects. A few interesting features: - It is light-weight and fast (`numba` accelerated), i.e. many experiments can be simply run on a laptop - Installation and getting started is easy: `pip install ser` - Example notebooks: https://github.com/fabridamicelli/ser/blob/main/examples The SER model is a classical cellular automaton model used to study all sorts of complex systems, ranging from forest fire dynamics to self-organized criticality in the brain. Despite of the simplicity of its setup, it is capable of generating complex emergent dynamics with absolutely non-trivial higher-order statistical properties. To learn more about the SER model and its applications, you might want to check out these references: - Messe et al. (2018). Toward a theory of coactivation patterns in excitable neural networks. doi.org/10.1371/journal.pcbi.1006084 - Haimovici et al. (2013). Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome. doi.org/10.1103/PhysRevLett.110.178101 - Damicelli et al. (2019). Topological reinforcement as a principle of modularity emergence in brain networks. doi.org/10.1162/netn_a_00085 Check it out and any feedback/suggestions/bug reports are more than welcome (simply open an issue: https://github.com/fabridamicelli/ser/issues) All the best, Fabrizio Damicelli

Oct. 24, 2022

Funded PhD Scholarship in Developmental Neurorobotics at Sheffield Hallam University (UK)

by Di Nuovo, Alessandro

About the Project This scholarship will support the PhD candidate to carry out frontier research in Developmental Neurorobotics, a combination of Neuromorphic Computing and Cognitive Developmental Robotics. The PhD candidate will work with the Professor Alessandro Di Nuovo and his team, who have been awarded funding by the EPSRC for his research in the field. We are looking for an outstanding student to work with the team of a new EPSRC project. The research will pioneer the new developmental neuromorphic paradigm, which will be a synergic combination that will go beyond the limitations of the individual paradigms: neuromorphic computing will provide efficient brain-like resources able to process a more accurate representation of the real world, meanwhile developmental robotics will deliver the missing learning mechanisms for complex applications of neuromorphic spiking neural networks. The short-term objective is to demonstrate feasibility and lay the foundation of a biologically plausible framework to simulate the human-like learning process of numerical and abstract cognition, a fundamental characteristic of human intelligence. The team long-term goal is to create an artificial mind for robots that 'grows up' like a child's brain. This will be underpinned by neuromorphic computing which emulates the deep-lying architecture of the brain and will allow it to interpret and adapt to situations. As well as enabling the creation of robots with a human-like ability to reason, behave and interact the creation of an artificial mind will boost research in life sciences disciplines such as neuroscience by allowing researchers to run biologically realistic simulations to test theories. By simulating information on the inner workings of the brain that could not otherwise be detected, it could enhance our understanding of neurodevelopmental and learning disorders and lead to new treatments. Eligibility Information on entry requirements can be found here<https://www.findaphd.com/common/clickCount.aspx?theid=147190&type=184&DID=6…> The ideal candidate should have a Masters degree in Neuromorphic Computing, Computational Neuroscience, Machine Learning, or closely related disciplines in AI and Robotics, excellent programming skills and experience in interdisciplinary research. How to apply Your application should be emailed to industry-innovation-admissions(a)shu.ac.uk<javascript:void(0)> by the closing date of 31 October 2022. Any interested candidates must contact the lead academic, Prof. Alessandro Di Nuovo, a.dinuovo(a)shu.ac.uk<javascript:void(0)>, to discuss your application. The application should explain how the candidate knowledge, skills and experience are relevant to the project short and long-term objectives. Start date for studentship: 01/02/2023 Interviews are scheduled for: 11/11/2022 For information on how to apply please visit https://www.shu.ac.uk/research/degrees<https://www.findaphd.com/common/clickCount.aspx?theid=147190&type=184&DID=6…> ________________________________ Funding Notes The PhD studentship provides tuition fees at UK level and a maintenance bursary at the UK Research Councils' national minimum doctoral stipend rate (£17,668 for 2022/23). The scholarship is available for three years of full-time. ________________________________ References Di Nuovo, A., McClelland (2019). Developing the knowledge of number digits in a child-like robot. Nature Machine Intelligence, 1(12), 594-605. Di Nuovo, A., & Jay, T. (2019). Development of numerical cognition in children and artificial systems: a review of the current knowledge and proposals for multi-disciplinary research. Cognitive Computation and Systems, 1(1), 2-11. Di Nuovo, A., & Cangelosi, A. (2021). Abstract Concept Learning in Cognitive Robots. Current Robotics Reports, 2(1), 1-8. Roy, Jaiswal, Panda, (2019). Towards spike-based machine intelligence with neuromorphic computing. Nature, 575(7784), 607-617. Krichmar (2018). Neurorobotics-A Thriving Community and a Promising Pathway Toward Intelligent Cognitive Robots. Frontiers in Neurorobotics, Vol. 12, p. 42. Furber (2016). Large-scale neuromorphic computing systems. Journal of Neural Engineering, 13(5), 51001. Sengupta, et al. (2019). Going deeper in spiking neural networks: VGG and residual architectures. Frontiers in Neuroscience, 13, 95. Quax, D'Asaro,van Gerven, (2020). Adaptive time scales in recurrent neural networks. Scientific Reports, 10(1), 11360. Alessandro Di Nuovo, PhD Professor of Machine Intelligence College of Business, Technology and Engineering Sheffield Hallam University, United Kingdom Email: a.dinuovo(a)shu.ac.uk<mailto:a.dinuovo@shu.ac.uk>

Oct. 23, 2022

Faculty position in computational neuroscience/machine learning at Stony Brook University

by Giancarlo La Camera

Stony Brook University is currently running a faculty search for a computational neuroscientist. The position will be in the Department of Neurobiology and Behavior and will be filled at the Assistant or Associate Professor level. A competitive recruitment package will include state-funded salary and benefits, newly renovated lab space and competitive start-up funding. The ideal candidate is an outstanding scientist in theoretical neuroscience, neuro-AI or in statistics/machine learning approaches to investigate topical questions in neuroscience. Candidates from diverse and underrepresented backgrounds are especially encouraged to apply. The successful candidate will join a vibrant, collaborative and diverse group of neuroscientists at Stony Brook University and its affiliated institutions, and will actively contribute to the Department’s mission in research as well as undergraduate, graduate and medical student education. Applications should be submitted at the following URL: https://apply.interfolio.com/111422. Review of applications will begin by Nov 15th, 2022 and continue until the position is filled. -- Giancarlo La Camera <https://www.stonybrook.edu/commcms/neurobiology/people/faculty_Giancarlo_La…>, PhD Associate Professor of Neurobiology and Behavior Director, Center for Neural Circuit Dynamics <https://www.stonybrook.edu/cncd/> Stony Brook University

Oct. 22, 2022

Brain Criticality 2022 Hybrid Meeting - Deadline October 23rd for virtual registration

by BATTAGLIA Demian

*** LAST DAYS TO REGISTER FOR VIRTUAL PARTICIPATION AND SUBMIT VIRTUAL POSTER ABSTRACT*** We are happy to announce that the next conference on “Criticality in Neural Systems 2022: Collective Behavior, Synchronization, and Complexity” will take place at the Porter Neuroscience Research Center (National Institute of Health, Bethesda, MD, USA) from Nov. 7 – 9th, 2022 (shortly before the SFN Meeting, San Diego) The conference will be held in a hybrid format, both in presence at NIH Bethesda and via Zoom. Presentations by the invited speakers, with ample time for discussions, including with online participants, will be complemented by a poster session (in presence and online) and by a selection of contributed spotlight presentations. Keeping line with previous editions of the Brain Criticality conferences series, this conference will present the latest experimental and theoretical advancements in the study of critical phenomena in brain activity and their functional relevance. While preserving emphasis on criticality, this edition will feature as well communications about general collective behavior and dynamic complexity in the brain (and other biological systems). REGISTRATION IS OPEN AND FREE ON https://braincriticality.org/ Deadline for virtual registration: October 23rd Deadline for abstract submission (with spotlight oral preference): October 9th Deadline for abstract submission (poster only): October 23rd Confirmed Speakers Ad Aertsen (Freiburg, Germany) Dante Chialvo (Buenos Aires, Argentina) Karin Dahmen (Urbana-Champaign, IL) Irene Giardina (Rome, Italy) Michelle Girvan (College Park, MD) Moritz Helias (Jülich, Germany) Henrik Jensen (London, UK) Arvind Kumar (Stockholm, Sweden) Anna Levina (Tübingen, Germany) Klaus Linkenkaer-Hansen (Amsterdam, the Netherlands) Simona Olmi (Sesto Fiorentino, Italy) Orit Peleg (Boulder, CO) Pawel Romanczuk (Berlin, Germany) Woodrow Shew (Fayetteville, AR) James Shine (Sidney, Australia) Samir Suweis (Padova, Italy) Enzo Tagliazucchi (Buenos Aires, Argentina) Bradley Voytek (La Jolla, CA) Ralf Wessel (St Louis, MO) Changsong Zhou (Hong Kong, Popular Republic of China) Organizers Dietmar Plenz (Bethesda, MD) Lucilla de Arcangelis (Caserta, Italy) Miguel Angel Munoz (Granada, Spain) Mauro Copelli (Recife, Brazil) Demian Battaglia (Marseille & Strasbourg, France) For more information visit our website https://braincriticality.org/ or contact us over email.

Oct. 21, 2022

Computational Neuroscience and Computational Neurology

by André Fabio Kohn

Computational Neuroscience has shown a tremendous development in the last decades, and we think it is ripe to contribute substantially to computational neurology. In selected cases, large-scale and multi-scale models encompassing sensory receptors, neurons, muscles, bones, joints and other structures can be modeled to represent different degrees of dysfunction of a specific subsystem associated with a given neurological pathology. The model simulations can shed light to early signs of the disease (earlier detection than with current diagnosis techniques) and provide estimates of the progression of disease as a function of different treatment courses. We show in a recent paper published in the Journal of Neural Engineering [ https://iopscience.iop.org/article/10.1088/1741-2552/ac91f8/pdf] how neuropathies associated with Guillain-Barré Syndrome can affect functional aspects of the control of foot movement in a controlled setting, which is suitable for clinical evaluation. Muscle electrical activity also was shown to be a useful quantifier of the demyelination process. The multi-scale model encompasses neuronal ionic channels of spinal cord neurons up to a model of the foot and its muscles acting around the ankle joint when controlling force and foot position in a controlled lab experiment. The extension of such an approach to other peripheral nervous disorders or to spinal cord related pathologies (e.g., motoneuron diseases) seems quite feasible, provided that parameter data from patients are available to realize a biologically compatible model. Andre Fabio Kohn, Ph.D., Professor of Biomedical Engineering, University of Sao Paulo

Oct. 20, 2022