- Comp-neuro - lists.cnsorg.org

9th Iberian Conference on Perception, Barcelona, June 2022
by Xavier Otazu Porter Feb. 22, 2022

Feb. 22, 2022

Dear colleague, perception researcher, We are pleased to invite you to attend the 9th Iberian Conference on Perception (CIP 2022), which will be held on June 27th-29th, 2022, in Barcelona, Catalonia, Spain (https://www.cip2022.org<https://www.cip2022.org/>). CIP focuses on perception, with an emphasis on, but no limitation to, several aspects of perception such as motion perception, spatial vision, stereopsis, colour, perception and action, attention and cognition, audition, multisensory integration, reading and speech, brain imaging, art and aesthetics, perception in clinical populations and computational modelling. In addition to talks on this broad range of topics, the conference will include five symposia, poster sessions, and two keynote lectures from young internationally renowned scientists, Dr Iris Groen, from the University of Amsterdam, and Prof Manuel Spitschan, from the Max Planck Institute in Tübingen.. The event will take place at the Casa Convalescència<https://uab-casaconvalescencia.org/es/> in the modernist historical complex of Hospital de la Santa Creu i Sant Pau <https://www.santpaubarcelona.org/> in Barcelona, which was declared a UNESCO World Heritage Site in 1997. The COVID-19 pandemic forced us to break with the biennial organisation of CIP and to cancel the 2021 edition. For this year's edition, the health and safety of our speakers, attendees and staff will be a top priority. We designed the conference around this premise. The venue and program will allow social distancing while on site and regular ventilation of the conference room. All attendees will be asked to continue to wear face masks (unless medically exempt) during the conference and throughout all the premises of the Casa Convalescència. These policies will continue to be reviewed in line with government and local authority updates. We look forward to an exciting scientific programme and rely on the renewed eagerness of the research community to submit abstracts on their recent studies. Abstracts deadline: 15th April Registration: From February 15th to 1st June The Organising Committee Dr Xavier Otazu Dr Xim Cerda-Company Dr C. Alejandro Parraga Dr Olivier Penacchio Computer Vision Center<http://www.cvc.uab.cat> CONFIDENTIALITY WARNING<http://www.cvc.uab.es/?page_id=7475>

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Postdoctoral position in natural language processing and neuroscience, University of Geneva, Switzerland
by Timothée Proix Feb. 21, 2022

Feb. 21, 2022

Dear all, a postdoctoral position is available under the supervision of Prof. Pierre Mégevand (https://www.unige.ch/medecine/neucli/en/groupes-de-recherche/1034megevand/) and Timothée Proix (https://ndlab.ch/) at the University of Geneva, Switzerland. The project lies at the interface between natural language processing and intracranial EEG data analysis. The goal of the project is to understand the neural representations of speech production and perception in conversations. The candidate will contribute to acquisition and analysis of intracranial EEG data and speech recordings. Applicants should have prior experience with machine learning methods for natural language processing, as well as in advanced data analysis techniques. Experience in electrophysiological recordings and knowledge in neuroscience is a significant advantage. The research will be performed at the campus Biotech in the university of Geneva, in collaboration with the Geneva University Hospital. The positions are funded by the private foundation of the Geneva University Hospital, and salaries are in compliance with guidelines of the University of Geneva. The ideal start is May 2022, with some flexibility. To apply, send a motivation letter, CV, and contact information for 2-3 references, as well as any other relevant documents to the Prof. Pierre Mégevand (pierre.megevand (at) unige.ch) as well as Timothée Proix (timothee.proix (at) unige.ch). Screening of applications will start by March 2022 and will continue until the position is filled. Timothée Proix

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NSF Neuronex Big Brain Imaging Hybrid Workshop @ Boston University - April 7-8, 2022
by Chen, Jerry Feb. 21, 2022

Feb. 21, 2022

We are proud to announce the 2022 Big Brain Imaging Workshop hosted jointly by the NSF NeuroNex Nemonic Hub as well as the Boston University Neurophotonics Center! This two-day hybrid workshop will take place at Boston University and online on Thursday, April 7th and continue on Friday, April 8th, 2022. The 2022 Big Brain Imaging Workshop is an opportunity to join in the discussion on optical approaches for cellular resolution imaging of neuronal populations that have been highly instrumental for uncovering the principles of neural circuit function. To date, the microscope technology development for imaging the nervous system has largely been focused on applications on a subset of model species with smaller, optically accessible brains (ex. c. elegans, zebrafish, mice). The ability to translate these technologies to species with brains that are both physically larger and less optically accessible remains a challenge. The topic of the workshop is focused on advancing cellular resolution brain imaging techniques in "larger" brains of mammalian species (ex. rats, cats, ferrets, marmosets, macaques). The goal is to bring neuroscientists and optical engineers together to discuss challenges and come up with potential solutions that will pave the way for the next generation of microscopes that can be applied to a greater number of model organisms. Invited Speakers Daniel Aharoni - University of California, Los Angeles Adam Charles - Johns Hopkins University Anna Devor - Boston University David Fitzpatrick - Max Planck Institute Florida Emily Gibson - University of Colorado Anschutz Prakash Kara - University of Minnesota Jerome Mertz - Boston University Kristina Nielsen - Johns Hopkins University Anitha Pasupathy - University of Washington Bijan Perasan - New York University Nicholas Priebe - University of Texas, Austin Eyal Seidemann - University of Texas, Austin Lei Tian - Boston University Alipasha Vaziri - Rockefeller University Chris Xu - Cornell University In addition to the invited speakers, spots are available for short talks by visiting the registration page and submitting a brief abstract: For more information and free registration, visit: https://nemonic.ece.ucsb.edu/index.php/Big_Brain Jerry Chen, PhD Assistant Professor Department of Biology Boston University www.chen-lab.org<http://www.chen-lab.org> jerry(a)chen-lab.org<mailto:jerry@chen-lab.org> Jerry Chen, PhD Assistant Professor Department of Biology Boston University www.chen-lab.org<http://www.chen-lab.org> jerry(a)chen-lab.org<mailto:jerry@chen-lab.org>

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postdoc in Computational Neuroscience, Cambridge-Columbia collaboration
by Máté Lengyel Feb. 18, 2022

Feb. 18, 2022

Postdoc in Computational Neuroscience, Cambridge-Columbia collaboration We are seeking a highly creative and motivated postdoctoral fellow to work on a collaborative project between the labs of Guillaume Hennequin and Máté Lengyel at the Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, and Daniel Wolpert at the Zuckerman Mind Brain Behavior Institute, Columbia University. The project studies neural network mechanisms underlying the context-dependent (continual) learning of motor repertoires and ties together several threads of research recently developed in our labs, based on the following key publications (note that several are collaborative between our groups): - JB Heald, M Lengyel, DM Wolpert (2021) Contextual inference underlies the learning of sensorimotor repertoires. Nature 600:489-493. - TC Kao, MS Sadabadi, G Hennequin (2021). Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model. Neuron 109:1567-1581. - TC Kao, KT Jensen, GM van de Ven, A Bernacchia, G Hennequin (2021) Natural continual learning: success is a journey, not (just) a destination. NeurIPS, https://tinyurl.com/2jfyss8c - Echeveste R, Aitchison L, Hennequin G, Lengyel M (2020) Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nature Neuroscience 23:1138-1149. The postdoc will be based in Cambridge, with an opportunity for regular visits to Columbia. The successful candidate will have - a strong quantitative background - demonstrable interest in theoretical neuroscience - obtained (or be close to the completion of) a PhD or equivalent in computational neuroscience, physics, mathematics, computer science, machine learning or a related field Preference will be given to candidates with - previous experience in computational neuroscience, especially with the dynamics of recurrent neural network, and function-optimized neural networks - sufficient programming skills to run numerical simulations and to use large scale optimization packages - expertise with advanced data analysis and Bayesian techniques For further details and to send your application, please go to https://www.jobs.cam.ac.uk/job/33691/. For informal queries, please contact Guillaume Hennequin <g.hennequin(a)eng.cam.ac.uk>, Máté Lengyel <m.lengyel(a)eng.cam.ac.uk> or Daniel Wolpert <wolpert(a)columbia.edu>. Máté Lengyel -- Professor of Computational Neuroscience Computational and Biological Learning Lab Cambridge University Engineering Department Trumpington Street, Cambridge CB2 1PZ, UK tel: +44 (0)1223 748 532, fax: +44 (0)1223 765 587 email: m.lengyel(a)eng.cam.ac.uk web: lengyellab.org

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KogWis2022 | 15th Biannual Conference of the German Cognitive Science Society +++ The call for symposia is open
by Constantin Rothkopf Feb. 18, 2022

Feb. 18, 2022

Event: The 15th Biannual Conference of the German Cognitive Science Society, KogWis2022 Theme: Understanding Minds Date: 5.-7. September 2022 Location: Freiburg im Breisgau, Germany Host: Albert-Ludwigs University Freiburg Web: https://www.kogwis2022.uni-freiburg.de/ Abstract: The 15. biannual Conference of the German Cognitive Science Society (KogWis2022) will take place from 5.-7. September 2022 in Freiburg i.Br., Germany, organized by the Center for Cognitive Science. The theme “Understanding Minds” reflects two perspectives: The conference provides a forum for all topics in the study of how minds – both human and artificial – operate. The theme also puts a specific spotlight on how cognitive systems make sense of the world, in particular in language comprehension and communication. The Call for Symposia for the 15th Biannual Conference of the German Cognitive Science Society, KogWis2022, is now open! https://www.kogwis2022.uni-freiburg.de/ Confirmed keynote speakers are: Dedre Gentner, Northwestern University, Illinois Seana Coulson, University of California, San Diego Marcel Brass, Humboldt University, Berlin Matthew Crocker, Saarland University, Saarbrücken For any inquiries please use the conference e-mail-address kogwis2022(a)cognition.uni-freiburg.de. Further information is available on the conference website: www.kogwis2022.uni-freiburg.de. Organising Committee: Evelyn Ferstl, Lars Konieczny, Rul von Stülpnagel, Lisa Zacharski, Judith Beck

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MBL Methods in Computational Neuroscience Course
by Stephen A. Baccus Feb. 18, 2022

Feb. 18, 2022

Applications are open for the Methods in Computational Neuroscience course at the Marine Biology Laboratory in Woods Hole, MA. The course will run from July 28 to August 24, 2022, and the online application form can be found at: https://www.mbl.edu/education/courses/methods-in-computational-neuroscience/<https://ws2.mbl.edu/studentapp/studentapp.asp?courseid=MCN> The course application deadline is *March 30*. The course covers a range of topics in computational neuroscience including neuronal biophysics, neural coding & information processing, circuit dynamics, learning & memory, motor control, and cognitive processing & disease. In addition, numerous tutorials and problem sets will cover a broad range of computational and mathematical modeling methods. The course strongly emphasizes the collaboration between theory and experiment in solving neuroscience problems, and lectures will be given by a mixture of theorists and experimentalists. The final weeks of the course are primarily reserved for work on projects that students design in collaboration with the resident faculty. Further information can be found on the MCN website: http://www.mbl.edu/mcn/ 2022 Course Directors: Stephen Baccus, Stanford University Xiao Jing Wang, New York University 2022 Faculty: Larry Abbott, Columbia University Tim Behrens, Univ. College London Emery Brown, MIT Randy Buckner, Harvard University Anne Churchland, UCLA Claudia Clopath, Imp. College London Albert Compte, IDIBAPS Shaul Druckmann, Stanford University Uri Eden, Boston University Bard Ermentrout, U. of Pittsburgh Adrienne Fairhall, U. of Washington Michale Fee, MIT James Fitzgerald, Janelia Res., HHMI Loren Frank, UCSF Stefano Fusi, Columbia University Surya Ganguli, Stanford University Paul Glimcher, New York University Mark Goldman, UC Davis Nancy Kopell, Boston University Eve Marder, Brandeis University Yiota Poirazi, IMBB-FORTH David Redish, U. Minnesota Terry Sejnowski, Salk Institute Reza Shadmehr, Johns Hopkins Univ. Sara Solla, Northwestern University Haim Sompolinsky, Hebrew University Nelson Spruston, Janelia Res., HHMI Josh Tenenbaum, MIT Nao Uchida, Harvard University Greg Wayne, DeepMind

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[jobs] Phd opportunity in Lille, France - Spiking Neural Networks for Video Analysis
by Ioan Marius BILASCO Feb. 17, 2022

Feb. 17, 2022

The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France is looking to recruit a PhD student starting as soon as possible on the following subject : Spiking Neural Networks for Video Analysis The FOX research group is part of the CRIStAL laboratory (University of Lille, CNRS), located in Lille, France. We focus on video analysis for human behavior understanding. Specifically, we develop spatio-temporal models of motions for tasks such as abnormal event detection, emotion recognition, and face alignment. We are also involved in IRCICA (CNRS), a research institute promoting multidisciplanary research. At IRCICA, we collaborate with computer scientists and experts in electronics engineering to create new models of neural networks that can be implemented on low-power hardware architectures. Recently, we designed state-of-the-art models for image recognition with single and multi-layer unsupervised spiking neural networks. We were among the first to succesfully apply unsupervised SNNs on modern datasets of computer vision. We also developed our own SNN simulator to support experiments with SNN on computer vision problems. Our work is published in major journals (Pattern Recognition, IEEE Trans. on Affective Computing) and conferences (WACV, IJCNN) in the field. Abstract: Spiking Neural Network have recently been evaluated on classical image recognition tasks [1]. This work has highlighted their promising performances in this domain and have identified ways to improve them to be competitive with comparable deep learning approaches. In particular, it demonstrated the ability of SNN architectures to learn relevant patterns for static pattern recognition in an unsupervised manner. However, dealing with static images is not enough, and the computer vision community is increasingly interested in video analysis, for two reasons. First, video data is more and more common and corresponds to a wide range of applications (video surveillance, audio-visual productions, autonomous vehicles...). Second, this data is richer than isolated static images, and thus offers the possibility to develop more effective systems, e.g. using motion information. Thus, it is recognized in the community that modeling motion in videos is more relevant than studying visual appearance alone for tasks such as action or emotion recognition. The next step for SNNs is therefore to study their ability to model motion rather than, or in addition to, image appearance. The goal of the Ph.D. candidate will be to explore the use of SNNs for space-time modeling in videos. This work will be targeted towards applications in human behavior understanding and especially action recognition. More specifically, the Ph.D. candidate is expected to: * identify what issues may prevent space-time modeling with SNNs and how they can be circumvented; * propose new supervised and unsupervised SNN models for motion modeling, which are compatible with hardware implementations on ultra-low power devices; * evaluate the proposed models on standard datasets for video analysis. Detailed subject: https://bit.ly/stssnnfox <https://bit.ly/stssnnfox> Candidates must hold a Master degree (or an equivalent degree) in Computer Science, Statistics, Applied Mathematics or a related field. Experience in one or more of the following is a plus: • image processing, computer vision; • machine learning; • bio-inspired computing; • research methodology (literature review, experimentation…). Candidates should have the following skills: • good proficiency in English, both spoken and written; • scientific writing; • programming (experience in C++ is a plus, but not mandatory). This PHD thesis will be funded in the framework of the ANVI-Luxant industrial chair. The general objective of the Chair is to make a scientific and technological progress in the mastery of emerging information processing architectures such as neuromorphic architectures as an embedded artificial intelligence technique. The use-case studies will come from video protection in the context of retail and transportation. The candidate will be funded for 3 years; he/she is expected to defend his/her thesis and graduate by the end of the contract. The monthly gross salary is around 2000€, including benefits (health insurance, retirement fund, and paid vacations). The position is located in Lille, France. With over 110 000 students, the metropolitan area of Lille is one France's top education student cities. The European Doctoral College Lille Nord-Pas de Calais is headquartered in Lille Metropole and includes 3,000 PhD Doctorate students supported by university research laboratories. Lille has a convenient location in the European high-speed rail network. It lies on the Eurostar line to London (1:20 hour journey). The French TGV network also puts it only 1 hour from Paris, 35 mn from Brussels, and a short trips to other major centres in France such as Paris, Marseille and Lyon. For application, please send the following information in a single PDF file to Dr. Marius Bilasco (marius.bilasco(a)univ-lille.fr <mailto:marius.bilasco@univ-lille.fr>) with subject [PhD_Luxant-ANVI]: * A cover letter. * A curriculum vitae, including a list of publications, if any. * Transcripts of grades of Master's degree. * The contact information of two references (and any letters if available). We look forward to receiving your application as soon as possible, but no later than 26.3.2022

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[jobs] PhD opportunity - Spatio-temporal augmentation models for motion pattern learning - AI_PhD@Lille, France
by Ioan Marius BILASCO Feb. 17, 2022

Feb. 17, 2022

The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France is looking to recruit a PhD student starting on October 1st 2022 on the following subject : Spatio-temporal data augmentation models for motion pattern learning using deep learning: applications to facial analysis in the wild The FOX research group is part of the CRIStAL laboratory (University of Lille, CNRS), located in Lille, France. We focus on video analysis for human behavior understanding. Specifically, we develop spatio-temporal models of motions for tasks such as abnormal event detection, emotion recognition, and face alignment. Our work is published in major journals (Pattern Recognition, IEEE Trans. on Affective Computing) and conferences (WACV, IJCNN). Abstract: Facial expression analysis is a well-studied field when dealing with segmented and constrained data captured in lab conditions. However, many challenges must still be addressed for building in-the-wild solutions that account for various motion intensities, strong head movements during expressions, the spotting of the subsequence containing the expression, partially occluded faces, etc. In recent years, learned features based on deep learning architectures were proposed in order to deal with these challenges. Deep learning is characterized by neural architectures that depend on a huge number of parameters. The convergence of these neural networks and the estimation of optimal parameters require large amounts of training data, especially when dealing with spatio-temporal data, particulary adequate for facial expression recognition. The quantity, but also the quality, of the data and its capacity to reflect the addressed challenges are key elements for training properly the networks. Augmenting the data artificially in an intelligent and controlled way is an interesting solution. The augmentation techniques identified in the literature are mainly focused on image augmentation and consist of scaling, rotation, and flipping operations, or they make use of more complex adversarial training. These techniques can be applied at the frame level, but there is a need for sequence level augmentation in order to better control the augmentation process and ensure the absence of temporal artifacts that might bias the learning process. The generation of dynamic frontal facial expressions has already been addressed in the literature. The goal of this Ph.D. is to conceive new space-time augmentation methods for unconstrained facial analysis (involving head movements, occultations, etc.). Attention should be paid in assessing the quality standards related to facial expression requirements: stability over time, absence of facial artifacts, etc. More specifically, the Ph.D. can didate is expected to conceive augmentation architectures that address various challenges (motion intensities, head movements) while maintaining temporal stability and eliminating facial artifacts. More details are available here : https://bit.ly/staugm_motion <http://bit.ly/st_augm_motion> Candidates must hold a Master degree in Computer Science, Statistics, Applied Mathematics or a related field. Experience in one or more of the following is a plus: • image processing, computer vision; • machine learning; • research methodology (literature review, experimentation…). Candidates should have the following skills: • good proficiency in English, both spoken and written; • scientific writing; • programming (experience in C++ is a plus, but not mandatory). This PHD thesis will be funded in the framework of the AI_PhD@Lilleprogram. http://www.isite-ulne.fr/index.php/en/phd-in-artificial-intelligence/ <http://www.isite-ulne.fr/index.php/en/phd-in-artificial-intelligence/> The candidate will be funded for 3 years; he/she is expected to defend his/her thesis and graduate by the end of the contract. The monthly gross salary is around 2000€, including benefits (health insurance, retirement fund, and paid vacations). Additional financial support is expected in the framework of the AI_PhD@Lille program. The position is located in Lille, France. With over 110 000 students, the metropolitan area of Lille is one France's top education student cities. The European Doctoral College Lille Nord-Pas de Calais is headquartered in Lille Metropole and includes 3,000 PhD Doctorate students supported by university research laboratories. Lille has a convenient location in the European high-speed rail network. It lies on the Eurostar line to London (1:20 hour journey). The French TGV network also puts it only 1 hour from Paris, 35 mn from Brussels, and a short trips to other major centres in France such as Paris, Marseille and Lyon. We look forward to receiving your application as soon as possible, but no later than 26.03.2021.

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job: staff scientist/team leader at NIMH
by Francisco Pereira Feb. 17, 2022

Feb. 17, 2022

The National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), is recruiting for a Staff Scientist/Team Leader position to direct the Scientific and Statistical Computing Core/AFNI Team (https://afni.nimh.nih.gov).The primary function of this group is to support multimodal neuroimaging research in the intramural research program (IRP) at NIH. This includes (i) applying, extending, and developing new and existing software tools and techniques to conduct pioneering analyses on a diverse array of neuroimaging modalities (e.g. MRI, DTI, fMRI, MEG, PET, and EEG), (ii) advising and training IRP researchers on neuroimaging analysis methods, and (iii) leading the future growth and development of the AFNI code base ( https://github.com/afni/afni) Primary responsibilities of the Team Leader will include serving as a partner in and facilitator of neuroimaging research within the NIMH IRP; providing guidance on study design; and ensuring that appropriate standards of quality, rigor, and transparency are maintained. They will also act in coordination with the leaders of the Data Science and Sharing ( https://cmn.nimh.nih.gov/dsst) and Machine Learning Teams ( https://cmn.nimh.nih.gov/mlt) Finally, they will be responsible for management and supervision of approximately 9 full time staff members including software engineers, statisticians, and other biomedical science professionals. The successful candidate must be committed to scientific excellence and highly collaborative research, ensuring an immense impact on the fields of brain imaging and mental health research. Candidates must have earned a Ph.D. or M.D. and demonstrate experience as an outstanding scientist. Applicants must possess the ability to analyze, prioritize, and delegate resources while managing multiple projects. Expertise and experience should include demonstration of positive and collegial interactions with staff at all levels and the ability to communicate and promote their research, software, and scholarly contributions. Applicants should also have knowledge and experience in software engineering, including writing, distributing, maintaining, and supporting scientific software for a large user base while adhering to community recognized standards and practices. Please see the official posting https://cmn.nimh.nih.gov/compcore-job for more details, including instructions on how to apply.

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new Open Access Perspective
by Daniel Gardner Feb. 17, 2022

Feb. 17, 2022

What do brains compute, and how do they do it? Has evolution conserved a small canonical set of computations performed by neural circuitry? In a just-published invited Perspective for the Journal of Computational Neuroscience, I present arguments for (and against) these fundamental questions. The title, Where are the cores in this thing? …and what are they computing? (with apologies to Larry Abbott) is of course an homage to Larry's 'Where are the switches on this thing?' It's Open Access and freely downloadable (tell your students and friends) at: https://link.springer.com/content/pdf/10.1007/s10827-021-00809-1.pdf The very first sentence is: "Evolution conserves neural components and connectivity—does this extend to computations and circuitry?" I go on to note that independent of specific microcircuit connectivity, some key neuronal and network properties, conserved across all nervous systems, may be essential for computation. Candidates include irregular firing, stochastic plastic synapses, alternate A/D PSPs/spikes, nonlinear f-I, cellular longevity, dynamic but stable networks with both fixed and adaptive features, and local processing linked by distant connectivity for coherence and modulation. Collegially, I invite discussion. I also urge neuroscientists to add these questions to those that guide their own research, and I offer community efforts to widen the scope. ...Daniel Gardner, Ph.D. dan(a)med.cornell.edu dg458(a)columbia.edu Head, Laboratory of Neuroinformatics Professor Physiology & Biophysics/Neurology/Neuroscience Weill Medical College of Cornell University lab: (212) 746-6373 cell: (917) 902-0654

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