August 2020 - Comp-neuro - lists.cnsorg.org

Up to 6 PhD fellowships in Cognitive Neuroscience at SISSA, Trieste, Italy, to start Oct, 2020
by Alessandro Treves Oct. 12, 2022

Oct. 12, 2022

SISSA is an elite postgraduate research institution located in Trieste, Italy, and focused on Maths, Physics and Neuroscience. It operates in English: many among its students and faculty are not Italian. The Cognitive Neuroscience Department hosts 6 research labs that study the neuronal bases of time and magnitude processing, visual perception, motivation and intelligence, language and reading, tactile perception and learning, and neural computation. The Department is very inter–disciplinary; our approaches include behavioural, psychophysics, and neurophysiological experiments with humans and animals, as well as computational and mathematical models. Students with diverse backgrounds (physics, math, medicine, psychology, biology) are most welcome to apply. The Department has an exceptional track record with highly competitive funds (recent funding includes 4 ERC and 3 Human Frontiers grants) and publication in top journals (Nature, Science, PNAS, Current Biology). Recent placements after PhD include Harvard, MIT, Columbia and Princeton in the US; the NTNU, Manchester and Radboud in Europe; and Sydney University, Australia. Our alumni have been awarded 4 ERC grants, the 2015 Kandel Young Neuroscientist Prize and the 2017 ICTP Prize. More information about the Department is available at our website https://phdcns.sissa.it. The selection procedure is now open; to learn how to apply, please visit https://phdcns.sissa.it/admission-procedure. Please contact the PhD Coordinator (davide.crepaldi(a)sissa.it) and/or your prospective supervisor for more information and informal enquiries. -- Alessandro Treves SISSA, Cognitive Neuroscience, http://people.sissa.it/~ale/limbo.html if in, rm 241, via Bonomea 265, 34136 Trieste, Italy: +39-040-3787623

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Postdoc in neural data analysis
by Mark Humphries June 26, 2022

June 26, 2022

A 3 year postdoc post is available in the lab of Prof Mark Humphries, at the University of Nottingham (UK) In this project, our aim is to test the overarching hypothesis that movement transitions are encoded by the same neural population reconfiguring its joint activity, for both rhythmic and discrete movements. We will tackle this question using newly-available population recordings during rhythmic movement transitions in Aplysia and discrete arm movement transitions in monkeys, The Humphries’ group researches fundamental insights into how the joint activity of neurons encodes information about and actions in the world. For more see: https://www.humphries-lab.org Queries: mark.humphries(a)nottingham.ac.uk Deadline: 5th September Full details, and to apply: https://jobs.nottingham.ac.uk/Vacancy.aspx?id=27005&forced=2 [http://jobs.nottingham.ac.uk/org/images/socialmedia.png]<https://jobs.nottingham.ac.uk/Vacancy.aspx?id=27005&forced=2> Job Vacancy at the University of Nottingham: Research Associate/Fellow (Fixed term)<https://jobs.nottingham.ac.uk/Vacancy.aspx?id=27005&forced=2> Applications are invited for a postdoctoral research fellow to join the Humphries’ group on the MRC-funded project “Uncovering the neural basis of movement transitions”. The Humphries’ group researches fundamental... jobs.nottingham.ac.uk Professor Mark Humphries | MRC Senior non-Clinical Fellow | Chair in Computational Neuroscience Lab: humphries-lab.org<https://www.humphries-lab.org/> Twitter: @markdhumphries Public blog: https://medium.com/the-spike This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please contact the sender and delete the email and attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham. Email communications with the University of Nottingham may be monitored where permitted by law.

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Explainable AI: Overcoming problems of Deep Learning problems with Adaptive Resonance
by Grossberg, Stephen April 9, 2022

April 9, 2022

Dear Colleagues, There's currently a lot of interest in AI, notably Deep Learning, and in achieving autonomous adaptive intelligence. The following new article summarizes foundational problems of reliability and trustworthiness of Deep Learning, explains how Adaptive Resonance overcomes them, and outlines a blueprint for achieving autonomous adaptive intelligence: Grossberg, S. (2020). A path towards Explainable AI and autonomous adaptive intelligence: Deep Learning, Adaptive Resonance, and models of perception, emotion, and action. Frontiers in Neurobotics, June 25, 2020. https://doi.org/10.3389/fnbot.2020.00036 Best, Steve Grossberg

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Postdoc at the University of Leicester to work on human single cell recordings
by Quian Quiroga, Rodrigo (Prof.) Nov. 23, 2021

Nov. 23, 2021

The Centre for Systems Neuroscience at the University of Leicester, UK, offers a 3-years postdoc position to study single cell recordings in humans – performed in epileptic patients for curative surgery. The project will investigate concept cells (a.k.a. Jennifer Aniston neurons) – that means, neurons in the hippocampus and surrounding areas that selectively fire to specific concepts, like different pictures and the written or spoken name of a particular person (e.g. Quian Quiroga et al, Nature 2005; Quian Quiroga Nature Reviews Neuroscience 2012), which are involved in declarative memory functions. For more details see: www.le.ac.uk/csn The successful candidate will contribute to performing recordings with patients in collaborating hospitals and the analysis of the data to investigate the role of these neurons in memory. He/She should have very good quantitative skills (data processing, and programing e.g. in Matlab) and a strong background or interest in neuroscience. He/She should be able to work both independently and as part of a research team within the Centre for System Neuroscience at Leicester and with our external collaborators. Informal enquiries are welcome and should be made to Paula Warrington, Research Centre Manager, csn(a)le.ac.uk or 0116 252 3249. To apply, please use this link: https://jobs.le.ac.uk/vacancies/1393/research-associate.html The applications should include a curriculum vitae and a brief description of their scientific interests The closing date for the applications is 21st October 2019. Cheers, Rodrigo Rodrigo Quian Quiroga Centre for Systems Neuroscience University of Leicester www.le.ac.uk/csn

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Postdoc (3 yrs) in dynamical reconfigurations of neural populations during movement
by Mark Humphries March 9, 2021

March 9, 2021

A 3 year postdoc post is available in the lab of Prof Mark Humphries, at the University of Nottingham (UK) In this project, our aim is to test the overarching hypothesis that movement transitions are encoded by the same neural population reconfiguring its joint activity, for both rhythmic and discrete movements. We will tackle this question by developing new computational algorithms for dynamic systems, and applying them to newly-available population recordings during rhythmic movement transitions in Aplysia and discrete arm movement transitions in monkeys, The Humphries’ group researches fundamental insights into how the joint activity of neurons encodes information about and actions in the world. For more see: https://www.humphries-lab.org Queries: mark.humphries(a)nottingham.ac.uk Deadline: 10th January 2020 Full details, and to apply: https://www.nottingham.ac.uk/jobs/currentvacancies/ref/SCI182819X1 Professor Mark Humphries | MRC Senior non-Clinical Fellow | Chair in Computational Neuroscience Lab: humphries-lab.org<https://www.humphries-lab.org/> Twitter: @markdhumphries Public blog: https://medium.com/the-spike This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please contact the sender and delete the email and attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham. Email communications with the University of Nottingham may be monitored where permitted by law.

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Announcing the 1st Brain-Computer Interface Un-Conference (BCI-UC)
by Moritz Grosse-Wentrup Feb. 5, 2021

Feb. 5, 2021

Dear Colleagues: We cordially invite you to the 1st Brain-Computer Interface Un-Conference (BCI-UC)! https://bciunconference.univie.ac.at/ The 1st BCI-UC is an entirely virtual event that takes place on July 23rd 2020 from 3 pm to 9:30 pm (CEST). It differs in various ways from traditional conferences in our field: * All presentations are publicly streamed via Crowdcast. * Anybody can apply for a presentation slot by submitting an abstract. All registered attendees can up-vote their favorite abstracts. In this way, the whole community decides which presentations they would like to see presented at the conference. * Voting commences on Monday, June 29th at 8 am (CEST) and ends on Thursday, July 16th at 12 midnight (CEST). The authors of the top-rated abstracts are invited to present their work at the conference. * Registration (free of charge) is required for submitting an abstract and to up-vote submissions. In addition to the contributed talks, the 1st BCI Un-Conference features two keynotes presentations: 1. Mariska J Vansteensel (UMC Utrecht Brain Center): Towards the Daily Life Implementation of ECoG-BCIs 2. Christian Herff (Maastricht University): Decoding Speech from Invasive EEG Please join us in exploring this new way of disseminating research in our community! The BCI-UC Organizing Committee: Moritz Grosse-Wentrup Anja Meunier Philipp Raggam Anita Resch 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/

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Funded PhD and Data scientist positions
by Abhishek Banerjee Jan. 14, 2021

Jan. 14, 2021

Dear colleagues, Two funded *Ph.D. positions* are available in the newly established Adaptive Decision-making lab as a part of Neuroscience, Neurodisability and Neurological Disorders research theme, Biosciences Institute, Newcastle University, UK. The research carried out as part of th Ph.D fellowship will investigate the role of behaviourally relevant neural circuits in decision-making and its dysfunctions in neurological disorders using experimental and theoretical approaches (PNAS 2016; BRAIN 2019; bioRxiv 2020). Exciting local and international collaborations will ensure a high standard of training and mentorship. The position is funded (UK/EU rate) for up to 3.5 years and the application deadline is *6th January 2020*. Application details can be found here or by email provided below: https://www.findaphd.com/phds/project/mrc-dimen-doctoral-training-partnersh… . Additionally, the Banerjee Lab is also hiring a *Data Scientist* to be involved in projects that focus on understanding cortical computation during behaviour by combining large-scale recordings and imaging of neuronal populations. This person will be involved in analysing experimental data, developing software tools, and helping with data infrastructure. The application deadline is *31st January 2020*. Qualifications for the Data scientist position: - M.Sc. degree (or exceptional B.Sc.) in Physics, Computer Science, Mathematics, Engineering, or related technical field. - Programming experience in MATLAB is required, and additional experience in Python and LABView is preferred. - Background in computational neuroscience, image analysis, or machine learning is a plus. For informal inquiry, or if interested in applying for the Data scientist position, please submit a CV along with names of 3 referees to: Dr. Abhi Banerjee <abhi.banerjee(a)newcastle.ac.uk>. More details here: https://loop.frontiersin.org/people/11499/overview and http://neuronic.mit.edu/ Best wishes, Abhi -- Abhishek Banerjee Senior Lecturer Head, Adaptive Decision-making Lab Biosciences Institute Newcastle University United Kingdom I neuronic.mit.edu Skype: amiappu I @abhii_mit

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Creating a list of important computational predictions
by Yiota Poirazi Jan. 4, 2021

Jan. 4, 2021

Dear all, I believe you would agree that, the greatest recognition of our computational work comes with the experimental verification of our computational predictions. To highlight the most important outcomes of our theoretical work and facilitate a closer interaction with experimental labs so as to increase the chances of our predictions being experimentally tested, I would like to encourage you to nominate your favorite prediction at the following google form: https://docs.google.com/forms/d/e/1FAIpQLSeJZ2RrXJ0nZonIuyNzN0Cl gXDKDb7xTzoJOiqZTGxm9Kh7ow/viewform?c=0&w=1&usp=mail_form_link This effort started during the CNS 2017 meeting in Antwerp with the support of the OCNS Board. The resulting list will be posted on the CNS 2017 web site and disseminated in social media, as a reference for experimentalists. Thank you very much for supporting this important effort! Best wishes, Yiota Poirazi -- Panayiota Poirazi, Ph.D. Research Director Computational Biology Laboratory Institute of Molecular Biology and Biotechnology (IMBB) Foundation of Research and Technology-Hellas (FORTH) Vassilika Vouton, P.O.Box 1385, GR 70013, Heraklion, Crete GREECE Tel: +30 2810 391139 Fax: +30 2810 391101 Εmail: poirazi(a)imbb.forth.gr Lab site: www.dendrites.gr FENS-Kavli Network of Excellence <http://www.fens.org/Outreach/FENS-Kavli-Network-of-Excellence/> -- This message has been scanned for viruses and dangerous content by *E.F.A. Project* <http://www.efa-project.org>, and is believed to be clean. Click here to report this message as spam. <http://mailgate1.imbb.forth.gr/cgi-bin/learn-msg.cgi?id=4BCD7A00A1.A57D7&to…> -- Panayiota Poirazi, Ph.D. Research Director Computational Biology Laboratory Institute of Molecular Biology and Biotechnology (IMBB) Foundation of Research and Technology-Hellas (FORTH) Vassilika Vouton, P.O.Box 1385, GR 70013, Heraklion, Crete GREECE Tel: +30 2810 391139 Fax: +30 2810 391101 Εmail: poirazi(a)imbb.forth.gr Lab site: www.dendrites.gr FENS-Kavli Network of Excellence <http://www.fens.org/Outreach/FENS-Kavli-Network-of-Excellence/>

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ANDA 2020 - G-Node Advanced Neural Data Analysis Course, April 14-30, 2020, Jülich, Germany
by Thomas Wachtler Dec. 23, 2020

Dec. 23, 2020

ANDA 2020 - G-NODE ADVANCED NEURAL DATA ANALYSIS COURSE April 14 - 30, 2020 Haus Overbach, Jülich-Barmen, Germany Techniques to record neuronal data from populations of neurons are rapidly improving. Simultaneous recordings from hundreds of channels are possible while animals perform complex behavioral tasks. The analysis of such massive and complex data becomes increasingly challenging. This advanced course aims at providing deeper training in state-of-the-art analysis approaches in systems neuroscience. The course is addressed to excellent master and PhD students and young researchers who are interested in learning advanced techniques in data analytics and in getting hands-on experience in the analysis of electrophysiological data. Internationally renowned researchers will give lectures on statistical data analysis and data mining methods with accompanying exercises. Students will define and perform their own analyses on provided data to solve a challenge. Participants are required to have a strong interest in data analysis, a background in a mathematical or related field, knowledge of algebra, matrix operations, and statistics, and need to have solid programming experience (preferably in Python). FACULTY · Moshe Abeles, Hebrew Univ. Jerusalem, Israel · Michael Denker, Jülich Research Center, Germany · Udo Ernst, Univ. Bremen, Germany · Sonja Grün, Jülich Research Center, Germany · Adam Kohn, Albert Einstein College of Medicine, New York, USA · Jakob Macke, TU Munich, Germany · Luca Mazzucato, Univ. of Oregon, Eugene, USA · Martin Nawrot, Univ. of Cologne, Germany · Yifat Prut, Hebrew Univ. Jerusalem, Israel · Hansjörg Scherberger, German Primate Center, Göttingen, Germany · Thomas Wachtler, LMU Munich, Germany TOPICS COVERED Single neuron properties and statistics · Stochastic processes · Surrogate methods · Detection of spatio-temporal patterns · Unitary Events · Statistical analysis of massively parallel spike data · Higher-order correlation analyses · Elephant toolbox · Spike-LFP relationship · Population coding · State space analysis · Machine learning · Data mining · Research data management and reproducibility REQUIREMENTS Applicants should be familiar with linear algebra, probability, differential and integral calculus and experienced using Python or Matlab. Preparatory reading material will be provided. Students should bring their own laptops and should be able to install software on their system. Students that do not have a suitable laptop should indicate this immediately after acceptance to the course. We will be able to provide a small number of laptops for the time of the course. COURSE FEE A course fee of 1000 Euros will be charged to cover costs for accommodation and meals. Limited financial support may be available for students that otherwise would not be able to attend, which is to be indicated in the application. HOUSING Accommodation in 2-bed rooms for students will be provided at the course site. HOW TO APPLY The application should include · a letter of motivation (max 1 page) · curriculum vitae (please indicate the relevant courses you have taken) · description of programming experience · a letter of recommendation. Please send all documents as a single PDF file to <advanced-course(a)g-node.org>. APPLICATION DEADLINE Applications must be received by NOVEMBER 15, 2019. Early application is encouraged. For further information see http://www.g-node.org/anda

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SMILES virtual workshop @ICDL 2020
by Xavier Hinaut Oct. 28, 2020

Oct. 28, 2020

The SMILES (Sensorimotor Interaction, Language and Embodiment of Symbols) Workshop will take place virtually at the ICDL 2020 (International Conference on Developmental Learning). * Call for abstracts and papers: - Deadline: 25th of September 2020 - papers abstracts: 2 pages - long papers: 4-6 pages - Submissions: smiles.conf(a)gmail.com <mailto:smiles.conf@gmail.com> - Papers format: same as ICDL conference https://cis.ieee.org/publications/t-cognitive-and-developmental-systems/tcd… <https://cis.ieee.org/publications/t-cognitive-and-developmental-systems/tcd…> - Workshop dates (coming soon): the days before or after the ICDL conference (26-30 October 2020) * Workshop Short Description On the one hand, models of sensorimotor interaction are embodied in the environment and in the interaction with other agents. On the other hand, recent Deep Learning development of Natural Language Processing (NLP) models allow to capture increasing language complexity (e.g. compositional representations, word embedding, long term dependencies). However, those NLP models are disembodied in the sense that they are learned from static datasets of text or speech. How can we bridge the gap from low-level sensorimotor interaction to high-level compositional symbolic communication? The SMILES workshop will address this issue through -an interdisciplinary approach involving researchers from (but not limited to): - Sensori-motor learning, - Emergent communication in multi-agent systems, - Chunking of perceptuo-motor gestures (gestures in a general sense: motor, vocal, ...), - Sensori-motor learning, - Symbol grounding and symbol emergence, - Compositional representations for communication and action sequence, - Hierarchical representations of temporal information, - Language processing and acquisition in brains and machines, - Models of animal communication, - Understanding composition and temporal processing in neural network models, and - Enaction, active perception, perception-action loop. * More info - contact: smiles.conf(a)gmail.com <mailto:smiles.conf@gmail.com> - organizers: Xavier Hinaut, Clement Moulin-Frier, Silvia Pagliarini, Joni Zhong, Loo CHU KIONG, Michael Spranger, Tadahiro Taniguchi - invited speakers (coming soon) - workshop website (comming soon): https://sites.google.com/view/smiles-workshop/ <https://sites.google.com/view/smiles-workshop/> - ICDL conference website: https://cdstc.gitlab.io/icdl-2020/ <https://cdstc.gitlab.io/icdl-2020/> * Workshop Long Description Recently Deep Learning networks have broken many benchmarks in Natural Language Processing (NLP), e.g. [1]. Such breakthroughs are realized by a few mechanisms (e.g. continuous representations like word embedding, attention mechanism, ...). However, no equivalent finding happened towards the understanding of which mechanisms enable the brain to perform similar functions. Deep Learning does not reproduce learning mechanisms nor the dynamics of the brain. The brain needs to parse incoming stimuli and learn from them incrementally, it cannot unfold time like deep learning algorithms such as Back-propagation through time (BPTT). Thus, we still lack the key neuronal mechanisms needed to properly model the (hierarchies of) functions in language perception and production. Other models of language processing reproducing the behavior of brain dynamics (Event-Related-Potentials (ERPs) [2] or functional Magnetic Resonance Imaging (fMRI) [3]) have been developed. However, such model softens lack explanatory power demonstrating the causes of such observed dynamics: i.e. what is computed and why is it computed – for which purpose? We need more biologically plausible learning mechanisms while producing causal explanations of the experimental data modelled. There is converging evidence that language production and comprehension are not separated processes in a modular mind, they are rather interwoven, and this interweaving is what enables people to predict themselves and each other [4].Interweaving of action and perception is important because it allows a learning agent (or a baby) to learn from its own actions: for instance, by learning the perceptual consequences(e.g. the heard sounds) of its own actions (e.g. vocal productions) during babbling [5]. Thus, the agent learns in a self-supervised way instead of relying only on supervised learning, which in contrast, imply non-biological teacher signals cleverly designed by the modeler. Explicit neuronal models explaining which are the mechanisms shaping these perceptuo-motor units through development are missing. The existence of sensorimotor (i.e. mirror) neurons at abstract representation levels (often called action-perception circuits [6]), jointly with the perceptuo-motor shaping of sensorimotor gestures [5], suggest the existence of similar action-perception mechanisms implemented at different levels of hierarchy. How could we go towards such hierarchical architectures based on action-perception mechanisms? Importantly, a language processing model needs a way to acquire the semantics of the (symbolic) perceptuo-motor gestures and of the more abstract representations, otherwise it would consider only morphosyntactic and prosodic featuresof language. These symbolic gestures, i.e. signs, need to be grounded to the mental concept, i.e. signified, they are representing. Several theories and robotic experiments give examples of how symbols could be grounded or how symbols could emerge [7]. However, current neurocomputational models aiming to explain brain processes are not grounded. Robotics have an important role here for the grounding of semantics by experiencing the world through interactions with the physical world and with humans. We need mechanisms that start from raw sensory perception and raw motor commands in order to let emerge plausible representations through development, instead of arbitrary representations. Finally, computational models of emergent communication in agent populations are currently gaining interest in the machine learning community [8], [9]. These contributions show how a communication system can emerge to solve cooperative tasks in sequential environments. However, they are still relatively disconnected from the earlier theoretical and computational literature aiming at understanding how language might have emerged from a prelinguistic substance [10], [11]. We need to conceive communication as the emergent result of a collective behavior optimization process and to ground the resulting computational models into the theoretical literature from language evolution research. The SMILES workshop will aim at discussing each of the questions mentioned above, together with original approaches on how to integrate them. [1] J. Devlin et al., 2018. [2] H. Brouwer and J. C. J. Hoeks, 2013. [3] M. Garagnani et al. 2008. [4] M. J. Pickering and S. Garrod, 2013. [5] J.-L. Schwartz et al., 2012. [6] F. Pulvermüller and L. Fadiga, 2010. [7] T. Taniguchi et al., 2016. [8] S. Sukhbaatar et al.,2016. [9] I. Mordatch and P. Abbeel, 2017. [10] M. Tomasello et al., 2005. [11] P.-Y. Oudeyer and L. Smith, 2015. Xavier Hinaut Inria Researcher (CR) Mnemosyne team, Inria LaBRI, Université de Bordeaux Institut des Maladies Neurodégénératives +33 5 33 51 48 01 www.xavierhinaut.com

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