- Comp-neuro - lists.cnsorg.org

Publication of the NeurIPS2019 Reproducibility Challenge reports
by Nicolas P. Rougier May 22, 2020

May 22, 2020

Dear all, The accepted reports for the NeurIPS2019 Reproducibility challenge have just been published at https://rescience.github.io/read (volume 6, issue 2). All articles are open access with open code, data and reviews. Nicolas Rougier ReScience C Editor-in-chief

1 0

Community review of two new standards now open
by Helena Ledmyr May 22, 2020

May 22, 2020

INCF has two new community standards up for public review, and we are now soliciting input from the user community. The standards are the Neuroscience Information Exchange (NIX) and the Neuroimaging Data Model (NIDM)-Results. More information is available here: https://www.incf.org/comment-on-these-sbps *The mission of the INCF is to develop, evaluate, and endorse standards and best practices that embrace the principles of Open, FAIR, and Citable neuroscience. INCF also provides training on how standards and best practices facilitate reproducibility and enables the publishing of the entirety of research output, including data and code.* All the best Helena ----------------------------- Helena Ledmyr, PhD *Director* *Development and Communications* International Neuroinformatics Coordinating Facility Secretariat Karolinska Institutet. Nobels väg 15A, SE-171 77 Stockholm. Sweden Email: helena.ledmyr(a)incf.org Phone: +46 8 524 870 35 incf.org neuroinformatics.incf.org

1 0

Call for papers: Neuroinformatics special issue "Building the NeuroCommons"
by Helena Ledmyr May 22, 2020

May 22, 2020

Neuroinformatics is currently soliciting submissions for a special issue called "Building the NeuroCommons - research and commentary on how neuroscience is becoming open and FAIR". Submissions due *July 31 2020*. As neuroscience increasingly defines and adopts FAIR, the act of publishing in neuroscience will not only serve to communicate new research but drive the population of a robust, more data- and resource-driven ecosystem - the NeuroCommons - on which to build further findings. This special, open-access, issue of Neuroinformatics is dedicated to building the NeuroCommons and will include articles that embody the principles of open and FAIR neuroscience: - FAIRification of neuroscience data, tools and other research objects: What does it take to move neuroscience closer to the ideal of FAIR? - Using public data to drive discovery science - Research infrastructure for neuroscience and how it supports open and FAIR neuroscience - Standards-based neuroscience. When and where have standards been effective? What have we learned about creating standards and gaining adoption? Where are they lacking? - Perspectives and commentary on the concept of the NeuroCommons, e.g., what will it take for neuroscience to become a data-driven discipline based on pools of open research data? What are challenges and opportunities? Where have there been successes? More information and submission at https://www.springer.com/journal/12021/updates/17978796 All the best Helena ----------------------------- Helena Ledmyr, PhD *Director* *Development and Communications* International Neuroinformatics Coordinating Facility Secretariat Karolinska Institutet. Nobels väg 15A, SE-171 77 Stockholm. Sweden Email: helena.ledmyr(a)incf.org Phone: +46 8 524 870 35 incf.org neuroinformatics.incf.org

1 0

Imperial College MRes in Neurotechnology
by Kozlov, Andriy May 21, 2020

May 21, 2020

Imperial College MRes in Neurotechnology Neurotechnology is the use of insights and tools from mathematics, physics, chemistry, biology and engineering to investigate neural function and treat dysfunction. Brain-related illnesses affect more than two billion people worldwide, and the numbers are growing. Reducing this burden is a major challenge for society. The MRes Neurotechnology<http://www.imperial.ac.uk/bioengineering/study/postgraduate-research/mres-n…> is a one-year full-time programme which will provide you with technical knowledge, expertise and transferable skills in this exciting area. Our MRes will prepare you for an innovative research career at the interface between neuroscience and engineering, in academia, industry, or elsewhere. The programme comprises lectures, practical work and workshops in the first term, followed by full-time work on a research project<http://www.imperial.ac.uk/bioengineering/study/postgraduate-research/mres-n…>. In addition, you will attend regular seminars and a journal club throughout the year. Your research project will normally be supervised by a multidisciplinary team of at least two Imperial College London<https://www.imperial.ac.uk> supervisors who will bring technical and neuroscience expertise to the project. Careers The MRes Neurotechnology provides unique training at the interface between neuroscience and engineering, enhancing your skills in research, analysis and scientific presentation and developing your ability to work in a multidisciplinary team. Our programme provides a solid foundation in research for those continuing with a neurotechnology career, whether through a PhD or working in industry. How to apply Please visit our webpage<http://www.imperial.ac.uk/bioengineering/study/postgraduate-research/mres-n…> for full details of the course and how to apply, including entry requirements and projects available for 2020 entry<http://www.imperial.ac.uk/bioengineering/study/postgraduate-research/mres-n…>. The deadline for applications is 15 June, but applications are considered on a rolling basis, so we encourage you to apply now. ******************** Thank you. Best wishes, Andrei Kozlov (Director of the MRes in Neurotechnology)

1 0

DEPSCoR funding opportunity for tenured/tenure-track faculty in select US states/territories
by GREENWALD, HAL S DR-04 USAF AFMC AFOSR/RTA May 20, 2020

May 20, 2020

The Defense Established Program to Stimulate Competitive Research (DEPSCoR) is a capacity building program that is designed to strengthen the research infrastructure at institutions of higher education in underutilized states/territories. Eligible applicants are full-time tenured or tenure-track faculty members at institutions of higher education in the following US states/territories: Alabama, Alaska, Arizona, Arkansas, Connecticut, Delaware, District of Columbia, Guam, Hawaii, Idaho, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Mexico, North Dakota, Oklahoma, Oregon, Puerto Rico, Rhode Island, South Carolina, South Dakota, Tennessee, U.S. Virgin Islands, Vermont, West Virginia, Wisconsin, and Wyoming. Applicants must never have served as the PI on a prior DoD-funded award. Applicants must select a co-PI/collaborator who is a tenured or tenure-track faculty member at an institution of higher education in a DEPSCoR-eligible state/territory and has served as the PI on a DoD-funded research award active between 1 October 2013 and 30 September 2020. Awarded grants may be for up to $200,000 per year for three years. Topic areas: Air Force Office of Scientific Research: 1. Cognitive and Computational Neuroscience 2. Space Science 3. Agile Science of Test and Evaluation 4. Materials with Extreme Properties Army Research Office: 5. Propulsion and Energetics 6. Computational Architectures and Visualization 7. Optoelectronics 8. Probability and Statistics 9. Molecular Structure and Dynamics 10. Social and Behavioral Science 11. Biotronics Office of Naval Research: 12. Aerospace Structures and Materials 13. Ocean Acoustics 14. Machine Learning, Reasoning, and Intelligence 15. Power Electronics & Electromagnetism, Adaptive & Machinery Controls and Advanced Machinery Systems For complete details, please see the funding opportunity announcement at https://www.grants.gov/web/grants/view-opportunity.html?oppId=326765. White papers are due by 21 September 2020. Hal S. Greenwald, Ph.D. Program Officer, Cognitive & Computational Neuroscience Air Force Office of Scientific Research (AFOSR/RTA) 875 N. Randolph St. Arlington, VA 22203-1768 (703) 588-8441

1 0

PhD position Deep Learning for MRI-based addiction research
by Fabian Eitel May 20, 2020

May 20, 2020

1 0

PostDoc in Reinforcement Learning for Intensive Care Treatment (including Covid-19) @ Imperial College London
by Faisal, Aldo A May 20, 2020

May 20, 2020

Dear All, We are looking for a Post-Doctoral research fellow to join our machine learning effort to establish the computational foundations for an “AI clinician” (Komorowksi et Faisal, 2018, Nature Medicine; Li et Faisal, 2020, ICLR AI4Health). While a “fully autonomous doctor” is far more ambitious (and perhaps undesirable) than self-driving cars (Topol, 2019, Nature Medicine), our goal is to link our work in the established foundations of clinical decision recommendation systems and evaluate our developments in a meaningful clinical setting. This vision in turn requires the development of novel RL techniques, which are at the heart of this fellowship, such as causal inference in RL and explainable RL (XRL). We have previously shown that RL and Bayesian Optimization can be applied for learning optimal interventions in closed loop (Bohte et al, 2013, Lorenz et al, 2016). We work on methods allowing us to train RL agents interactively (off-policy policy estimation) on historical data of medical interventions (“actions”) and patient responses to them (“state”). We want to build on our groundwork drawing on causal inference, deep- and kernel-based machine learning (Liu et al, 2018, NeurIPS; Parbhoo et al, 2018, PLOS1; Peng et al, 2018, AMIA) and explainable RL (Shafti et al, IROS, 2019) for the emerging domain of RL for medical interventions. Crucially, we believe that Machine Learning for medical intervention may have a larger impact on health than diagnostics, as treatment is where medicine really starts helping people. Crucially, we are working on both the theory and application for RL in healthcare and have as a team already the necessary regulatory approvals for clinical evaluations in our university hospitals are in place and already recruited clinical staff and hospital IT teams. Moreover, as our domain application is the optimal treatment of patient intensive care units, the PostDoc will have the opportunity to join the fight against Covid-19 to save lives by better understanding. Our lab is leading the data science effort for the UK national service evaluation in critical care (https://www.imperial.ac.uk/artificial-intelligence/research/covid/covid-icu/) and is thus working on data from ICUs across an entire country to identify optimal treatment policies. Advert here: https://www.imperial.ac.uk/jobs/description/ENG01259/research-associate https://www.jobs.ac.uk/job/BZP190/research-associate Applications can be received after the advertised deadline (which will be also extended due to Covid-19). Please do reach out to me to find out more. kind regards Aldo -- Aldo Faisal Director, Centre in AI for Healthcare – https://ai4health.io Reader in Neurotechnology -- https://FaisalLab.org Data Science Institute, Dept. of Computing & Dept. of Bioengineering Imperial College London

1 0

PhD scholarship on spiking network computational models at Barcelona
by Xavier Otazu May 18, 2020

May 18, 2020

The Neurocomputation and Biological Vision Team (NeuroBiT) (<http://www.cvc.uab.es/neurobit> ) offer a PhD scholarship to work in a highly multidisciplinary project which includes computational modelling of primary visual cortex and psychophysical experimentation of colour perception and its relationship to visual migraines. The Research will take place at the Computer Vision Centre <http://www.cvc.uab.es> (http://www.cvc.uab.es) of the Univ. Autònoma de Barcelona, which is consistently ranked among the top Spanish Universities <https://www.uab.cat/web/about-the-uab/the-uab/the-uab-in-the-rankings-13456…> (https://www.uab.cat/web/about-the-uab/the-uab/the-uab-in-the-rankings-13456…) The centre is located in a campus outside the vibrant city of Barcelona and has a strong tradition of multidisciplinary research in computational colour vision with their own psychophysical laboratory, living labs facilities and several computer clusters. The candidate will be part of the NeuroComputation and Biological Vision Team (NeuroBiT <http://www.cvc.uab.es/neurobit/> ), a recently-formed multidisciplinary research group which combines perceptual and computational approaches with visual neuroscience. We offer regular meetings with other PhD students, international contacts and the possibility of internship/cooperation with other experimental/theoretical labs. The successful candidate is likely to obtain several high-impact publications. The student research topic will be one of the following (to discuss with the candidate): 1) The development of recurrent multilayer spiking networks to reproduce the biological architecture of the primary visual cortex and several of its physiological properties, aiming at reproducing the results of psychophysical studies in colour perception and clinical studies on migraines. 2) Use of STDP (Spike Time Dependent Plasticity) for the definition of biologically plausible deep learning computational architectures (i.e. SNN –Spiking Neural Networks-). The duration of the position is 4 years (48 months). We expect the position to start on september/october 2020 (i.e. eventual visa issues should be solved beforehand!). The total annual stipend of the grant is around €15.000 gross, which include the cost of registration fees for the official doctorate program. Conditions: The candidate must * hold a European Higher Education Area (EHEA) qualification of at least 300 ECTS credits or a university-awarded Master degree or equivalent in the natural sciences/engineering at the time of signing the contract. * have experience in programming with Python and/or Matlab (C, C++, Unix environment and computer cluster experience are also valued) * have knowledge of / interest in the human visual system, visual perception and statistics * proficiency in English and Spanish * have excellent skills in analytical thinking, perceptiveness, commitment, self-organization, teamwork, and communication Any previous experience in the subject of computational modelling will also be a bonus. How to apply: Please send your CV and a short statement (500 words) connecting your previous work with the topics of this grant to xotazu(a)cvc.uab.es <mailto:xotazu@cvc.uab.es> and a copy of the official academic transcript (including GPA). We encourage candidates with strong skills in programming who are interested to work in a highly multidisciplinary project and environment (computer science, neuroscience, psychophysics), to apply. Candidates should have a degree in a related subject (Neuroscience, Computer Science, Mathematics, Physics, Biology, etc.).

1 0

PhD position in computational neuroscience and robotics
by Xavier Hinaut May 15, 2020

May 15, 2020

A PhD position is available at the Inria Bordeaux Sud-Ouest center and the Institute of Neurodegenerative Disease in Bordeaux, France. What: PhD position in computational neuroscience and robotics Where: Inria, Bordeaux, France When: October 2020 (3 years duration) Who: Xavier Hinaut & Frédéric Alexandre Application deadline: May 22th (22/05/2020) How to apply: https://jobs.inria.fr/public/classic/en/offres/2020-02637 Title of the PhD topic ================= NewSpeak: Neuro-computational models of language comprehension and production grounded in robots Keywords ================= Recurrent Neural Network (RNN), Reservoir Computing, Developmental Language Learning, Neuro-Robotics, Multimodal Language Grounding, Computational Neuroscience, Reinforcement Learning Candidate profile ================= - Good background in maths and computer science; - A strong interest for neuroscience and the physiological processes underlying learning; - Python programming with experience in scientific libraries Numpy/Scipy (or similar coding language: matlab, etc.); - Experience in machine learning or data mining is a preferred; - Independence and ability to manage a project; - Good English reading/speaking skills. Proposed research ================= We target to embody models into robots that will developmentally ground language. The grounding of semantics should come from the robot experiencing the world through its interactions with humans and the physical world. The goals are (1) to test hypotheses with biologically plausible language learning models with the Nao robot, (2) to extend the current model with unsupervised training and by reinforcement learning, and (3) to propose a new kind of Generative Adversarial Networks (GANs) for developmental language learning conditioned by grounded modalities such as vision. In order to model how a sentence can be processed, word by word (Hinaut & Dominey 2013) or even phoneme by phoneme (Hinaut 2018), the use of recurrent (artificial) neural networks, such as Reservoir Computing, offers interesting advantages. In particular the possibility to compare the dynamics of the model with data from neuroscience experiments (EEG, fMRI, ...). This paradigm allows to learn with few learning examples, and offers negligible runtime for human-robot interactions. The use of linguistic models with robots is not only useful to validate the models in real conditions, it also allows to test other hypotheses, notably on the anchoring of the language or the emergence of symbols. This involves finding out how a learning agent can link and categorise physical stimuli (vision, hearing, proprioception, etc.) to make the correspondence with symbols (Harnard 1990), or even to make these symbols emerge from stimuli coming from sensors (Taniguchi et al. 2016). We aim that a robot could process language from morphemes to sentences, similarly as a child, in order to better model how children acquire language. One aim is to obtain symbolic representations that are a composition of multimodal grounded representations. We will experiment how the newly developed language model will be able to learn to understand utterances by exploring which meanings the morphemes, words, ... can have based on other modalities of the robot (e.g. vision, proprioception). Starting from preliminary results (Juven & Hinaut 2020), we will first consider merging the representations from vision with a pre-trained CNN (Convolutional Neural Network). Then, reinforcement learning experiments will explore how the robot can learn the meaning of sentences: first by doing random actions for any user utterance, and then bootstrap from the user’s feedback. We will use a concrete corpus of sentences based on actions a robot can do (Hinaut & Twiefel 2019). We will implement several variants of language models: (1) extension of the reservoir computing model linked with grounded CNN, (2) adapt such model to the GAN paradigm in order to couple language comprehension and production in a self-learning generative mechanism (thus creating more biologically plausible GANs), (3) explore unsupervised (cross-situational learning) and reinforcement learning with these models. In parallel, we will adapt models features and behaviours to the ones observed in language acquisition experiments in psychology, and neural evidences in neuro-linguistic studies. In particular, we will explore how the models could shed light on language developmental impairments. We will run models in simulated humanoid robots and in a Nao robot. More information ============ More information is available on the application web page: https://jobs.inria.fr/public/classic/en/offres/2020-02637 Questions can be asked by email to Xavier Hinaut (xavier.hinaut(a)inria.fr) 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

1 0

Postdoc position in the Mainen lab at Champalimaud
by Luca Mazzucato May 14, 2020

May 14, 2020

Systems Neuroscience Postdoctoral Fellow The Champalimaud Foundation, a private, non-profit research institution, is opening a call for a post-doctoral researcher, within the scope of the research project entitled "Neural mechanism of value based decision making of staying or leaving - Deciding when to initiate locomotion to move to the next reward location." This position involves data analysis/modeling of foraging behavior and self-initiated movements in rodents, related to a Systems Neuroscience experiment addressing the following question. When interacting with a complex environment, animals generate naturalistic behavior in the form of action sequences. To analyze and classify such behavior from video recordings is a computationally demanding task, requiring development of specific software pipelines adapted for big data structures using deep learning methods and state space models. This project aims at creating such a pipeline for analyzing videos of mouse behavior previously collected in the Mainen Lab at the Champalimaud Center for the Unknown, Portugal. In the experiment, animals performed certain action sequences, with variable onsets timing, leading to a water reward. Optogenetics photostimulation of inhibitory neurons in secondary motor regions was also performed during selected action sequences. Behavior during this task was recorded with two high speed, high resolution cameras pointing at the animal's face and front limbs. The large size of the datasets generated in this experiment (~1 Tb per animal, for 10 animals) requires an extensive effort to classify behavioral motifs. In the second part of the project, behavioral motifs will be related to large scale recordings of neural activity from Neuropixel probes. The fellow will investigate the relationship between neural activity and behavior using state space models and mechanistic models based on recurrent neural networks. The fellow will integrate a dynamic team of researchers as part of an international collaboration between the Mainen Lab <https://mainenlab.org/> at the Champalimaud Centre for the Unknown and the Mazzucato Lab <https://www.mazzulab.com/> at University of Oregon. National, foreign and stateless candidates can develop the proposed work remotely where team interaction is supported by online platforms (e.g. slack, zoom, etc). *References:* Recanatesi, S. et al., biorxiv (2020). Vertechi, P. et al., Neuron (2020). Murakami, M. et al., Neuron (2017). Murakami, M. et al., Nat. Neuro (2014). Essential qualifications: PhD in Neuroscience, Cognitive Science, Biology, Experimental Psychology, Engineering, Physics, Mathematics, or other areas relevant to neurophysiology and animal behavior; Proficiency in Python and Matlab programming language and familiarity with object oriented programming; Familiarity with Deep Learning software (TensorFlow); Familiarity with data analysis of biological experiments; Ability to work independently and troubleshoot technical issues; Good capacity and value for teamwork and communication skills; Fluency in English. REMUNERATION The monthly remuneration to be attributed is the 33rd level of the single remuneration table (TRU), approved by Portaria nº 1553-C/2008, of December 31st, starting at a base gross salary of 2,128.34 Euros. SELECTION PROCESS Applications will be accepted until June 15th, 2020 or until a qualified candidate is hired. Candidates must submit a single document (maximum 4 pages; paper or PDF file) that contains a motivation letter and the Curriculum Vitae, both written in English. Email applications must be addressed to careers(a)research.fchampalimaud.org and include “CPD2020-LOCODECISION” in the subject line. The selected candidate must submit proof of her/his PhD or Doctoral Degree within 10 days following notification of the preliminary results. More information about Diplomas recognition may be found in the Euraxess portal <http://www.euraxess.pt/portugal/information-assistance/recognition-diplomas> . All candidates who formalize their application incorrectly or who fail to provide the requirements imposed by this tender are excluded from admission. In case of doubt, the panel is entitled to request further documentation to support candidate statements. Notification of Results: The highest scoring candidate will be notified by email or telephone. All other candidates will be notified solely by email. Non-discrimination and equal access policy: The Champalimaud Foundation actively promotes a policy of non-discrimination and equal access, so that no candidate can be privileged, benefited, harmed or deprived of any right, on basis of age, sex, sexual orientation, marital status, family status, economic situation, education, social origin or condition, genetic heritage, reduced working capacity, disability, chronic illness, nationality, race, territory of origin, language, religion, political or ideological convictions, and trade union membership.

1 0