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Publication of the NeurIPS2019 Reproducibility Challenge reports

by Nicolas P. Rougier

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

May 22, 2020

Community review of two new standards now open

by Helena Ledmyr

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

May 22, 2020

Call for papers: Neuroinformatics special issue "Building the NeuroCommons"

by Helena Ledmyr

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

May 22, 2020

Imperial College MRes in Neurotechnology

by Kozlov, Andriy

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)

May 21, 2020

DEPSCoR funding opportunity for tenured/tenure-track faculty in select US states/territories

by GREENWALD, HAL S DR-04 USAF AFMC AFOSR/RTA

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

May 20, 2020

PhD position Deep Learning for MRI-based addiction research

by Fabian Eitel

May 20, 2020

PostDoc in Reinforcement Learning for Intensive Care Treatment (including Covid-19) @ Imperial College London

by Faisal, Aldo A

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

May 20, 2020

PhD scholarship on spiking network computational models at Barcelona

by Xavier Otazu

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.).

May 18, 2020

Faculty position at CHAIN: Assistant Professor or Lecturer

by Hideaki Shimazaki

Dear colleagues, Our newly established Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) at Hokkaido University in Japan invites applications for a faculty position at the Assistant Professor or Lecturer level. The full description and details are attached below. See also: https://jrecin.jst.go.jp/seek/SeekJorDetail?fn=3&ln=1&id=D120050453&ln_jor=1 https://www.chain.hokudai.ac.jp/?lang=en The deadline for applications is 8 June 2020 (JST). All the best, Hideaki --- Hideaki Shimazaki, Ph.D https://www.neuralengine.org Position Open: Assistant Professor or Specially Appointed Lecturer, Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, in Hokkaido, Japan invites applications for a 3.5 year research and teaching position with an anticipated start date of September 1st, 2020. The successful applicant will be appointed as a faculty member at the rank of Specially Appointed Assistant Professor or Lecturer in CHAIN. Women and members of other underrepresented groups are particularly encouraged to apply. CHAIN is a research and education center for interdisciplinary studies in the humanities, social sciences, neuroscience, and artificial intelligence. It aims to open up new directions of research into the nature of human existence by fostering interaction and collaboration between humanities and the social sciences, including philosophy, ethics, psychology (cognitive and social), law, and economics, and the natural sciences, such as neuroscience, AI, and cognitive science. In particular, it focuses on connecting philosophical ideas with methods and insights of mathematical and empirical sciences. A new interdisciplinary graduate program of CHAIN starts in July 2020. Qualifications The ideal applicant will have an independent research project that will strengthen and complement existing interdisciplinary research in CHAIN. They are expected to demonstrate the following qualities and characteristics: 1. A PhD by the time of the appointment in one (or more) of the following area(s): philosophy, ethics, psychology, cognitive science, neuroscience, AI, information science, or robotics. 1. Outstanding publication records and/or demonstrable potential to publish in highly esteemed publication outlets. 1. Strong motivation to fulfill research, teaching, and administrative duties as a faculty member in CHAIN. 1. Enthusiasm and competence for delivering postgraduate and undergraduate courses on topics pertinent to CHAIN’s research and education goals. 1. A demonstrated ability and strong interest to develop and engage in interdisciplinary research projects in collaboration with existing members of the institution. Application materials Submit the following materials, compiled in a single ZIP file, via email at jobs [at]chain.hokudai.ac.jp to complete your application: 1. Curriculum Vitae* 2. Summary of past research accomplishments (1-2 pages) 3. Three most important publications relevant to this position. 4. Brief statement of research and teaching interests (1-2 pages) 5. Two references (Names and contact details) Use common fonts such as Times New Roman in black and size 12 points. Submit each publication in a separate PDF file. Compile all the rest (1, 2, 4, and 5) in a single PDF file. This makes four PDF files in total, which must then be compiled in a single ZIP file. Submission is due Monday, 8 June, 2020 (JST). The subject line of the email must read “Application (Lecturer/Assistant Professor).” You will receive a confirmation email shortly after making a submission. Please contact us immediately if you did not receive one within 3 business days. Personal information obtained through these materials will only be used within this selection process. *Applicants who have been employed by Hokkaido University after April 1, 2014 (in any position, including Part-time Lecturer, Teaching Assistant, Teaching Fellow, Research Assistant and Short-term Support Assistant, etc.) should provide full details of their employment history in the CV. Appointment terms This is a full-time research and teaching position with an anticipated start date of September 1st, 2020. The salary will be paid under the annual-salary scheme and the discretionary labor system. The annual salary is determined in accordance with the internal regulations of Hokkaido University. Upon employment, one will be enrolled in the Ministry of Education, Culture, Sports, Science and Technology Mutual Aid Association, Employees Pension, Workers’ Accident Compensation Insurance and Employment Insurance. A one month probationary period applies. Questions about the position should be addressed to the chair of the search committee, Shigeru Taguchi (inquiry[at]chain.hokudai.ac.jp). Further information Website: www.chain.hokudai.ac.jp/?lang=en Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) Hokkaido University Kita 12 Nishi 7, Kita-ku, Sapporo Hokkaido 060-0812 Japan Application submission: jobs [at] chain.hokudai.ac.jp Inquiries: : inquiry [at] chain.hokudai.ac.jp

May 15, 2020

PhD position in computational neuroscience and robotics

by Xavier Hinaut

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

May 15, 2020