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PhD Comp Neuro position, Moreno-Bote lab, Barcelona

by Ruben Moreno Bote

*PhD Position in* *Computational Neuroscience, Ruben Moreno-Bote Lab * *Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain* 1 fully funded PhD position is available in the group of Theoretical and Cognitive Neuroscience of Ruben Moreno-Bote in the Center for Brain and Cognition at the University Pompeu Fabra, Barcelona, Spain. We are looking for excellent candidates with Physics, Mathematics or Machine Learning background interested in developing theory for the brain and mind. Knowledge of Computational Neuroscience or Neuroscience would be ideal, but it is not necessary. The candidate will develop state of the art theory and models to understand neuronal dynamics and coding and will have access to multielectrode recording neural data. She/he could also be involved in the design of monkey and human behavioral experiments and the development of models of visual perception, decision making and working memory. The candidate will benefit from the stimulating environment of Barcelona area in Theoretical and Systems Neuroscience and will have the opportunity of enjoying a lively city. Our lab (https://www.upf.edu/web/ruben-moreno-bote) forms part of a larger network of neuroscience labs in Barcelona. Financial support is provided by the Howard Hughes Medical Institute and the Spanish MINECO. APPLICATION: A full CV in pdf format and an extract of grades for degree and master studies should be sent to the following address: ruben.moreno(a)upf.edu DEADLINE: until the position is filled STARTING DATE: any time between September 2019 and January 2020 -- Rubén Moreno Bote Serra Húnter & ICREA Academia Professor Center for Brain and Cognition & Dept. of Information and Communications Technologies University Pompeu Fabra Campus Ciutadella c\ Ramon Trias Fargas, 25-27 Mercè Rodoreda building, 3th floor (room 24.331) 08005 Barcelona, Spain http://www.upf.edu/pdi/ruben-moreno-bote

March 16, 2019

[CALL FOR PAPERS] IEEE/WIC/ACM International Conference on Web Intelligence (WI'19) 14-17 OCTOBER 2019 Thessaloniki, GREECE

by Gigg Liu

IEEE/WIC/ACM International Conference on Web Intelligence (WI'19) 14-17 OCTOBER 2019 Thessaloniki, GREECE https://webintelligence2019.com/ Sponsored By: Web Intelligence Consortium (WIC) Association for Computing Machinery (ACM) IEEE Computer Society In Cooperation With: Chinese Computer Federation (CCF) CALL FOR PAPERS Web has evolved as an omnipresent system which highly impacts science, education, industry and everyday life. Web is now a vast data production and consumption platform at which threads of data evolve from multiple devices, by different human interactions, over worldwide locations under divergent distributed settings. Such a dynamic complex system demands adaptive intelligent solutions, which will advance know-ledge, human interactions and innovation. Web intelligence is now a cutting edge area which must address all open issues towards deepening the understanding of all Web’s entities, phenomena, and developments. The theme for the WI'19 is: "Web Intelligence = AI in the Connected World". The 18th Web Intelligence conference (WI'19) aims to achieve a multi-disciplinary balance between research and technological disruptive advances in the fields of how intelligence is impacting the Web of People, the Web of Data, the Web of Things, the Web of Trust, and the Web of Health. WI'19 welcomes research, application as well as Industry/Demo track paper submissions in these core thematic pillars under wider topics, which demand WI innovative and disruptive solutions for any of the next indicative sub-topics. Track 1: Web of People Social networks analytics Social media and dynamics User and behavioral modeling Human centric computing Opinion mining Recommendation engines Sentiment analysis Crowdsourcing and social data mining People oriented applications and services Track 2: Web of Data Data science and machine learning Big data analytics Data integration and data provenance Cognitive models Computational models Information search and retrieval Algorithms and knowledge management Knowledge bases and semantic networks Linked data management and analytics Data driven services and applications Track 3: Web of Things IoT data analytics Web infrastructures and devices Mobile web Distributed systems and devices Open autonomous systems Industrial multi-domain web Streaming data analysis Smart city applications and services Track 4: Web of Trust Hidden web analytics Blockchain analytics and technologies Web-scale security, integrity, privacy and trust Web cryptography Fake content and fraud detection Web safety and openness Monetization services and applications Track 5: Web of Health Personalized health management and analytics Health data exchange and sharing Big data in medicine Wellbeing and healthcare in the digital era Omics research and trends Healthcare and medical applications and services IMPORTANT DATES The Program Chairs are soliciting contributed technical papers for presentation at the Conference and publication in the Conference Proceedings by ACM. Submissions are encouraged before the next deadlines: Feb 20th, 2019: Workshop Proposals May 12th, 2019: Full Papers; Demo papers; Tutorial proposals June 30th, 2019: Notification of Acceptance/Rejection July 14th, 2019: Final, Camera Ready Papers, Due PAPER SUBMISSION Please follow guidelines: https://wi-lab.com/cyberchair/2019/wi19/scripts/submit.php?subarea=Wr/. Papers must be submitted electronically via Cyberchair in standard ACM format (max 8 pages). Submitted papers will undergo a peer review process, coordinated by the International Program Committee. ORGANIZATION General Chairs Georg Gottlob, UK Yannis Manolopoulos, Cyprus Program Chairs Payam Barnaghi, UK Athena Vakali, Greece Steering Committee Chairs Jiming Liu, HK, China Ning Zhong, Japan Track Chairs Panagiotis Bamidis, Greece Marios Dikaiakos, Cyprus Elena Ferrari, Italy Josiane Xavier Parreira, Ireland Herman Purohit, USA Workshop Chairs Dimitrios Katsaros, Greece Rahul Pandey, USA Demo Chairs Amelie Gyrard, USA George Pallis, Cyprus Proceedings Editor Theodoros Tzouramanis, Greece Publicity Chairs Karin Becker, Brasil Leonidas Anthopoulos, Greece Yang Liu, Hong Kong Xujuan Zhou, Australia

March 16, 2019

Post-Doc position: HuRRiCane project (deadline 27/03)

by Xavier Hinaut

Dear Colleagues, I would like to share the following post-doc position opening in my team (see below) with the title: HuRRiCane: Hierarchical Reservoir Computing for Language Comprehension If you know people interested, please forward. Thank you very much. Best regards, Xavier Hinaut Inria Researcher (CR) Mnemosyne team, Inria LaBRI, Université de Bordeaux, France Institut des Maladies Neurodégénératives +33 5 33 51 48 01 www.xavierhinaut.com --- Title HuRRiCane: Hierarchical Reservoir Computing for Language Comprehension Post-doc position for 1 year Starting October 2019 Deadline for applications Send email ASAP and before 27th of March 2019 Apply on before 31st of March 2019 How to apply? All info here: https://sites.google.com/site/xavierhinaut/job-internship-offers/post-docto… Remuneration 2653€ / month (before taxes) Keywords Computational Neuroscience, Recurrent Neural Networks, Reservoir Computing, Language Processing, Language Acquisition, Speech Processing, Modeling, Language Grounding, Prefrontal Cortex, Sequence Learning, Machine Learning. Scientific research context How does our brain understand a sentence at the same time as it is pronounced? How are we able to produce sentences that the brain of the hearer will understand? There is a huge number of tools for Natural Language Processing (NLP), but there is fewer computational models that try to understand how language comprehension and production works effectively in the brain. There are theoretical models or models based on psychological experiments (Dell & Chang 2014), but few models are based on neuro-anatomy and brain processes (Hinaut & Dominey 2013). Moreover, even fewer of these models have been implemented in robots, to demonstrate their robustness among other things (Hinaut et al. 2014). Using robots to study language grounding, acquisition and development is not a new topic, in fact Cangelosi et al. (2010) have proposed a roadmap to tackle this long research plan. 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 neural networks such as Reservoir Computing (Jaeger 2004), and its extension, the Conceptors (Jaeger 2017), offers advantages and interesting results. In particular, the possibility to compare the dynamics of the model with the dynamics of brain electrophysiological recordings (Enel et al. 2016) is an interesting asset. The Reservoir Computing paradigm is also interesting because it can be trained with few data and have quick execution time for human-robot interactions. The use of linguistic models with robots, is not only useful to validate the models in real conditions: it also enables to test other hypotheses, notably the anchoring of the language (Harnard 1990) or the emergence of symbols (Taniguchi et al., 2016). Work description The objective is to experiment how a sentence comprehension model, based on reservoir computing, can learn to understand sentences by exploring which meanings can have the sentences, implying several steps from stream of phonemes to words and from stream of words to sentence comprehension. The model will be implemented on a virtual agent first and then on the Nao humanoid robot For the experiments we want to make, a model implemented in a situated agent or robot is needed in order to "understand" the meaning of the utterance which is being told and figure out whether the meaning is plausible or not. If the meaning found does not make sense, the model could "re-parse" the sentence and reinterpret it. Moreover, we prefer to have a concrete corpus of based on actions a robot can do rather than abstract sentences of the Wall Street Journal (a classical benchmark). We have already performed several experiments with both sentence comprehension and production models with humanoid robots such as iCub and Nao (Hinaut et al. 2014, Twiefel et al. 2016). Having a plausibility measure of a meaning will allow us to set up a cascading reinterpretation if necessary, first from the word level, and if that is not enough, to reinterpret the phonemes to find new plausible words. There exists two main types of observations made with an electroencephalogram (EEG) with human subject: P600 and N400. They are assumed to correspond to syntactically or semantically reinterpretations of the sentence. The model developed will have to account for these induced observations. In Hinaut & Dominey (2013), we showed that our model could provide an equivalent of P600 when a sentence was syntactically complex. This project is linked to other projects in the team on the hierarchical organization of the prefrontal cortex (including Broca's area, involved in language). This hierarchy corresponds to an increasingly higher abstraction, which is made by different sub-areas. We will therefore be able to link this post-doc project to existing projects of the team, where different levels of abstractions are necessary for sentence comprehension. Main References - G. S. Dell, & F. Chang (2014) The P-chain: Relating sentence production and its disorders to comprehension and acquisition. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1634). - H. Jaeger, (2017) Using conceptors to manage neural long-term memories for temporal patterns. The Journal of Machine Learning Research, 18(1), 387-429. - X. Hinaut, P.F. Dominey (2013) Real-Time Parallel Processing of Grammatical Structure in the Fronto- Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing. PloS ONE 8(2): e52946. - X. Hinaut, M. Petit, G. Pointeau, P.F. Dominey (2014) Exploring the Acquisition and Production of Grammatical Constructions Through Human-Robot Interaction. Frontiers in NeuroRobotics 8:16. - Hinaut, X. (2018). Which Input Abstraction is Better for a Robot Syntax Acquisition Model? Phonemes, Words or Grammatical Constructions? In 2018 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). Skills Good background in computational neuroscience, computer science, physics and/or mathematics; A strong interest for neuroscience, linguistics and the physiological processes underlying learning; Python programming with experience with scientific libraries Numpy/Scipy (or similar programming language: matlab, etc.); Experience in machine learning or data mining; Independence and ability to manage a project; Good English reading/speaking skills. French speaking is not required Benefits package Subsidized meals Partial reimbursement of public transport costs Possibility of teleworking (after 6 months of employment) and flexible organization of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training Social security coverage

March 15, 2019

PhD FELLOWSHIP IN INTERDISCIPLINARY NEUROSCIENCE @ LISBON (pre-selection 20th Mar, application 28th Mar)

by Prata, Diana

For divulgence - many thanks. PhD FELLOWSHIP IN INTERDISCIPLINARY NEUROSCIENCE @ LISBON (pre-selection 20th Mar, application 28th Mar) The annual FCT state-sponsored PhD scholarship application call is now open (deadline 28th of March - see conditions in https://www.fct.pt/apoios/bolsas/concursos/individuais2019.phtml.en) and is an excellent opportunity to be part of a PhD project at the Biomedical Neuroscience Lab (Diana Prata's lab; dpratalab.wordpress.com<http://dpratalab.wordpress.com>) at the University of Lisbon. If you are interested in a project within one the research streams below, please contact the PI (diana.prata(a)kcl.ac.uk<mailto:diana.prata@kcl.ac.uk>) for a skype interview by 20th of March, to ascertain mutual interest. This being ascertained, your FCT PhD scholarship (deadline 28th Mar) application can be associated with one of the lab's projects. Research stream 1: Neurobiology of Social Cognition Context. Understanding the neurochemistry and circuitry mediating social cognition is key to treat a large range of neuropsychiatric disorders – as social deficits are often present at their origin and often do not subside with treatment. Working out what others think, intend and feel is essential for optimal communication and cooperation and is dysfunctional in schizophrenia and other illnesses. We are characterizing the physiology involved in social cognition, for example: how does oxytocin promote social reinforcement learning? What effect does it have in brain and behaviour? How does it interact with other neurotransmitter systems? Tools. We will study healthy humans and schizophrenia patients with structural and functional neuroimaging (MRI, DTI and MRS), double blind placebo-controlled pharmacological administration, psychological testing, social cognition tasks, eye-tracking, pupilometry, skin conductance response, EEG, DNA/proteomics testing and computational modelling. We use mainly MATLAB, SPSS, and other more specific quantitative data analysis and task presentation software. Sponsors. European Commission, Portuguese Science and Technology Foundation, and Bial Foundation. Main collaborations. King’s College London (UK), Emory University (USA), and The Netherlands Institute for Neuroscience (The Netherlands). Project Stream 2. Multimodal biomarkers to predict the onset and prognosis of neuropsychiatric illnesses. Keywords. Genetics, neuroimaging, environment, clinical biomarkers, schizophrenia, autism, Alzheimer’s, Parkinson’s. Context. Psychiatry and, to a lesser extent, neurology are still fields of medicine that take very little advantage of quantitative, biological and objective measurements – with a lot of trial-and-error and one-size-fits-all therapeutics. This may be why diagnosis, prediction of prognosis and response to treatment are relatively inaccurate, late and expensive. For example, about a third of Alzheimer’s cases go on mis- or under-diagnosed; it is still undetected which one third of people with at-risk symptoms for schizophrenia go on to develop this chronic illness, and about one quarter of schizophrenia patients do not respond to their first line of treatment. Can we capitalize on the existing information in brain scans and other quantitative measurements to assist clinicians in deciding on patients’ diagnosis or prognosis, earlier and more accurately than currently – so that the correct treatment can start as soon as possible? Tools. We are developing pattern recognition algorithms that can statistically predict the level of personalized risk of each new patient. To train these algorithms, we use pre-existing samples (free online or our own) containing neuroimaging and also genetic, psychological, environmental and clinical data. We use mainly MATLAB, Python and machine learning tools. Sponsors. Portuguese Science and Technology Foundation, NIHR (National Institute for Health Research, UK) Main Collaborations. King’s College London (UK), Radboud University Nijmegen (The Netherlands), University of Munich (Germany)

March 15, 2019

Job Openings: Professor / Associate Professor / Assistant Professor (Computer Science) at Hong Kong Baptist University

by COMP HKBU

The Department of Computer Science of Hong Kong Baptist University, presently offers BSc, MSc, MPhil, and PhD programmes, now seeks outstanding applicants for the following faculty positions on tenure-track. The appointees are expected to perform high-impact research; to teach and manage programmes at undergraduate and postgraduate levels, as well as to contribute to professional and institutional services. Collaboration with other faculty members in research and teaching is also expected. They will be encouraged to collaborate with colleagues within the Department to contribute to two special thematic applications including (a) health informatics and (b) secure and privacy-aware computing, and/or outside the Department to contribute to interdisciplinary research projects under our University’s Research Cluster on Data Analytics and A.I. in X. Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information Systems, or a related field, and sufficiently demonstrate abilities to conduct high-quality research in one of the Department’s key research areas: (i) computational intelligence, (ii) databases and information management, (iii) networking and systems, and (iv) pattern recognition and machine learning. Applicants should also demonstrate strong commitment to undergraduate and postgraduate teaching in computer science and/or information systems, possess track record of innovative research and high-impact publications, and demonstrate the ability to bid for and pursue externally-funded research programmes. Initial appointment will be made on a fixed-term contract of three years. Re-appointment thereafter is subject to mutual agreement and availability of funding. For enquiry, please contact Dr. William Cheung, Head of Department (email: william(a)comp.hkbu.edu.hk) More information about the Department can be found at http://www.comp.hkbu.edu.hk . Rank and salary will be commensurate with qualifications and experience. Application Procedure: Applicants are invited to submit their applications at the HKBU e-Recruitment System (jobs.hkbu.edu.hk). Applicants are requested to send in samples of publications, preferably three best ones out of their most recent publications. Applicants should also request two referees to send in confidential letters of reference, with PRnumber (stated above) quoted on the letters, to the Personnel Office (email: recruit(a)hkbu.edu.hk) direct. All application materials including publication samples, scholarly/creative works will be destroyed after completion of the recruitment exercise. Details of the University's Personal Information Collection Statement can be found at http://pers.hkbu.edu.hk/pics. The University reserves the right not to make an appointment for the posts advertised, and the appointment will be made according to the terms and conditions applicable at the time of offer. Review of applications will begin in mid April 2019 and will continue until the positions are filled. URL: https://www.comp.hkbu.edu.hk/v1/?page=job_vacancies&id=503

March 15, 2019

CFP 28th International Conference on Artificial Neural Networks ICANN 2019, Sept 17-19, Munich, DE, Deadline extension

by Paolo Masulli ENNS

28th International Conference on Artificial Neural Networks http://icann2019.org = Second announcement - submission deadline extended to April 1, 2019 = Munich, Germany 17 - 19 September 2019 Special event: BIGCHEM (http://bigchem.eu) final meeting ============================================================ The International Conference on Artificial Neural Networks (ICANN) is the annual flagship conference of the European Neural Network Society (ENNS). In 2019 the Helmholtz Zentrum Muenchen - German Research Center for Environmental Health GmbH (HMGU) and the Technical University of Munich (TUM) together with the "Big Data in Chemistry" Marie Skłodowska-Curie Innovative Training Network European Industrial Doctorate project (BIGCHEM) organize the 28th ICANN Conference from the 17th to the 19th of September 2019 in Munich, Germany. CONFERENCE TOPICS ICANN 2019 is a dual-track conference featuring tracks in Brain Inspired Computing and Machine Learning and Artificial Neural Networks, with strong cross-disciplinary interactions and applications. All research fields dealing with Neural Networks will be present at the conference. A non-exhaustive list of topics includes: Machine Learning: Deep Learning, Neural Network Theory, Neural Network Models, Graphical Models, Bayesian Networks, Kernel Methods, Generative Models, Information Theoretic Learning, Reinforcement Learning, Relational Learning, Dynamical Models Recurrent Networks. Brain Inspired Computing: Cognitive models, Computational Neuroscience, Self-organisation, Reinforcement Learning, Neural Control and Planning, Hybrid Neural-Symbolic Architectures, Neural Dynamics. Neural Applications for: Bioinformatics, Biomedicine, Intelligent Robotics, Neurorobotics, Language Processing, Image Processing, Sensor Fusion, Pattern Recognition, Data Mining, Neural Agents, Brain-Computer Interaction, Neural Hardware, Evolutionary Neural Networks. Special session (BIGCHEM): Big Data analysis in chemistry, chemoinformatics, use of deep learning to predict molecular properties, drug-discovery, modelling and prediction of chemical reaction data, synthetic route prediction, structure generation, molecular dynamics simulations and quantum chemistry. Special sessions: Artificial Intelligence in Medicine, Informed and Explainable Methods for Machine Learning, Deep Learning in Image Reconstruction, Machine Learning with Graphs: Algorithms and Applications, Neural Variational Inference and Time Series. Workshops: 1st International Workshop on Reservoir Computing (RC 2019) CALL FOR CONTRIBUTED SCIENTIFIC COMMUNICATIONS All scientific communications presented at ICANN 2019 will be reviewed and scientifically evaluated by a panel of experts. The conference will feature three categories of communications: - oral communications (15'+5') - poster communications - demonstrations Call for Papers: Authors willing to present original contributions in either oral or poster category may submit: - a proceedings paper manuscript of about 10 - 12 pages to be published in Springer-Verlag Lecture Notes in Computer Science (LNCS) series with individual DOI - extended abstracts of a maximum length of 4 pages to be published in Springer-Verlag Lecture Notes in Computer Science (LNCS) series. The number of oral slots is limited. In case the number of requested oral presentations is larger than the available slots the ICANN scientific committee will select which papers will be reassigned to a poster session. This selections will be based on the coherence of the programme and is totally independent of the category of submission. Submission of communications will be online. More details will be made available soon on the conference website: http://icann2019.org IMPORTANT DATES Deadline of special sessions submission: 1 February 2019 Opening of contribution submissions: 1 February 2019 Submission of demonstration proposals: 1 March 2019 Deadline of proceedings paper submission: EXTENDED to 1 April 2019 Deadline of extended abstracts submission: 1 April 2019 Notification of acceptance: 1 May 2019 Camera-ready paper and registration opening: 1 June 2019 Deadline early registration at discount rate: 15 June 2019 Conference dates: 17-19 September 2019 REGISTRATION FEES (EARLY RATES) Full registration: 400 EUR Student registration: 250 EUR Reductions for ENNS members BEST PAPER AWARDS ENNS will sponsor a maximum of four best paper awards, two in the Brain Inspired Computing track (one poster and one oral communication) and, analogously, two in the Machine Learning Research track. All awardees will be presented during the final ceremony. TRAVEL GRANTS The European Neural Network Society sponsors a number of Student Travel Grants covering part of the costs for attending ICANN. Details on the conference website. ORGANISATION General Chair: Igor Tetko (ENNS/HMGU Munich, Germany) and Fabian Theis (HMGU/TUM Munich, Germany) Organising Committee Chairs: Monica Campillos (HMGU Munich, Germany), Alessandra Lintas (ENNS Lausanne, Switzerland) Honorary Chair: Vera Kurkova, Czech Academy of Sciences (ENNS President) Communication Chair: Paolo Masulli (ENNS Lausanne, Switzerland)

March 14, 2019

[journals] CfP : Special Issue on Continual Unsupervised Sensorimotor Learning : Deadline extended

by Nguyen, Sao Mai

Dear Colleagues, IEEE Transactions on Cognitive and Developmental Systems is currently running a Special Issue entitled " Continual Unsupervised Sensorimotor Learning" : http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue… Due to a number of requests, we have extended the paper submission deadline to 21 March. IMPORTANT DATES *21st March 2019 – Extended Paper Deadline* (final) 15th March 2019 – Notification for authors 31st May 2019 – Deadline revised papers submission 30th June 2019 – Final notification for authors 31st July 2019 – Deadline for camera-ready versions September 2019 – Expected publication date AIM AND SCOPE Although machine learning algorithms continue to improve at a rapid pace enabling technologies and products such as autonomous driving cars and sophisticated image and speech recognition, it is often forgotten that these applications represent tailored solutions to specific tasks. Thus it is not clear if or how these autonomous systems can pave the road to general purpose machines envisioned by many. The pursuit for higher levels of autonomy and versatility in robotics is arguably lead by two main factors. Firstly, as we push robots out of the labs and productions lines, it becomes increasingly difficult to design for all possible scenarios that a particular robot might encounter. Secondly, the cost of designing, manufacturing, and maintaining such systems becomes prohibitive. As the algorithms for learning single tasks in restricted environments are improving, new challenges have gained relevance in order to get more autonomous artificial systems. These challenges include multi-task learning, multimodal sensorimotor learning and lifelong adaptation to injury, growth and ageing. Addressing these challenges promise higher levels of autonomy and versatility of future robots. This special issue on Continual Unsupervised Sensorimotor Learning is primarily concerned with the developmental processes involved in unsupervised sensorimotor learning in a life-long perspective, and in particular the emergence of representations of action and perception in humans and artificial agents in continual learning. These processes include action-perception cycle, active perception, continual sensory-motor learning, environmental-driven scaffolding, and intrinsic motivation. The special issue will highlight behavioural and neural data, and cognitive and developmental approaches to research in the areas of robotics, computer science, psychology, neuroscience, etc. Contributions might focus on mathematical and computational models to improve robot performance and/or attempt to unveil the underlying mechanisms that lead to continual adaptation to changing environment or embodiment and continual learning in open-ended environments. Contributions from multiple disciplines including cognitive systems, cognitive robotics, developmental and epigenetic robotics, autonomous and evolutionary robotics, social structures, multi-agent and artificial life systems, computational neuroscience, and developmental psychology, on theoretical, computational, application-oriented, and experimental studies as well as reviews in these areas are welcome. THEMES This special issue aims to report state-of-the-art approaches and recent advances on Continual Unsupervised Sensorimotor Learning with a cross-disciplinary perspective. Topics relevant to this special issue include but are not limited to: Emergence of representations via continual interaction Continual sensory-motor learning Action-perception cycle Active perception Environmental-driven scaffolding Intrinsic motivation Neural substrates, neural circuits and neural plasticity Human and animal behaviour experiments and models Reinforcement learning and deep reinforcement learning for life-long learning Multisensory robot learning Multimodal sensorimotor learning Affordance learning Prediction learning SUBMISSION Manuscripts should be prepared according to the “Information for Authors” of the journal found at https://cis.ieee.org/publications/t-cognitive-and-developmental-systems/tcd… Submissions must be done through the IEEE TCDS Manuscript center: https://mc.manuscriptcentral.com/tcds-ieee. During the submission process, please select the category “SI: Continual Unsupervised Sensorimotor Learning”. More information on Continual Unsupervised Sensorimotor Learning http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue… GUEST EDITORS Nicolás Navarro-Gerrero Aarhus University, Aarhus, Denmark nng(a)eng.au.dk Sao Mai Nguyen IMT Atlantique, Francenguyensmai(a)gmail.com Erhan Öztop Özyeğin University, Turkeyerhan.oztop(a)ozyegin.edu.tr Junpei Zhong National Institute of Advanced Industrial Science and Technology (AIST), Japanjoni.zhong(a)aist.go.jp ---- Nguyen Sao Mai nguyensmai(a)gmail.com Researcher in Cognitive Developmental Robotics http://nguyensmai.free.fr

March 14, 2019

3-year PhD studentship at University of Strathclyde, Glasgow

by Shuzo Sakata

A fully funded 3-year PhD studentship is available to work with Dr Shuzo Sakata at the University of Strathclyde in Glasgow, UK. This PhD project concerns a large dataset of in vivo silicon probe recordings with optogenetic tagging from the mouse auditory system across age. By applying advanced statistical approaches, we aim to better understand how state-dependent and cell-type-specific information processing in the auditory system can change over multiple timescales. In this project, data analysis with computer programs (e.g., Python, MATLAB) is an essential component. A successful candidate should have or expect to have an Honours Degree at 2.1 or above (or equivalent) in Computational Neuroscience, Physics, Data Science, Computer Science, Statistics or related fields. During their PhD, they will also have an excellent opportunity to learn about in vivo experimental approaches as well as neurotechnology. In the first instance, candidates may send their application to Dr Shuzo Sakata (shuzo.sakata(a)strath.ac.uk) including a CV and cover letter, detailing their motivation for this particular PhD project and their career goal. -- Shuzo Sakata, Ph.D. Senior Lecturer in Circuit Neurophysiology Strathclyde Institute of Pharmacy and Biomedical Sciences University of Strathclyde 161 Cathedral Street, Glasgow G4 0RE, UK tel: +44-(0)141-548-2156, fax: +44-(0)141-552-2562 email: shuzo.sakata(a)strath.ac.uk The University of Strathclyde is a charitable body, registered in Scotland, number SC015263

March 13, 2019

Bernstein Network Information Booth at the 13th Göttingen Meeting of the German Neuroscience Society (NWG)

by Alexandra Stein

Dear colleagues, The Bernstein Network Computational Neuroscience<https://www.bernstein-network.de/de/neues/termine/13th-goettingen-meeting-o…> will have an exhibition booth (C) at the upcoming Göttingen Meeting of the German Neuroscience Society<https://www.nwg-goettingen.de/2019/> (March 20-23, 2019). The booth will present: * research fields * open positions * study and training programs * infrastructural facilities: Bernstein Facility for Data Technology: G-Node<http://www.g-node.org/> and Bernstein Facility for High Performance Simulation and Data Analysis: Simulation Laboratory Neuroscience <http://www.fz-juelich.de/ias/jsc/EN/Expertise/SimLab/slns/_node.html> * Data management service (GIN)<https://web.gin.g-node.org/>, Electrophysiology Analysis Toolkit (Elephant)<http://neuralensemble.org/elephant/>, and The Neural Simulation Technology (NEST) Initiative <http://www.nest-initiative.org/> Furthermore, there will be various demos at our booth. Please find the schedule below: G-Node will give demo presentations of their data management services (GIN) for organizing, sharing, and publishing research data: Wednesday, March 20, 13:00 - 14:30 Thursday, March 21, 10:00 - 11:30 and 16:30 - 18:00 Friday, March 22, 10:00 - 11:30 Walk-ins are welcome any time during the conference. Demos of data analysis using the Electrophysiology Analysis Toolkit (Elephant) will take place at: Wednesday, March 20, 13:00 - 14:30 Thursday, March 21, 16:30 - 18:00 Friday, March 22, 10:00 - 11:30 The Neural Simulation Technology (NEST) Initiative will give demos at the following times: Wednesday, March 20, 13:00 - 14:30 Thursday, March 21, 16:30 - 18:00 Friday, March 22, 10:00 - 11:30 We are looking forward to welcoming you at our booth no. C! Best regards, Alexandra Stein -- Dr. Alexandra Stein Head of Bernstein Coordination Site Bernstein Network Computational Neuroscience | Bernstein Coordination Site (BCOS) Branch Office of the Forschungszentrum Jülich at the University of Freiburg Hansastr. 9A | 79104 Freiburg, Germany phone: (+49) 0761 203 9583 mobile: (+49) 0151 67114645 mail: a.stein(a)fz-juelich.de<mailto:a.stein@fz-juelich.de> web: www.bernstein-network.de<http://www.bernstein-network.de> Twitter: NNCN_Germany YouTube: Bernstein TV Facebook: Bernstein Network Computational Neuroscience, Germany LinkedIn: Bernstein Network Computational Neuroscience, Germany ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, Prof. Dr. Sebastian M. Schmidt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

March 13, 2019

Computational Vision Summer School (CVSS) 2019 Call for Applications

by Hendrikje Nienborg

[apologies for cross-posting] We are seeking applications for the 3rd Computational Vision Summer School (CVSS)from*June 30th to July 7th 2019*in Freudenstadt (Black Forest), Germany. ** *The summer school, aimed at PhD students and Postdocs, spans the spectrum of vision research from neuroscience and psychophysics to computer vision to foster discussions at the intersection of biological and artificial vision systems. It seeks to bring together people from diverse disciplines who all share a computational view of vision and who, due to the diversity of disciplines involved, do not regularly meet.* *** CVSS is free of tuition and accomodation is sponsored by the German Research Foundation (DFG: CRC/1233 Robust Vision). Application deadline: April 15, 2019 http://orga.cvss.cc/ Confirmed speakers: ** Ted Adelson (MIT, USA) ** Matthias Bethge (University of Tübingen, Germany) Michael Black (MPI-IS Tübingen, Germany) EJ Chichilnisky (Stanford University, USA) Alexei Efros (UC Berkeley, USA) Sanja Fidler (University of Toronto / NVIDIA, Canada) Chelsea Finn (UC Berkeley, USA) Roland Fleming (University Giessen, Germany) Bill Geisler (UT Austin, USA) Otmar Hilliges (ETH Zürich, Switzerland) Mackenzie Mathis (Harvard University, USA) Bruno Olshausen (UC Berkeley, USA) Ruth Rosenholtz (MIT, USA) Stefan Roth (TU Darmstadt, Germany) Eero Simoncelli (New York University, USA) Antonio Torralba (MIT, USA) Raquel Urtasun (University of Toronto / Uber ATG, Canada) Felix Wichmann (University of Tübingen, Germany) Li Zhaoping (MPI-IS and University of Tübingen, Germany) Program Chairs and Advisory Board: Andreas Geiger, Hendrikje Nienborg, Siyu Tang, Bei Xiao Matthias Bethge, Michael Black, Felix Wichmann * -- Dr. Hendrikje Nienborg Werner Reichardt Centre for Integrative Neuroscience Universitaet Tuebingen Tel: +49 (0) 7071- 29 88846 Fax: +49 (0) 7071- 29 25008 email:hendrikje.nienborg@cin.uni-tuebingen.de

March 13, 2019