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CFP: TAC | Special Issue on Automated Perception of Human Affect from Longitudinal Behavioral Data

by Pablo Barros

CALL FOR PAPERS IEEE Transactions on Affective Computing Special Issue on Automated Perception of Human Affect from Longitudinal Behavioral Data Website: https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/tacSpecialIssue201… I. Aim and Scope Research trends within artificial intelligence and cognitive sciences are still heavily based on computational models that attempt to imitate human perception in various behavior categorization tasks. However, most of the research in the field focuses on instantaneous categorization and interpretation of human affect, such as the inference of six basic emotions from face images, and/or affective dimensions (valence-arousal), stress and engagement from multi-modal (e.g., video, audio, and autonomic physiology) data. This diverges from the developmental aspect of emotional behavior perception and learning, where human behavior and expressions of affect evolve and change over time. Moreover, these changes are present not only in the temporal domain but also within different populations and more importantly, within each individual. This calls for a new perspective when designing computational models for analysis and interpretation of human affective behaviors: the computational models that can timely and efficiently adapt to different contexts and individuals over time, and also incorporate existing neurophysiological and psychological findings (prior knowledge). Thus, the long-term goal is to create life-long personalized learning and inference systems for analysis and perception of human affective behaviors. Such systems would benefit from long-term contextual information (including demographic and social aspects) as well as individual characteristics. This, in turn, would allow building intelligent agents (such as mobile and robot technologies) capable of adapting their behavior in a continuous and on-line manner to the target contexts and individuals. This special issue aims at contributions from computational neuroscience and psychology, artificial intelligence, machine learning, and affective computing, challenging and expanding current research on interpretation and estimation of human affective behavior from longitudinal behavioral data, i.e., single or multiple modalities captured over extended periods of time allowing efficient profiling of target behaviors and their inference in terms of affect and other socio-cognitive dimensions. We invite contributions focusing on both the theoretical and modeling perspective, as well as applications ranging from human-human, human-computer and human-robot interactions. II. Potential Topics Given computational models, the capability to perceive and understand emotion behavior is an important and popular research topic. That is why recent special issues on the IEEE Journal on Transactions on Affective Computing covered topics from emotion behavior analysis “in-the-wild” to personality analysis. However, most of the research published by these specific calls treat emotion behavior as an instantaneous event, relating mostly to emotion recognition, and thus neglect the development of complex emotion behavior models. Our special issue will foster the development of the field by focusing excellent research on emotion models for long-term behavior analysis. The topics of interest for this special issue include, but are not limited to: - New theories and findings on continuous emotion recognition - Multi- and Cross-modal emotion perception and interpretation - Lifelong affect analysis, perception, and interpretation - Novel neural network models for affective processing - New neuroscientific and psychological findings on continuous emotion representation - Embodied artificial agents for empathy and emotion appraisal - Machine learning for affect-driven interventions - Socially intelligent human-robot interaction - Personalized systems for human affect recognition III. Submission Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Affective Computing guidelines ( https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165369) Please submit your papers through the online system ( https://mc.manuscriptcentral.com/taffc-cs) and be sure to select the special issue: Special Issue/Section on Automated Perception of Human Affect from Longitudinal Behavioral Data. IV. IMPORTANT DATES: Submissions Deadline: 15th of January 2019 Deadline for reviews and response to authors: 06th of April 2019 Camera-ready deadline: 05th of August 2019 V. Guest Editors Pablo Barros, University of Hamburg, Germany Stefan Wermter, University of Hamburg, Germany Ognjen (Oggi) Rudovic, Massachusetts Institute of Technology, United States of America Hatice Gunes, University of Cambridge, United Kingdom -------------------------- -- Dr. Pablo Barros Postdoctoral Research Associate - Crossmodal Learning Project (CML) Knowledge Technology Department of Informatics University of Hamburg Vogt-Koelln-Str. 30 22527 Hamburg, Germany Phone: +49 40 42883 2535 Fax: +49 40 42883 2515 barros at informatik.uni-hamburg.dehttp://www.pablobarros.nethttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.htmlhttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/

Oct. 18, 2018

NEURON course at SFN 2018 meeting

by Ted Carnevale

A few seats remain open for the NEURON course that we will be presenting at the SFN 2018 meeting in San Diego. However, the registration deadline is Friday, October 19--just three days from today, so you'll have to act quickly if you are interested in learning things like * why you should be using NEURON to model biological neurons and neural circuits * how to get started building models with NEURON's GUI and/or Python * advanced tips for getting the most out of using Python as NEURON's interpreter, and using NEURON's GUI for interactive modeling and debugging of models implemented with Python * using the Import3d tool to convert Neurolucida, Eutectic, and SWC morphometric data files to NEURON models * how to add new ion channels and other biophysical mechanisms to NEURON For the course description and registrion form, see https://neuron.yale.edu/neuron/static/courses/sd2018/sd2018.html --Ted This message was composed in plain text and may contain plain text formatting of source code, tables, and lists. Reading it in a client that ignores "excess" whitespace characters or line breaks may destroy that formatting, making this message unreadable. Outlook does that by default; learn how to disable the "Auto Remove Line Breaks" ""feature"" at support.microsoft.com

Oct. 16, 2018

Postdoctoral position in circuit modelling at Nottingham, deadline 7th November

by Mark Humphries

A postdoctoral position is available in the lab of Prof Mark Humphries at the University of Nottingham. We seek a postdoctoral researcher to tackle the deep question of how dopamine controls the dynamics of the striatum. As the main target of dopamine in the brain, the striatum bears the brunt of dopamine loss in Parkinson’s disease. Yet how dopamine controls the output of the striatum, and how the loss of dopamine disrupts that output, is unknown. The successful candidate will be responsible for constructing and simulating models of the striatum to investigate how dopamine controls the striatum’s dynamics, and the implications this will have for Parkinson’s disease. The project will build upon the detailed models of the striatum from the Humphries group (e.g. Humphries et al 2009 Neural Networks; 2010, PLoS Computational Biology). This is also a chance to join the newly-created computational neuroscience centre at Nottingham, which includes Profs Mark van Rossum and Stephen Coombes. Find out more about the lab and its interests here: https://www.humphries-lab.org/ The post will be available for 18 months in the first instance. More details, eligibility, and application instructions here: https://www.nottingham.ac.uk/jobs/currentvacancies/ref/SCI353818 Deadline: 7th November 2018 To discuss this or other projects, please contact Mark (mark.humphries(a)nottingham.ac.uk)

Oct. 16, 2018

Fully-funded PhD in computational neuroscience at Nottingham, deadline 31st October

by Mark Humphries

A fully funded PhD position in Computational Neuroscience is available with Prof. Mark Humphries at the School of Psychology, University of Nottingham. Deadline: October 31st Full details and to apply: https://www.nottingham.ac.uk/psychology/news/funded-phd-opportunity-prof-ma… The Humphries’ lab interrogates how the joint activity of many neurons encodes the past, present, and future, in order to guide behaviour. We combine simulations of neural circuits with “neural data science” – the development and use of machine-learning approaches to systems neuroscience data. We are particularly interested in PhD projects on the following research questions: * “Mapping the wires from neural activity”. How to use the next generation of imaging techniques that directly record neuron voltages to accurately infer the wiring between neurons. * “How to write activity into neurons". What features of neural activity should we change with optogenetics in order to test hypotheses about population activity? * “Dynamic control of the striatum in health and disease”. How the influence of the striatal interneurons is controlled by dopamine, and how that influence goes haywire when dopamine is lost in Parkinson’s disease. For a primer on our research, and our recent publications, visit our lab website: /https://www.humphries-lab.org/ To discuss these or other projects, please contact Prof Mark Humphries (mark.humphries(a)nottingham.ac.uk)

Oct. 16, 2018

Master-PhD studentship:Studying the physiological synergy of different ion channels in neurons

by luiza filipis

M2 stage (April – July 2019) and possibility to continue with a 3-years PhD, starting in October 2019, with scholarship already granted Studying the physiological synergy of different ion channels in neurons Following the recent award of a competitive National grant, we are looking for a brilliant and motivated undergraduate student, with biological and/or physical background, to perform a final stage at the last University level (Master-2 or equivalent), starting April 1st 2019 or earlier if possible and ending in July 2019. The student will receive a stipend of ~500€/month for the entire duration of the stage. The objective of the project is to combine advanced imaging techniques applied to neurons in brain slices, novel photoactivable toxins to target individual ion channels and advanced computational techniques to extract the precise activity of multiple ion channels underlying neuronal signals. The student will learn patch clamp and imaging techniques and apply those to test novel photoactivable toxins targeting calcium channels. At the end of the stage, if positively evaluated, a 3-year PhD with an already available scholarship from the labex consortium ICST (http://www.labex-icst.fr/en) will be offered to start on October 1st 2019. The criteria for selecting the students will include (in order of importance) - The academic record of the student (notes) with respect to the disciplines associated with the project. (For students coming from French Universities, an average note of >13/20 in the M1 and in the first semester of M2 is compulsory to be considered). - Motivation to carry on a career in scientific research as the following PhD opens the gate to further postdoctoral experiences outside France. - Personal attitude to laboratory life including relationships with colleagues. Applications, addressed to Dr Marco Canepari ( marco.canepari(a)univ-grenoble-alpes.fr) should contain: 1. a CV based on the academic record including notes (at least the most relevant and recent); 2. a motivation letter; 3. (optional) one or two supporting letters by academic staff or name(s) and email address(es) of one or two referees who know the candidate. Applications are open until November 10th 2018 and the selected student will be decided by December 1st 2018. More information on the project and on the research activity of our group is available at: -- Luiza Filipis,PhD Student Equipe MOTIV Laboratoire Interdisciplinaire de Physique (LIPhy), Université Grenoble Alpes et CNRS UMR 5588, Bat. E45, 140 avenue de la physique, Domaine univ., 38402 St Martin d'Hères cedex, FRANCE 33(0)476514731, Fax +33(0)476635495. luiza.filipis(a)univ-grenoble-alpes.fr http://marco-canepari.wix.com/neuron-imaging-team

Oct. 16, 2018

Funded Mathematics PhD at the University of Exeter - Designing the dynamics of coupled oscillators

by Purcell, Sarah

Designing the dynamics of coupled oscillators<http://www.exeter.ac.uk/studying/funding/award/?id=3301> Mathematics PhD (funded) University of Exeter Supervisors: Dr Christian Bick<http://emps.exeter.ac.uk/mathematics/staff/cb618> - University of Exeter Dr Kyle Wedgwood<http://emps.exeter.ac.uk/mathematics/staff/kcaw201> - University of Exeter The University of Exeter's College of Engineering, Mathematics and Physical Sciences is inviting applications for a fully-funded PhD studentship to commence in January 2019 or as soon as possible thereafter. For eligible students the studentship will cover tuition fees (UK/EU/International) in full plus an annual tax-free stipend of at least £14,777 for 3.5 years full-time, or pro rata for part-time study. The student would be based in the Department of Mathematics in the College of Engineering, Mathematics and Physical Sciences in Exeter. Networks of interacting oscillatory units are abundant in nature and technology. Their function and dynamics governs crucial aspects of our lives, ranging from the stability of power grids to the dynamics of the human brain. The dynamics of oscillator networks depends on (1) which oscillators interact with which other oscillator and (2) how oscillators interact with one another. This project will contribute to the question how the interplay between these two factors shape the network dynamics. The main aim of this PhD project is to develop theoretical and numerical approaches how oscillator networks can be designed to have specific interactions. This will allow to generate networks that exhibit a range of synchronized and locally synchronized dynamics. Moreover, the supervisor is currently collaborating with the experimental group of Istvan Z Kiss in St Louis (USA) who are working on electrochemical oscillators. Hence, there may be an opportunity to help implement the theoretical results in real-world experimental systems. Entry requirements Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology. If English is not your first language you will need to have achieved at least 6.5 in IELTS and no less than 6.0 in any section by the start of the project. Alternative tests may be acceptable (see http://www.exeter.ac.uk/postgraduate/apply/english/)<http://see%20http:/www.exeter.ac.uk/postgraduate/apply/english/)> For more information, please contact Dr. Christian Bick (C.Bick(a)exeter.ac.uk<mailto:C.Bick@exeter.ac.uk>) or Dr. Kyle Wedgwood (K.C.A.Wedgwood(a)exeter.ac.uk<mailto:K.C.A.Wedgwood@exeter.ac.uk>) or see http://www.exeter.ac.uk/studying/funding/award/?id=3301

Oct. 16, 2018

Interviews at SfN 2018 for a Postdoctoral Fellow in Neural Decoding and Data Analytics Lab - Center for Bioelectronic Medicine

by Zanos, Theodoros

Attending Neuroscience 2018 in San Diego? Apply to be considered for an interview during the conference by sending your CV to tzanos(a)northwell.edu The Neural Decoding and Data Analytics Lab at the Center for Bioelectronic Medicine, headed by Assistant Professor Theo Zanos PhD, focuses on neural recordings and data analysis methods to gain insights into decoding and encoding mechanisms of the brain and peripheral nerves to and from stimuli, actions and other physiological functions. The lab utilizes a variety of techniques from signal processing, machine learning, optimization, neurophysiology and bioelectronic systems for closed-loop neural recording and stimulation, both in health and in disease models. The ultimate goal is to develop neuromodulation therapies that will observe the state of the organism and the organ system that is targeted, to deliver individualized treatment of diseases in real-time. The successful candidate will develop or deploy computational tools, will take part in neurophysiology experiments and collect data, organize and analyze data, collaborate with other research staff and publish and present data in a timely manner. They will also work collaboratively with other investigators to facilitate their scientific research and training. Furthermore, they will participate in various types of institute and community activities and may apply for grant funding and/or patents as appropriate. The position comes with considerably competitive compensation. Job Responsibilities Include: Develops computational tools for neuroscience research. Run neurophysiology experiments. Performs bioelectronic medicine research. Collaborates with other research staff. Responsible for the planning, collection, organization, analysis, and interpretation of research data related to one or more projects Doctorate degree in Engineering, Computer Science or Statistics (Ph.D) required Authored/co-authored research papers and/or scientific abstracts, required Experience with various signal processing and machine learning methods, required Strong programming skills of Matlab, required, Python, C++, preferred Research experience in neurophysiology/neuroscience/animal surgery, preferred The information contained in this electronic e-mail transmission and any attachments are intended only for the use of the individual or entity to whom or to which it is addressed, and may contain information that is privileged, confidential and exempt from disclosure under applicable law. If the reader of this communication is not the intended recipient, or the employee or agent responsible for delivering this communication to the intended recipient, you are hereby notified that any dissemination, distribution, copying or disclosure of this communication and any attachment is strictly prohibited. If you have received this transmission in error, please notify the sender immediately by telephone and electronic mail, and delete the original communication and any attachment from any computer, server or other electronic recording or storage device or medium. Receipt by anyone other than the intended recipient is not a waiver of any attorney-client, physician-patient or other privilege.

Oct. 16, 2018

Research Scientist Position at Samsung

by Daniel Kepple

Samsung is recruiting computational neuroscientists for a new AI Research lab in New York City that will conduct fundamental research at the intersection of AI, robotics, and neuroscience. The position requires motivation to advance fundamental science and create revolutionary technological prototypes. Team members will have the intellectual freedom to cultivate their research roots by partnering with universities and publishing in leading scientific conferences and journals. Research Scientists in Machine Learning tackle unsolved problems in neural network architectures, learning algorithms, reinforcement learning, computer vision, natural language, and multimodal sensory processing. We will be at SfN, so if you are interested in learning more or applying, please send an email with your CV to d.kepple(a)samsung.com<mailto:d.kepple@samsung.com> See the following link for more details: https://samsungresearchamerica.applytojob.com/apply/IgDHsUiaPI/Research-Sci… Thanks, Daniel Kepple Research Scientist at Samsung AI Center, NY

Oct. 15, 2018

Funded PhD position in Statistical Analysis of Neural Data at UConn

by Ian Stevenson

The Statistical Neuroscience Lab at the University of Connecticut is recruiting PhD students to start Fall 2019. These are fully-funded positions, and candidates will be working in close collaboration with experimental collaborators to develop methods for modeling neural dynamics, interactions, and plasticity from large-scale spike train data. Qualifications: Our work is highly interdisciplinary, and applicants must have strong quantitative skills. Previous experience with neuroscience is helpful but not necessary. Candidates with backgrounds in physics, statistics, computer science, engineering, and related areas are welcome to apply. Applying: Interested students should apply through the UConn Neuroscience (*https://grad.psych.uconn.edu/prospective/phd/application-deadlines/ <https://grad.psych.uconn.edu/prospective/phd/application-deadlines/>*) or Biomedical Engineering (*http://www.bme.uconn.edu/academics/graduate-program/about-the-graduate-program-in-biomedical-engineering <http://www.bme.uconn.edu/academics/graduate-program/about-the-graduate-prog…>*) PhD programs. Deadlines are Feb 1 for Neuroscience and Jan 1 for BME. Contact ian.stevenson(a)uconn.edu for more information Thanks, Ian Stevenson Assistant Professor University of Connecticut Department of Psychological Sciences stevenson.lab.uconn.edu

Oct. 15, 2018

tenure track faculty searches at Georgia Tech

by Rozell, Christopher J

There are a number of tenure track faculty searches at Georgia Tech that may be of interest to this community. While only some are targeting neuro specifically, many have neuro as an area of specific interest. The ads are aggregated here: http://neuro.gatech.edu/career For this community, I would draw your attention to the searches in computationally-oriented departments such as Electrical & Computer Engineering (ECE), Math, and Computational Science and Engineering (CSE). Georgia Tech's campus is in the heart of midtown Atlanta, which is one of the fastest-growing metropolitan areas (and entrepreneurial communities) in the United States. Atlanta boasts a wide range of opportunities for recreation, culture, and easy access to domestic and international transportation. The neuroscience community is strong and rapidly growing both at Georgia Tech and at our local partners Emory (including Emory Medical School) and Georgia State University. In particular, there is a specific current and historical strength in computational neuroscience in the local area, with a strong and collegial community. More info about our local community can be found here: http://neuro.gatech.edu/ Please contact me if you have questions. regards, chris rozell

Oct. 15, 2018