[Comp-neuro] Postdoc Position in Modelling Functional Whole Brain Connectivity from fMRI

Postdoc Position in Modelling Functional Whole Brain Connectivity from fMRI

 

DTU Compute, Technical University of Denmark would like to invite applications for a 1-year Post Doc position starting as early as 1 November 2013, with possibility for an extension to two years.

Project Description
Functional magnetic resonance imaging (fMRI) has become a key non-invasive brain imaging technique used to quantify the functional connectivity of the brain. While this imaging modality can be used to derive connectivity estimates inferring the global patterns of functional connectivity at the whole brain level poses an important modeling challenge.

The main goal of this project is to develop and apply tools for the modeling of whole brain functional connectivity based on fMRI data. Key components in the project include fMRI data processing and connectivity analysis using Bayesian modeling approaches.  The project will be based on current work on non-parametric Bayesian models of brain connectivity and will be part of a larger project "Non-parametric Relational Modelling of Functional and Structural Brain Connectivity" funded by the Lundbeck Foundation (2012-2017) aiming at inferring whole brain connectivity from fMRI and diffusion MRI data.

Qualifications
Candidates must have a PhD degree in engineering or equivalent academic qualifications. Experience in multivariate modeling approaches for neuroimaging data in general and fMRI data in particular is a definite advantage. Proficiency programming in Matlab and using fMRI neuroimaging software is also important while the applicant is expected to have a basic knowledge about the human brain. Excellent communication skills in English are essential.

We offer
We offer an interesting and challenging job in an international environment focusing on education, research, public-sector consultancy and innovation, which contribute to enhancing the economy and improving social welfare. We strive for academic excellence, collegial respect and freedom tempered by responsibility. The Technical University of Denmark (DTU) is a leading technical university in northern Europe and benchmarks with the best universities in the world.

Salary and terms of appointment
The appointment will be based on the collective agreement with the Confederation of Professional Associations. The allowance will be agreed with the relevant union.

Further Information
Further information concerning the available position can be obtained from Associate Professor Morten Mørup at Section for Cognitive Systems, DTU Compute, Technical University of Denmark or Senior Researcher Kristoffer Hougaard Madsen at Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre.

Application procedure:
Please submit your online application no later than 20 October 2013. Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please visit http://www.compute.dtu.dk/english/about_us/vacant_jobs/a58fd704-92b0-495c-abcd-867310aba4a7.aspx and open the link  "Apply online", fill in the online application form, and attach all your materials in English in one PDF file. The file must include:

·         Application (cover letter)

·         CV

·         Diploma

·         List of publications

Applications and enclosures received after the deadline will not be considered.

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply.

DTU Compute conducts research and education in the fields of mathematical modelling and computer science. The expanding mass of information and the increasingly complex use of advanced technology in society demand development of advanced computer based mathematical models and calculations. The unique competences of the institute are in demand in IT innovation and production.

DTU is a technical university providing internationally leading research, education, innovation and public service. Our staff of 5,000 advance science and technology to create innovative solutions that meet the demands of society; and our 9,000 students are educated to address the technological challenges of the future. DTU is an independent academic university collaborating globally with business, industry, government, and public agencies.

[Comp-neuro] CFP MLINI WS on Machine Learning and Inference in Neuroimaging @ NIPS

MLINI-2013: 3rd Workshop on Machine Learning and Inference in
Neuroimaging at NIPS-2013

https://sites.google.com/site/mlininips2013/

December 9-10, 2013, Lake Tahoe, Nevada, United States

Submission deadline: October 11th, 2013

Workshop Overview:
-----------------------
MLINI is a two day workshop on the topic of machine learning approaches
in
neuroscience and neuroimaging. The goal of the workshop is to pinpoint
the
most pressing issues and common challenges across the fields, and to
sketch
future directions and open questions in the light of novel methodology.
The
workshop aims at providing a forum that joins machine learning,
neuroscience, psychology and psychiatry community, and should facilitate
formulating and discussing the issues at their interface. Motivated by
two
previous workshops, MLINI '11 and MLINI'12, we will center this workshop
around invited talks, and two panel discussions. Triggered by these
discussions, this year we plan to adapt the workshop topics to a less
traditional scope that investigates the role of machine learning in
neuroimaging of both animals and humans, as well as in behavioral models
and psychology.

Open questions and possible topics for contribution will be structured
around the following 4 main topics:

- Machine learning and pattern analysis methodology in neuroimaging
- Functional networks and dynamical models of the brain
- Multi-modal analysis of mental state inference from imaging and/or
behavioral data
- Linking machine learning, neuroimaging and neuroscience

Workshop Format:
--------------------------

In this two-day workshop we will explore perspectives and novel
methodology
at the interface of Machine Learning, Inference, Neuroimaging and
Neuroscience. We aim to bring researchers from machine learning and
neuroscience community together, in order to discuss open questions,
identify the core points for a number of the controversial issues, and
eventually propose approaches to solving those issues.
Each session will be opened by 2-3 invited talks, and an in depth
discussion. This will be followed by original contributions. Original
contributions will also be presented and discussed during a poster
session.
The workshop will end with a panel discussion, during which we will
address
specific questions, and invited speakers will open each segment with a
brief presentation of their opinion.

Paper Submission:
--------------------------
We seek for submission of original (previously unpublished) research
papers. The length of the submitted papers should not exceed 8 pages in
Springer format, excluding the references (LaTeX2e style files are
available on the workshop page).
Submission of previously published work is possible as well, but the
authors are required to mention this explicitly. Previously published
work
can be presented at the workshop, but will not be included into the
workshop proceedings (which are considered peer-reviewed publications of
novel contributions). Moreover, the authors are welcome to present
their
novel work but choose to opt out of the workshop proceedings in case
they
have alternative publication plans.

Important dates:
--------------------------
- October 11, 2013 - paper submission
- October 26, 2012 - notification of acceptance/rejection
- December 9-10, 2013 - Workshop in Lake Tahoe, Nevada US, following
the
NIPS conference

Organizing Committee:
--------------------------
Guillermo Cecchi (IBM T.J. Watson Research Center)
Kai-min Kevin Chang (Language Technologies Institute, Carnegie Mellon
University)
Georg Langs (Medical University of Vienna, CSAIL, MIT)
Brian Murphy (Knowledge & Data Engineering, Queen's University Belfast)
Irina Rish (IBM T.J. Watson Research Center)


--
Dr. Brian Murphy
Lecturer (Assistant Professor)
Knowledge & Data Engineering (EEECS)
Queen's University Belfast
brian.murphy@qub.ac.uk

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[Comp-neuro] NeuroEng 2014, the 7th Australian Workshop on Computational Neuroscience

NeuroEng 2014, the 7th Australian Workshop on Computational Neuroscience

Date: 2-3 February, 2014

Venue: National Wine Centre of Australia, The University of Adelaide, South Australia

Bringing together computational neuroscientists and researchers at the interface between neuroscience and engineering

• Theoretical, Mathematical & Computational Neuroscience  •  Neuroimaging & Connectomics  •  EEG Analysis  •  Neural Modeling & Engineering  •  Neuromorphic Engineering  •  Neuroprosthetics & Brain Machine Interfaces  •  Neural Information Processing  •  Neuroinformatics  •  Cognitive Modeling

Keynote speakers:

Professor Eric L. Schwartz, Boston University, USA
Professor Tobi Delbrück, ETH Zurich, Switzerland
Professor Eric Warrant, Lund University, Sweden

The workshop is a satellite of the 34th Annual Meeting of the Australasian Neuroscience Society to be held at the Adelaide Convention Centre (Jan 28-31).

Abstract submission deadline, 1 November 2013. Register for the workshop online at www.neuroeng.org.au/neuroeng2014/ 

The early bird period closes 12 December 2013.

A social function (day tour in an Adelaide wine region) will be held on Saturday 1st February and will conclude with a general welcome event in the evening.  For more information about South Australia, visit www.southaustralia.com. The complimentary workshop dinner will be held at the National Wine Centre on Sunday 2nd February. The dinner will commence after the poster session that will follow the day's oral presentations.

 
For enquiries, please contact: Dr Steven Wiederman 

www.adelaide.edu.au/directory/steven.wiederman

We thank our sponsors:

Adelaide Centre for Neuroscience Research, The University of Adelaide  •  Flinders University  •  South Australian Neuroscience Institute  •  The Swedish Foundation for International Cooperation in Research and Higher Education  •  University of South Australia  •  SDR Scientific

[Comp-neuro] University of Kent PhD studentship on EEG-based mental fatigue study

 

PhD Scholarship available Brain Signal Analysis using EEG

 

Research Theme              : This project aims to perform analysis on EEG (Electroencephalography) data to investigate mental fatigue. More generally, we aim to find an indicator for the identification of brain states (i.e. fatigue, non-fatigue) using novel methods. It might have a wide range of applications (i.e. military, sports and medical applications).

 

Key words                   : EEG, time-frequency analysis, phase synchrony, machine learning, pattern recognition, general linear model

 

Location                      : University of Kent at Medway, UK (40-50 mins from London by train)

Supervisor                  : Dr. Caroline Ling Li

Co-supervisors           : Prof. Samuele Marcora, Prof. Howard Bowman

 

Funding

Three-year PhD scholarship is available to UK, EU and overseas students and will cover home tuition fees plus a maintenance grant equivalent to the full UK Research Council rate.

 

Criteria 

Applicant must have a good first degree or good Master’s level degree. Ideally at Master level with relevant research experience. Strong programming or/and math skill is preferred. Previous research experience leading to publications would be an advantage.

 

How to apply 

Send covering letter and CV together with contacts of two referees to c.li@kent.ac.uk, including contact detail, research interest and your suitability for this award. Please also include experience, if any, of academic or professional research; experience, if any, of teaching or mentoring.

 

You must also complete the online form for making a formal application for a PhD (Doctor of Philosophy Research) in Computer Science at the University of Kent via

http://www.kent.ac.uk/courses/postgrad/apply/index.html

 

Successful candidate will be based at Medway Campus. Information about Medway Campus can be found here: http://www.kent.ac.uk/locations/medway/campus/index.html

 

For more information, please go to: http://www.cs.kent.ac.uk/people/staff/cl339 or contact c.li@kent.ac.uk

Kent provides a dynamic and challenging academic environment and has an excellent reputation for collaborative research with universities around the world. For more information about Kent’s research portfolio see: www.kent.ac.uk/pg

 

====================================================================================

Caroline Li (Ph.D)| Lecturer |School of Computing |University of Kent

Founder of Brain|Cognition|Computing Lab
M-318, Medway Building, University of Kent, Chatham, ME4 4AG
Tel: +44(0)1634 202987 | Mobile: +44 (0) 79355 75488 | Skype: llwelcome921

 

Web: http://www.cs.kent.ac.uk/people/staff/cl339/index.html | E-Mail : c.li@kent.ac.uk

====================================================================================

 

 

[Comp-neuro] ICBR 2013 - Call for Papers - IEEE - Tunisia

 Apologies for multiple postings.

 

*** ICBR 2013 - Call for Papers ***

 

--- The Individual and Collective Behaviors in Robotics (ICBR 2013) ---

*** Tunisia ***

15-17, December 2013

 

http://icbr.regim.org/

 

Related Robotics Competition: 4th International Robotics Competition (RoboComp 2013) http://robocomp.regim.org/

 

 

Publication: IEEE

------------------------------

 

ICBR'2013 is Technically co-sponsored by the IEEE Robotics & Automation Society.
The robotics field borrows its knowledge from different disciplines: mathematics and logic (formalization, modeling of behaviors), engineering (development of increasingly complex microprocessors, development of new architectures, new sensors, new communication tools, new control systems, etc.), neurosciences (comprehension of the brain, new methods dealing with computational collective intelligence), biology and Natural Science (living observation, bio-inspiration, stigmergy, etc.), psychology (validation of theories in relation to the memory functioning, language, individual and collective behavior, etc.), linguistics (models of interactive language handling), social Sciences (studies of adaptation to environment and to collective works), philosophy (questions related to the thought character).
The aim of ICBR conference is to bring together researchers in automation and behavioral aspects for robotics; it helps us exchange innovative ideas in designing robots and also in applying computational and collective intelligence, and benefit the progress of each research theory and application.

 

 

~~~TOPICS~~~

• Kinematics, Dynamics, and Control: Dexterous Manipulation, Locomotion, Nonlinear Control, Visual Servoing
• Planning and Algorithms: Motion Planning, Task Planning, Coordination, Complexity and Completeness, Computational Geometry, Simulation,
• Human-Robot Interaction and Human Centered Systems: Brain-Machine Interfaces, Haptics, Tactile Interfaces, Telerobotics, Human Augmentation, Assistive Robots, Social Robots, Safe Interaction, Robots and Art
• Collective Behaviors  models: Multi-Robot Systems (MRS), Networked Robots, Robot Soccer, bio-inspired MRS, Evaluation of collective behaviors
• Field Robotics: Underwater Robotics, Aerial/Space Robotics, Agricultural and Mining Robotics
• Medical Robotics: Robot-Assisted Procedures, Smart Surgical Tools, Rehabilitation Robotics, Interventional Therapy, Image-Guided Procedures, Surgical Simulation, Soft-Tissue Modeling, Telesurgery
• Biological Robotics: Biomimetic Robotics, Robotic Investigation of Biological Science and Systems, Neurobotics, Prosthetics, Robotics and Molecular Biology
• Mechanisms: Design, Humanoids, Hands, Legged Systems, Snakes, Novel Actuators, Reconfigurable Robots, MEMS/NEMS, Micro/Nanobots
• Robot Perception: Vision, Tactile and Force Perception, Range Sensing, Inertial and propreoceptive sensing, Sensor Fusion
• Mobile Systems and Mobility: Mapping, Localization, Navigation, SLAM, Collision Avoidance, Exploration
• Estimation and Learning for Robotic Systems: Reinforcement Learning, Bayesian Techniques, Graphical Models, Imitation Learning, Programming by Demonstration, Diagnostics

 

 

Author Guidelines:

---------------------------------------------

Submission of paper should be made through the submission link:

https://www.easychair.org/conferences/?conf=icbr2013

 

Submissions should be up to 6 pages, in double column IEEE proceeding format. Submitted papers will be double-blind reviewed by the Program Committee on the basis of novelty, relevance, and technical quality.

Conference content will be submitted for inclusion into IEEE Xplore as well as other Abstracting and Indexing (A&I) databases.

 

 

Important Dates:

---------------------------------------------

Paper submission due: September 30, 2013 extended

Notification of paper acceptance: October 27, 2013

Final manuscript due: November 17, 2013

 

 

ICBR 2013 team:

[Comp-neuro] Research topic on the speed-accuracy trade-off

Dear colleagues - apologies for cross-posting. Please see below the details of the Frontiers Research Topic "Toward a unified view of the speed-accuracy trade-off: behaviour, neurophysiology and modelling", hosted by Frontiers in Decision Neuroscience. The manuscript submission deadline is December 1. Please let me know if you're interested in submitting a manuscript or if you have questions about the suitability of material for the topic.

 

--

Toward a unified view of the speed-accuracy trade-off: behaviour, neurophysiology and modelling

 

http://www.frontiersin.org/Decision_Neuroscience/researchtopics/Toward_a_unified_view_of_the_s/1647

 

Topic Editors:

 

Dominic Standage, Queen's University, Canada

Da-Hui Wang, Beijing Normal University, China

Richard P. Heitz, Vanderbilt University, USA

Patrick Simen, Oberlin College, USA

 

Deadline for full article submission: 01 Dec 2013

 

When we make faster decisions, we make more mistakes. When we make slower decisions, we miss more deadlines and we limit the number of decisions we can make. These principles are intuitively obvious and are applicable to decisions in any domain, on any timescale, by any species or automated system. Their resolution defines the speed-accuracy trade-off (SAT). The SAT has long been the subject of experimental and theoretical enquiry. In experiments, subjects make slower, more accurate decisions when motivated to favour accuracy and make faster, less accurate decisions when motivated to favour speed. Computationally, the SAT is well characterized within the framework of bounded integration, where noisy evidence for the alternatives is integrated over time. When the evidence for one of the alternatives reaches a threshold or bound, a choice is made for that alternative. Higher thresholds therefore favour accuracy at the expense of speed. This framework captures a remarkable volume of experimental data, but there is a prominent discrepancy between model fits to behavioural and electrophysiological data. The latter have been taken to suggest that decision thresholds are fixed. If so, then how do we trade speed and accuracy? This question is the focus of intense research interest.

 

A growing number of studies have investigated the neural mechanisms underlying the SAT within the framework of bounded integration, where a convergence of neuroimaging and electrophysiological methods with mathematical and biophysical modelling has provided new perspectives on the mechanisms by which decision times are determined. For example, with a fixed decision threshold, the time spent integrating evidence may be adjusted by modulating the baseline activity of neural integrators, the rate and onset of integration, or the functional connectivity between integrators and other sources of input to thresholding circuitry. Furthermore, there is increasing evidence that the encoding of elapsed time plays a crucial role in the SAT, sometimes referred to as urgency. These and other hypotheses suggest that the conflicting demands of speed and accuracy may be resolved by the differential encoding, readout and integration of evidence. Striking the optimal balance between speed and accuracy in a given context further requires a means to control these mechanisms. In this Research Topic, we welcome articles that characterize or explain the SAT and its optimization according to any experimental factor or neural mechanism, using any experimental or theoretical methodology. While we take temporal integration as a starting point, we encourage articles expressing disagreement with the premises of the bounded integration framework and reporting evidence in favour of alternative explanations of the SAT. All Frontiers article types are welcome, including original research articles, methods articles, hypothesis and theory articles, opinions, perspectives and reviews.

--

 

Best

 

Dominic Standage

Research Scientist

Department of Biomedical and Molecular Sciences / Centre for Neuroscience Studies

Queen's University, Botterell Hall, Room 230

Kingston, Ontario, Canada K7L 3N6

Tel: (613) 533-6000 (ext 77446)

Email: standage@queensu.ca

 

 

[Comp-neuro] NIPS 2013 Workshop Call For Papers: High-dimensional Statistical Inference in the Brain

   
            NIPS WORKSHOP 2013 CALL FOR PAPERS
       High-dimensional Statistical Inference in the Brain
                Monday, December 9th, 2013
                  Lake Tahoe, Nevada USA
--------
Organizers:
 Mitya Chklovskii
 Alyson Fletcher
 Fritz Sommer
 Ian Stevenson
--------

Overview:
Understanding high-dimensional phenomena is at the heart of many
fundamental questions in neuroscience. How does the brain process
sensory data? How can we model the encoding of the richness of the
inputs, and how do these representations lead to perceptual
capabilities and higher level cognitive function? Similarly, the
brain itself is a vastly complex nonlinear, highly-interconnected
network and neuroscience requires tractable, generalizable models
for these inherently high-dimensional neural systems.

Recent years have seen tremendous progress in high-dimensional
statistics and methods for ``big data" that may shed light on
these fundamental questions. This workshop seeks to leverage these
advances and bring together researchers in mathematics, machine
learning, computer science, statistics and neuroscience to explore
the roles of dimensionality reduction and machine learning in
neuroscience.

Call for Papers
We invite high quality submissions of extended abstracts on topics including,
but not limited to not limited to, the following fundamental questions:

-- How is high-dimensional sensory data encoded in neural systems?
What insights can be gained from statistical methods in dimensionality
reduction including sparse and overcomplete representations?
How do we understand the apparent dimension expansion in higher level
cognitive functions from a machine learning and statistical perspective?

-- What is the relation between perception and high-dimensional statistical
inference? What are suitable statistical models for natural stimuli
in vision and auditory systems?

-- How does the brain learn such statistical models? What are the connections
between unsupervised learning, latent variable methods, online learning
and distributed algorithms? How do such statistical learning methods
relate to and explain experience-driven plasticity and perceptual learning in
neural systems?

-- How can we best build meaningful, generalizable models of the brain with
predictive value? How can machine learning be leveraged toward better design
of functional brain models when data is limited or missing? What role can
graphical models coupled with newer techniques for structured sparsity play
in this dimensionality reduction?

-- What are the roles of statistical inference in the formation and retrieval
of memories in the brain? We wish to invite discussion on the very open
questions of multi-disciplinary interest: for memory storage, how does the
brain decode the strength and pattern of synaptic connections?  Is it
reasonable to conjecture the use of message passing algorithms as a model?

-- Which estimation algorithms can be used for inferring nonlinear and
inter-connected structure of these systems? Can new compressed
sensing techniques be exploited? How can we model and identify
dynamical aspects and temporal responses?

We have invited researchers from a wide range of disciplines in electrical
engineering, psychology, statistics, applied physics, machine learning
and neuroscience with the goals of fostering interdisciplinary insights.  
We hope that active discussions between these groups can set in motion
new collaborations and facilitate future breakthroughs
on fundamental research problems.


Submissions should be in the NIPS_2013 format
(include link http://nips.cc/Conferences/2013/PaperInformation/StyleFiles)
with a maximum of four pages, not including references.

Dates:
Submission deadline: 23 October, 2013 11:59 PM PDT (UTC -7 hours)
Acceptance notification: 30 October , 2013

Web: http://users.soe.ucsc.edu/~afletcher/hdnips2013.html
email: hdnips2013@rctn.org


Organizers:
Mitya Chklovskii,  HHMI Janelia Farm
Allie Fletcher,   UCSC
Fritz Sommer,   UC Berkeley
Ian Stevenson,  University of Connecticut

Confirmed Speakers
Liam Paninski,  Columbia University
Maneesh Sahani,  University College London
Jonathon Pillow,   University of Texas
Surya Ganguli,  Stanford University
Matthias Bethge,  University of Tuebingen