5th G-Node Winter Course in Neural Data Analysis
February 25 - March 1, 2013 in Munich, Germany
The German Neuroinformatics Node (G-Node) organizes its fifth
international training course to promote state-of-the-art methods
of neural data analysis among PhD students and postdocs.
The course offers hands-on experience with model-driven analysis
of data from intra- and extracellular electrophysiology.
We encourage applications from students/postdocs with an experimental
background that want to widen their repertoire of analysis methods,
as well as from students with a theoretical background that have
an interest in analyzing physiological data.
Faculty:
Clemens Boucsein · Albert-Ludwigs-Universität and Bernstein Center Freiburg
Alex Loebel · Ludwig-Maximillians-Universität München and BCCN München
Jan Grewe · Ludwig-Maximillians-Universität München and BCCN München
Martin Nawrot · Freie Universität and BCCN Berlin
Keywords: Analysis of post-synaptic events - short-term plasticity -
spectral analysis - tuning and decoding
Deadline for application: December 20, 2012
For more information visit http://www.g-node.org/dataanalysis-course-2013
With best regards,
Clemens Boucsein (Organizer, Freiburg) and Thomas Wachtler (Local Organizer,
Munich)
Friday, November 30, 2012
Thursday, November 29, 2012
[Comp-neuro] UCSD Computational Neuroscience - Deadline Dec 3
UCSD GRADUATE PROGRAM IN COMPUTATIONAL NEUROSCIENCE
http://neurograd.ucsd.edu/2page.php?id=doccomp
Application deadline: December 3, 2012
http://neurograd.ucsd.edu/2page.php?id=gradadm
*****
The goal of the Computational Neuroscience Specialization in the
Neurosciences Graduate Program at UCSD is to train researchers
who are equally at home measuring large-scale brain activity,
analyzing the data with advanced computational techniques,
and developing new models for brain development and function.
Candidates from a wide range of backgrounds are invited to apply,
including Biology, Psychology, Computer Science, Physics and
Mathematics. The three major themes in the training program are:
1. Neurobiology of Neural Systems: Anatomy, physiology and behavior
of systems of neurons. Using modern neuroanatomical, behavioral,
neuropharmacological and electrophysiological techniques. Lectures, wet
laboratories and computer simulations, as well as research rotations. Major
new imaging and recording techniques also will be taught, including
two-photon laser scanning microscopy and functional magnetic resonance
imaging (fMRI).
2. Algorithms and Realizations for the Analysis of Neuronal Data:
New algorithms and techniques for analyzing data obtained from physiological
recording, with an emphasis on recordings from large populations of
neurons with imaging and multielectrode recording techniques. New
methods for the study of co-ordinated activity, such as multi-taper spectral
analysis and Independent Component Analysis (ICA).
3. Neuroinformatics, Dynamics and Control of Systems of Neurons:
Theoretical aspects of single cell function and emergent properties as
many neurons interact among themselves and react to sensory inputs. A
synthesis of approaches from mathematics and physical sciences as well as
biology will be used to explore the collective properties and nonlinear
dynamics of neuronal systems, as well as issues of sensory coding and
motor control.
*****
Participating Faculty include:
* Henry Abarbanel (Physics): Nonlinear and oscillatory dynamics;
modeling central pattern generators in the lobster stomatogastric ganglion.
* Thomas Albright (Salk Institute): Motion processing in primate visual
cortex; linking single neurons to perception; fMRI in awake, behaving
monkeys. Director, Sloan Center for Theoretical Neurobiology
* Darwin Berg (Neurobiology): Regulation synaptic components, assembly
and localization, function and long-term stability.
* Ed Callaway (Salk Institute): Neural circuits, visual perception, visual cortex
Genetic tools for tracing neural pathways.
* Gert Cauwenberghs (Bioengineering): Neuromorphic Engineering; analog VLSI
chips; wireless recording and nanoscale instrumentation for neural
systems; large-scale cortical modeling.
* Sreekanth Chalasani (Salk): C. elegans: genes, networks and behavior
Optical recording of olfactory processing.
* Andrea Chiba (Cognitive Science): Spatial attention, associative learning,
cholinergic neuromodulaiton of behavior, amygdala recordings
* EJ Chichilnisky (Salk Institute): Retinal multielectrode recording;
neural coding, visual perception.
* Todd Coleman (Bioengineering): Brain-Machine Interfaces (BMI)
* Garrison Cottrell (Computer Science and Engineering): Dynamical
neural network models and learning algorithms
* Virginia De Sa (Cognitive Science): Computational basis of perception
and learning; multi-sensory integration and contextual influences
* Mark Ellisman (Neurosciences, School of Medicine): High resolution
electron and light microscopy; anatomical reconstructions.
* Fred Gage (Salk Institute): Neurogenesis and models of the hippocampus;
neuronal diversity, neural stem cells.
* Timothy Gentner (Psychology): Birdsong learning. Neuroethology of vocal
communication and audition
* Robert Hecht-Nielsen (Electrical and Computer Engineering): Neural
computation and the functional organization of the cerebral cortex.
* Steve Hillyard (Neurosciences): EEG, perception, attention, memory,
Event related potentilas, SSVEP
* Harvey Karten (Neurosciences, School of Medicine): Anatomical,
physiological and computational studies of the retina and optic tectum
of birds and squirrels
* David Kleinfeld (Physics): Active sensation in rats; properties of
neuronal assemblies; optical imaging of large-scale activity.
* William Kristan (Neurobiology): Computational Neuroethology; functional
and developmental studies of the leech nervous system, including
studies of the bending reflex and locomotion.
* Scott Makeig (Institute for Neural Computation): Analysis of cognitive
event-related brain dynamics and fMRI using time-frequency and Independent
Component Analysis
* Javier Movellan (Institute for Neural Computation): Sensory fusion
and learning algorithms for continuous stochastic systems
* Howard Poizner (Institute for Neural Computation): Motor systems,
basal ganglia, reinforcment learning, Parkinson's disease.
* Mikhael Rabinovich (Institute for Nonlinear Science): Dynamical
systems analysis of the stomatogastric ganglion of the lobster and the
antenna lobe of insects
* Pamela Reinagel (Biology): Sensory and neural coding; natural scene
statistics; recordings from the visual system of cats and rodents.
* John Reynolds (Salk): Visual attention, cortex, psychophysics,
neurophysiology, neural modeling
* Massimo Scanziani (Biology): Neural circuits in the somotosensory
cortex; physiology of synaptic transmission; inhibitory mechanisms.
* Terrence Sejnowski (Salk Institute/Neurobiology): Computational
models and physiological studies of synaptic, neuronal and network function.
* Tanya Sharpee (Salk): Statistical physics and information theory
approaches to sensory processing in natural auditory and visual environments.
* Gabe Silva (Bioengineering): Cellular neural engineering
* Nicholas Spitzer (Neurobiology): Regulation of ionic channels and
neurotransmitters in developing neurons and neural function.
* Charles Stevens (Salk Institute): Synaptic physiology; theoretical
models of neuroanatomical scaling.
* Roger Tsien (Chemistry): Second messenger systems in neurons;
development of new optical and MRI probes of neuron function,
including calcium indicators and caged neurotransmitters
* Jing Wang (Biology): Representation of olfactory information in
the nervous system of Drosophila
* Ruth Williams (Mathematics): Probabilistic analysis of stochastic
systems and continuous learning algorithms
* Angela Yu (Cognitive Science): Sensory processing, attentional selection,
perceptual decision-making, sensorimotor integration, learning, and adaptation.
-----
On-line applications: http://neurograd.ucsd.edu/2page.php?id=gradadm
The deadline for completed application materials, including letters of
recommendation, is December 3, 2012.
_______________________________________________
Comp-neuro mailing list
Comp-neuro@neuroinf.org
http://www.neuroinf.org/mailman/listinfo/comp-neuro
http://neurograd.ucsd.edu/2page.php?id=doccomp
Application deadline: December 3, 2012
http://neurograd.ucsd.edu/2page.php?id=gradadm
*****
The goal of the Computational Neuroscience Specialization in the
Neurosciences Graduate Program at UCSD is to train researchers
who are equally at home measuring large-scale brain activity,
analyzing the data with advanced computational techniques,
and developing new models for brain development and function.
Candidates from a wide range of backgrounds are invited to apply,
including Biology, Psychology, Computer Science, Physics and
Mathematics. The three major themes in the training program are:
1. Neurobiology of Neural Systems: Anatomy, physiology and behavior
of systems of neurons. Using modern neuroanatomical, behavioral,
neuropharmacological and electrophysiological techniques. Lectures, wet
laboratories and computer simulations, as well as research rotations. Major
new imaging and recording techniques also will be taught, including
two-photon laser scanning microscopy and functional magnetic resonance
imaging (fMRI).
2. Algorithms and Realizations for the Analysis of Neuronal Data:
New algorithms and techniques for analyzing data obtained from physiological
recording, with an emphasis on recordings from large populations of
neurons with imaging and multielectrode recording techniques. New
methods for the study of co-ordinated activity, such as multi-taper spectral
analysis and Independent Component Analysis (ICA).
3. Neuroinformatics, Dynamics and Control of Systems of Neurons:
Theoretical aspects of single cell function and emergent properties as
many neurons interact among themselves and react to sensory inputs. A
synthesis of approaches from mathematics and physical sciences as well as
biology will be used to explore the collective properties and nonlinear
dynamics of neuronal systems, as well as issues of sensory coding and
motor control.
*****
Participating Faculty include:
* Henry Abarbanel (Physics): Nonlinear and oscillatory dynamics;
modeling central pattern generators in the lobster stomatogastric ganglion.
* Thomas Albright (Salk Institute): Motion processing in primate visual
cortex; linking single neurons to perception; fMRI in awake, behaving
monkeys. Director, Sloan Center for Theoretical Neurobiology
* Darwin Berg (Neurobiology): Regulation synaptic components, assembly
and localization, function and long-term stability.
* Ed Callaway (Salk Institute): Neural circuits, visual perception, visual cortex
Genetic tools for tracing neural pathways.
* Gert Cauwenberghs (Bioengineering): Neuromorphic Engineering; analog VLSI
chips; wireless recording and nanoscale instrumentation for neural
systems; large-scale cortical modeling.
* Sreekanth Chalasani (Salk): C. elegans: genes, networks and behavior
Optical recording of olfactory processing.
* Andrea Chiba (Cognitive Science): Spatial attention, associative learning,
cholinergic neuromodulaiton of behavior, amygdala recordings
* EJ Chichilnisky (Salk Institute): Retinal multielectrode recording;
neural coding, visual perception.
* Todd Coleman (Bioengineering): Brain-Machine Interfaces (BMI)
* Garrison Cottrell (Computer Science and Engineering): Dynamical
neural network models and learning algorithms
* Virginia De Sa (Cognitive Science): Computational basis of perception
and learning; multi-sensory integration and contextual influences
* Mark Ellisman (Neurosciences, School of Medicine): High resolution
electron and light microscopy; anatomical reconstructions.
* Fred Gage (Salk Institute): Neurogenesis and models of the hippocampus;
neuronal diversity, neural stem cells.
* Timothy Gentner (Psychology): Birdsong learning. Neuroethology of vocal
communication and audition
* Robert Hecht-Nielsen (Electrical and Computer Engineering): Neural
computation and the functional organization of the cerebral cortex.
* Steve Hillyard (Neurosciences): EEG, perception, attention, memory,
Event related potentilas, SSVEP
* Harvey Karten (Neurosciences, School of Medicine): Anatomical,
physiological and computational studies of the retina and optic tectum
of birds and squirrels
* David Kleinfeld (Physics): Active sensation in rats; properties of
neuronal assemblies; optical imaging of large-scale activity.
* William Kristan (Neurobiology): Computational Neuroethology; functional
and developmental studies of the leech nervous system, including
studies of the bending reflex and locomotion.
* Scott Makeig (Institute for Neural Computation): Analysis of cognitive
event-related brain dynamics and fMRI using time-frequency and Independent
Component Analysis
* Javier Movellan (Institute for Neural Computation): Sensory fusion
and learning algorithms for continuous stochastic systems
* Howard Poizner (Institute for Neural Computation): Motor systems,
basal ganglia, reinforcment learning, Parkinson's disease.
* Mikhael Rabinovich (Institute for Nonlinear Science): Dynamical
systems analysis of the stomatogastric ganglion of the lobster and the
antenna lobe of insects
* Pamela Reinagel (Biology): Sensory and neural coding; natural scene
statistics; recordings from the visual system of cats and rodents.
* John Reynolds (Salk): Visual attention, cortex, psychophysics,
neurophysiology, neural modeling
* Massimo Scanziani (Biology): Neural circuits in the somotosensory
cortex; physiology of synaptic transmission; inhibitory mechanisms.
* Terrence Sejnowski (Salk Institute/Neurobiology): Computational
models and physiological studies of synaptic, neuronal and network function.
* Tanya Sharpee (Salk): Statistical physics and information theory
approaches to sensory processing in natural auditory and visual environments.
* Gabe Silva (Bioengineering): Cellular neural engineering
* Nicholas Spitzer (Neurobiology): Regulation of ionic channels and
neurotransmitters in developing neurons and neural function.
* Charles Stevens (Salk Institute): Synaptic physiology; theoretical
models of neuroanatomical scaling.
* Roger Tsien (Chemistry): Second messenger systems in neurons;
development of new optical and MRI probes of neuron function,
including calcium indicators and caged neurotransmitters
* Jing Wang (Biology): Representation of olfactory information in
the nervous system of Drosophila
* Ruth Williams (Mathematics): Probabilistic analysis of stochastic
systems and continuous learning algorithms
* Angela Yu (Cognitive Science): Sensory processing, attentional selection,
perceptual decision-making, sensorimotor integration, learning, and adaptation.
-----
On-line applications: http://neurograd.ucsd.edu/2page.php?id=gradadm
The deadline for completed application materials, including letters of
recommendation, is December 3, 2012.
_______________________________________________
Comp-neuro mailing list
Comp-neuro@neuroinf.org
http://www.neuroinf.org/mailman/listinfo/comp-neuro
[Comp-neuro] Sleep, QEEG and Personalized Medicine workshop with Kerstin Hoedlmoser and Jay Gunkelman
Dear colleagues,
I would hereby like to let you know that we only have few places left for the workshop coming February. This advanced workshop will be on sleep, neurofeedback, QEEG and personalized medicine with Kerstin Hoedlmoser from the University of Salzburg (Austria), Jay Gunkelman from Brain Science International (BSI, USA) and myself. Also see: http://www.brainclinics.com/qeeg-personalized-medicine-workshop for more information.
During this year's workshop there will be a special focus on the role of sleep in psychiatric disorders (specifically ADHD and depression) where also techniques such as actigraphy and sleep questionnaires will be presented to better understand and assess sleep in patients. Furthermore, the relation of sleep to QEEG findings and implications for treatment such as neurofeedback, rTMS and other psychological and chronobiological interventions will also be discussed.
The workshop will be held from Thursday February 7th to 9th, 2013 in Nijmegen, the Netherlands.
Kind regards,
Martijn Arns, PhD
Director / Researcher
Research Institute Brainclinics
Brainclinics Treatment
Bijleveldsingel 34
6524 AD Nijmegen
Martijn Arns, PhD
Director / Researcher
Research Institute Brainclinics
Brainclinics Treatment
Bijleveldsingel 34
6524 AD Nijmegen
The Netherlands
Tel: +31(0)24-7503505
GSM: +31 (0)6-48177919
Fax: +31(0)24-8901447
E-mail: martijn@brainclinics.com
Tel: +31(0)24-7503505
GSM: +31 (0)6-48177919
Fax: +31(0)24-8901447
E-mail: martijn@brainclinics.com
Twitter: @Brainclinics
URL: www.brainclinics.com
URL: www.brainclinics.com
Wednesday, November 28, 2012
[Comp-neuro] Employment Opportunities @ NeuroVigil
1. Are you interested in the Brain?
2. Do you like to play with computer code and/or electrical circuits?
3. Are you considered one of the most creative people at your institution?
4. Would you like to help build Stephen Hawking's next brain computer interface?
5. Can you see yourself in Silicon Valley or blocks from the Pacific Ocean, in La Jolla, at the heart of the largest concentration of neuroscientists the world, in Southern California's Biotech Cluster?
6. Can you spell "N-A-S-A"?
7. Do you mind getting paid well?
8. Can you spell "S-T-O-C-K O-P-T-I-O-N-S"?
9. Can you handle not being micromanaged?
10. Do you have the Passion, Relentlessness and Endurance to work in a leading Neurotech startup at the forefront of Neuroengineering and Computational Neuroscience?
If you have answered "Yes!" to ALL of these questions, then you are ready to apply for a position at NeuroVigil, Inc. Send a current resume and references to jobs@neurovigil.com . Successful applicants will be contacted by email within 5-7 days. You do NOT need to be a US permanent resident to apply. NeuroVigil will sponsor outstanding candidates and provide relocation expenses to California from anywhere in the world. NeuroVigil, Inc. is dedicated to the betterment of the human condition. By merging neuroscience, non-invasive wireless brain recording technology and advanced computational algorithms, an accurate and automated reading of brain wave data is rapidly generated. This information is being used to assist with the diagnosis and treatment of a myriad of medical conditions. http://www.neurovigil.com
Warm Regards,
Philip
Philip Low, PhD
NeuroVigil, Inc.,
Chairman & C.E.O.
http://www.neurovigil.com
_______________________________________________
Comp-neuro mailing list
Comp-neuro@neuroinf.org
http://www.neuroinf.org/mailman/listinfo/comp-neuro
2. Do you like to play with computer code and/or electrical circuits?
3. Are you considered one of the most creative people at your institution?
4. Would you like to help build Stephen Hawking's next brain computer interface?
5. Can you see yourself in Silicon Valley or blocks from the Pacific Ocean, in La Jolla, at the heart of the largest concentration of neuroscientists the world, in Southern California's Biotech Cluster?
6. Can you spell "N-A-S-A"?
7. Do you mind getting paid well?
8. Can you spell "S-T-O-C-K O-P-T-I-O-N-S"?
9. Can you handle not being micromanaged?
10. Do you have the Passion, Relentlessness and Endurance to work in a leading Neurotech startup at the forefront of Neuroengineering and Computational Neuroscience?
If you have answered "Yes!" to ALL of these questions, then you are ready to apply for a position at NeuroVigil, Inc. Send a current resume and references to jobs@neurovigil.com . Successful applicants will be contacted by email within 5-7 days. You do NOT need to be a US permanent resident to apply. NeuroVigil will sponsor outstanding candidates and provide relocation expenses to California from anywhere in the world. NeuroVigil, Inc. is dedicated to the betterment of the human condition. By merging neuroscience, non-invasive wireless brain recording technology and advanced computational algorithms, an accurate and automated reading of brain wave data is rapidly generated. This information is being used to assist with the diagnosis and treatment of a myriad of medical conditions. http://www.neurovigil.com
Warm Regards,
Philip
Philip Low, PhD
NeuroVigil, Inc.,
Chairman & C.E.O.
http://www.neurovigil.com
_______________________________________________
Comp-neuro mailing list
Comp-neuro@neuroinf.org
http://www.neuroinf.org/mailman/listinfo/comp-neuro
Tuesday, November 27, 2012
[Comp-neuro] UOttawa Summer School in Computational Neuroscience
6th COMPUTATIONAL NEUROSCIENCE SUMMER SCHOOL
CENTER FOR NEURAL DYNAMICS
UNIVERSITY OF OTTAWA
JUNE 9-21, 2013
We are pleased to announce the 6th summer School in Computational Neuroscience, which will be held from Sunday June 9, 2013 until Friday June 21, 2013 inclusively. It is organized by the Center for Neural Dynamics at the University of Ottawa, and supported by the NSERC-CREATE program and the CIHR training program in Neurophysics. This highly pedagogical course is directed at graduate students and postdoctoral fellows from the physical sciences (e.g. physics, applied mathematics, engineering, computer science) and the life sciences (e.g. neuroscience, biology, physiology, human kinetics) who wish to develop their skills in neural data analysis and in mathematical modeling of neural activity. The topics will range from cellular to systems neuroscience, with a focus on sensory and motor systems. There is no tuition fee for participants from academic institutions, and further, there is support for travel and accommodation based on need.
The course will consist of 3 hours of lectures in the mornings, followed by 3-hour MATLAB-based computer laboratories in the afternoons. Participants will pair up for these laboratories, and an effort will be made to pair someone from the life sciences with someone from the physical sciences. All classes and laboratories will be held on the main downtown campus of the University of Ottawa. The School will be held in English. All participants must also do a research project and 15-minute presentation. The course can also be taken for official credits, since it is a University of Ottawa three-credit graduate course (NSC8104). The mark for the course will be based on work done in the computer laboratories, on the presentation of a research project by the end of the course, plus a write-up to follow within a week. The first day of the school (Sunday June 9th) will consist of a refresher on linear differential equations, linear algebra and probability theory open to all participants, as well as an introduction to MATLAB.
Enrollment in the course will be limited to 40 participants. Apart from enjoying the numerous cultural activities of the capital city, there will also be opportunities to hike, swim or bungee-jump in the Gatineau hills nearby, or to go white-water rafting down the world-class Ottawa river.
MATH PRE-REQUISITES: Calculus I and II, first-year university level Linear Algebra and Probability and Statistics.
LIFE SCIENCES PRE-REQUISITES: first-year university level life science courses for students in the physical sciences.
FACULTY
Prof. Maurice Chacron, Center for Nonlinear Dynamics, Dept. Physiology, McGill
Prof. John Lewis, Biology, University of Ottawa
Prof. Tim Lewis, Mathematics, University of California at Davis
Prof. André Longtin, Physics, Cellular and Molecular Medicine, University of Ottawa
Prof. Len Maler, Cellular and Molecular Medicine, University of Ottawa
Prof. Cathy E. Morris, Biochemistry, Microbiology, Immunology, U. Ottawa
Prof. Georg Northoff, Institute of Mental Health Research, University of Ottawa
Prof. Jean-Philippe Thivierge, School of Psychology, University of Ottawa
TUITION
None for participants from academia. $2500 for participants from industry.
See the application form at: http://www.neurodynamic.uottawa.ca/summer.html
ACCOMMODATION
Accommodation will be available at the Marchand Residence of the University of Ottawa, a few minutes walk away from the lecture hall and computer lab, cafeterias and downtown Ottawa with its restaurants, museums etc... Accommodation consists of a single room (with desk and internet access), with communal kitchen and living area and shared bathroom. The moderate cost will be announced shortly.
FINANCIAL SUPPORT
For those demonstrating the need, limited partial financial support for travel and accommodation will be available ONLY for students registered at canadian universities. Lunch and dinner will be covered for all participants. Support is provided by NSERC (CREATE training program in Quantitative Biomedicine, University of Ottawa) and CIHR (Neurophysics Training Grant, Universite Laval, McGill University and University of Ottawa).
IMPORTANT DATES
March 1st, 2013: Application, including a letter of recommendation sent to compneuro13@uottawa.ca
March 10, 2013: Notification of acceptance and level of financial support.
March 20, 2013: Notification of acceptance by the participant.
Accommodation: as soon as possible after notification of acceptance, participants can reserve their accommodation online at reserve@uottawa.ca or by phoning 1-888-564-4545.
REGISTER AT: www.neurodynamic.uottawa.ca/summer.html
CONTACT US: compneuro13@uottawa.ca
SYLLABUS
1) Introduction to Linear and Nonlinear Dynamical Systems (Longtin)
-solutions of linear differential equations
-qualitative analysis of nonlinear differential equations
2) Single Neuron Models (Longtin and J. Lewis)
-ionic models
-simplified deterministic models
-stochastic models
3) Neural Spike Train Analysis and Modeling (Chacron)
-basic statistics
-autocorrelation, spectrum
-information theory toolbox
4) Sensory Coding (Maler)
-artificial and naturalistic stimuli
-modeling activity along the afferent pathways
-modeling feedback
-population coding and information theory
5) Synaptic Plasticity (J. Lewis)
-short term depression and facilitation
-long term plasticity
-implications for information processing
6) Coupled Neurons (T. Lewis)
-gap junction
-excitatory and inhibitory synaptic coupling
-effect of coupling on neural population behavior
7) Functional and Structural Networks (Thivierge)
-graph statistics
-multi-scale networks
-analyzing dynamics from multi-electrode arrays
8) Computational Neurotrauma (Morris)
9) Dynamics of Resting State Activity and Psychiatric Illness (Northoff)
[Comp-neuro] Wellcome Trust 4-year PhD programme in Systems Neuroscience (Newcastle University)
Dear all,
our Wellcome Trust 4-year PhD programme in systems neuroscience, aimed at applicants from the physical sciences (physics, engineering, mathematics, or computer science), is now accepting applications for studentships starting in September 2013 (see below). Research areas include Neuroinformatics, Computational Neuroscience, Neuroimaging (fMRI, DTI, EEG, ECoG), Brain Connectivity, Clinical Neuroscience, Behaviour and Evolution, and Brain Dynamics (simulations and time series analysis). Strong interactions between clinical, experimental, and computational researchers are a key component of this programme.
Best,
Marcus
Wellcome Trust 4-year PhD programme 'Systems Neuroscience: From Networks to Behaviour'
Programme Directors: Prof. Stuart Baker, Prof. Tim Griffiths, and Dr Marcus Kaiser
The Institute of Neuroscience at Newcastle University integrates more than 100 principal investigators across medicine, psychology, computer science, and engineering. Research in systems, cellular, computational, and behavioural neuroscience. Laboratory facilities include auditory and visual psychophysics; rodent, monkey, and human neuroimaging (EEG, fMRI, PET); TMS; optical recording, multi-electrode neurophysiology, confocal and fluorescence imaging, high-throughput computing and e-science, artificial sensory-motor devices, clinical testing, and the only brain bank for molecular changes in human brain development.
The Wellcome Trust's Four-year PhD Programmes are a flagship scheme aimed at supporting the most promising students to undertake in-depth postgraduate research training. The first year combines taught courses with three laboratory rotations to broaden students' knowledge of the subject area. At the end of the first year, students will make an informed choice of their three-year PhD research project.
This programme is based at Newcastle University and is aimed to provide specialised training for physical and computational scientists (e.g. physics, chemistry, engineering, mathematics, and computer science) wishing to apply their skills to a research neuroscience career.
Eligibility/Person Specification: Applicants should have, or expect to obtain, a 1st or 2:1 degree, or equivalent, in a physical sciences, engineering, mathematics or computing degree.
Value of the award: Support includes a stipend for 4 years (£19k/yr tax-free), PhD registration fees at UK/EU student rate, research expenses, general training funds and some travel costs.
How to apply: You must apply through the University's online postgraduate application form (http://www.ncl.ac.uk/postgraduate/funding/search/list/in054 ) inserting the reference number IN054 and selecting 'Master of Research/Doctor of Philosophy (Medical Sciences) - Neuroscience' as the programme of study. Only mandatory fields need to be completed (no personal statement required) and a covering letter, CV and (if English is not your first language) a copy of your English language qualifications must be attached. The covering letter must state the title of the studentship, quote the reference number IN054 and state how your interests and experience relate to the programme.
The deadline for receiving applications is 27 January 2013.
You should also send your covering letter and CV to Suzi Englebright, Postgraduate Secretary, Institute of Neuroscience, Henry Wellcome Building, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, or by email to ion-postgrad-enq@ncl.ac.uk .
For more information, see http://www.ncl.ac.uk/ion/postgrad/research/wellcome/
--
Marcus Kaiser, Ph.D.
Reader (Associate Professor) in Neuroinformatics
School of Computing Science
Newcastle University
Claremont Tower
Newcastle upon Tyne NE1 7RU, UK
Visiting Professor in Neuroinformatics
Department of Brain and Cognitive Sciences
Seoul National University, Korea
http://www.biological-networks.org/
[Comp-neuro] PhD positions in Computational Neuroscience
The Erasmus Mundus Joint Doctoral Program "EuroSPIN" (European Study Programme
in Neuroinformatics) is inviting applications from students having a solid
background in mathematics, physics, computer sciences, biochemistry or
neuroscience (on a master level or equivalent), in all cases with computer
science skills. Documented interest in research like activities (e.g.
demonstrated in the form of master thesis work, or participation in research
related activities) is of large importance. Also fluency in English is
requested.
Four partners participate:
- Bernstein Center Freiburg, Germany
- KTH Royal Institute of Technology, Sweden
- National Centre for Biological Science, India
- University of Edinburgh (UoE), UK
They are all research leaders in the Neuroinformatics field, but they have
complementary strengths.
Each student will spend most of the time at two of the partner universities,
and also receive a joint (or double) PhD degree following a successful
completion of the studies. The mobility periods, as well as the courses a
student will follow, are tailored individually based on: a) the PhD students
background; b) which constellations of partners that are involved, as well as
c) the specific research project. During the PhD period each student has one
main supervisor from each of the two universities that grant the PhD degree.
There are excellent scholarship opportunities for students accepted to an
Erasmus Mundus Joint Doctorate programme. An employment contract will be given
to all selected PhD students during the study time, which is 4 years.
If you are interested, go to our webpage: http://www.kth.se/eurospin
If you have questions, send us email to <mundus-eurospin@kth.se>.
Deadline for Application (both EU and non-EU students): 30 Nov 2012.
EuroSPIN Coordinators,
Stockholm, SWEDEN.
_______________________________________________
Comp-neuro mailing list
Comp-neuro@neuroinf.org
http://www.neuroinf.org/mailman/listinfo/comp-neuro
in Neuroinformatics) is inviting applications from students having a solid
background in mathematics, physics, computer sciences, biochemistry or
neuroscience (on a master level or equivalent), in all cases with computer
science skills. Documented interest in research like activities (e.g.
demonstrated in the form of master thesis work, or participation in research
related activities) is of large importance. Also fluency in English is
requested.
Four partners participate:
- Bernstein Center Freiburg, Germany
- KTH Royal Institute of Technology, Sweden
- National Centre for Biological Science, India
- University of Edinburgh (UoE), UK
They are all research leaders in the Neuroinformatics field, but they have
complementary strengths.
Each student will spend most of the time at two of the partner universities,
and also receive a joint (or double) PhD degree following a successful
completion of the studies. The mobility periods, as well as the courses a
student will follow, are tailored individually based on: a) the PhD students
background; b) which constellations of partners that are involved, as well as
c) the specific research project. During the PhD period each student has one
main supervisor from each of the two universities that grant the PhD degree.
There are excellent scholarship opportunities for students accepted to an
Erasmus Mundus Joint Doctorate programme. An employment contract will be given
to all selected PhD students during the study time, which is 4 years.
If you are interested, go to our webpage: http://www.kth.se/eurospin
If you have questions, send us email to <mundus-eurospin@kth.se>.
Deadline for Application (both EU and non-EU students): 30 Nov 2012.
EuroSPIN Coordinators,
Stockholm, SWEDEN.
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Monday, November 26, 2012
[Comp-neuro] PhD positions at Ohio State University, Psychology Department
The Laboratory for Cognitive Modeling and Computational Cognitive
Neuroscience (http://cogmod.osu.edu ) in the Department of Psychology
at the Ohio State University is looking to accept up to 3 new graduate
students for the incoming class of 2013. We are particularly
interested in applicants with computational training (e.g., computer
science, mathematics, engineering) coupled with a clear interest in
cognitive science, particularly vision (both high- and low-level) and
analogy-making. The new students will be able to join the ongoing
research projects in the lab or formulate related projects of their
own. In particular, there is an opportunity to develop biologically
plausible neural-network models of visual perceptual learning and of
visual analogy-making. For details on how to apply, see
http://cogmod.osu.edu/prospective-students/
The Ohio State University is home to a vibrant community of cognitive
neuroscientists, cognitive psychologists, and vision scientists in the
Psychology Departments and other departments on campus. There is a
strong focus on computational and mathematical modeling within
psychology as well as interdisciplinary interactions with colleagues
in electrical engineering, computer science, and neuroscience programs
at OSU. For more information on our cognitive neuroscience group or
the Cognitive area, please visit:
http://www.psy.ohio-state.edu/cognitive-neuroscience/
http://www.psy.ohio-state.edu/programs/cognitive/
Visual cognitive neuroscience is another focus of the graduate program
in cognitive psychology at OSU. Labs in this cluster include:
Dr. Dirk Bernhardt-Walther (http://bwlab.psy.ohio-state.edu/ ),
Dr. Julie Golomb (Vision & Cognitive Neuroscience Lab;
http://faculty.psy.ohio-state.edu/golomb/lab/index.html ),
Dr. Andy Leber (Cognitive Control Lab;
http://faculty.psy.ohio-state.edu/leber/lab/ ),
Dr. Zhong-Lin Lu (Laboratory of Brain Processes, http://lobes.osu.edu ),
Dr. Alex Petrov (Laboratory for Cognitive Modeling and Computational
Cognitive Neuroscience; http://cogmod.osu.edu/ ),
Dr. Per Sederberg (Computational Memory Lab; http://memory.osu.edu/ ), and
Dr. Jim Todd (Vision Lab; http://faculty.psy.ohio-state.edu/todd/ ).
Potential graduate students interested in any of the above labs should
apply through the Department of Psychology (Cognitive Program):
http://www.psy.ohio-state.edu/graduate/application.php
Application deadlines:
November 30th, 2012 for international students, and
December 1st, 2012 for domestic students.
We look forward to hearing from you!
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Neuroscience (http://cogmod.osu.edu ) in the Department of Psychology
at the Ohio State University is looking to accept up to 3 new graduate
students for the incoming class of 2013. We are particularly
interested in applicants with computational training (e.g., computer
science, mathematics, engineering) coupled with a clear interest in
cognitive science, particularly vision (both high- and low-level) and
analogy-making. The new students will be able to join the ongoing
research projects in the lab or formulate related projects of their
own. In particular, there is an opportunity to develop biologically
plausible neural-network models of visual perceptual learning and of
visual analogy-making. For details on how to apply, see
http://cogmod.osu.edu/prospective-students/
The Ohio State University is home to a vibrant community of cognitive
neuroscientists, cognitive psychologists, and vision scientists in the
Psychology Departments and other departments on campus. There is a
strong focus on computational and mathematical modeling within
psychology as well as interdisciplinary interactions with colleagues
in electrical engineering, computer science, and neuroscience programs
at OSU. For more information on our cognitive neuroscience group or
the Cognitive area, please visit:
http://www.psy.ohio-state.edu/cognitive-neuroscience/
http://www.psy.ohio-state.edu/programs/cognitive/
Visual cognitive neuroscience is another focus of the graduate program
in cognitive psychology at OSU. Labs in this cluster include:
Dr. Dirk Bernhardt-Walther (http://bwlab.psy.ohio-state.edu/ ),
Dr. Julie Golomb (Vision & Cognitive Neuroscience Lab;
http://faculty.psy.ohio-state.edu/golomb/lab/index.html ),
Dr. Andy Leber (Cognitive Control Lab;
http://faculty.psy.ohio-state.edu/leber/lab/ ),
Dr. Zhong-Lin Lu (Laboratory of Brain Processes, http://lobes.osu.edu ),
Dr. Alex Petrov (Laboratory for Cognitive Modeling and Computational
Cognitive Neuroscience; http://cogmod.osu.edu/ ),
Dr. Per Sederberg (Computational Memory Lab; http://memory.osu.edu/ ), and
Dr. Jim Todd (Vision Lab; http://faculty.psy.ohio-state.edu/todd/ ).
Potential graduate students interested in any of the above labs should
apply through the Department of Psychology (Cognitive Program):
http://www.psy.ohio-state.edu/graduate/application.php
Application deadlines:
November 30th, 2012 for international students, and
December 1st, 2012 for domestic students.
We look forward to hearing from you!
_______________________________________________
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Comp-neuro@neuroinf.org
http://www.neuroinf.org/mailman/listinfo/comp-neuro
[Comp-neuro] postdoc for multiunit recording at UCLA
A postdoctoral position to be based at the David Geffen School of
Medicine UCLA is available to study the neuronal basis of subjective
confidence & attention in visual perception. This project is a
collaboration between Drs. Hakwan Lau and Michele Basso. In the newly
constructed Fuster laboratory for Cognitive Neuroscience there are
state of the art facilities for electrophysiological and surgical
procedures. The ideal candidate has some experience in conducting
single or multi- unit neuronal recording in awake subjects, and is
familiar with the primate visual system, especially the circuitries
involved in perceptual decision making. UCLA has a thriving
neuroscience community providing exciting opportunities for
intellectual development for the candidate. To apply please send a
brief statement describing research interests, a curriculum vitae,
bibliography and at least 2 letters of reference to: Dr. Hakwan Lau
(hakwan@gmail.com) and Dr Michele Basso (mbasso@mednet.ucla.edu),
before Dec 7, 2012 (or as soon as possible).
Hakwan Lau, D.Phil
http://www.fil.ion.ucl.ac.uk/~hclau/
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Medicine UCLA is available to study the neuronal basis of subjective
confidence & attention in visual perception. This project is a
collaboration between Drs. Hakwan Lau and Michele Basso. In the newly
constructed Fuster laboratory for Cognitive Neuroscience there are
state of the art facilities for electrophysiological and surgical
procedures. The ideal candidate has some experience in conducting
single or multi- unit neuronal recording in awake subjects, and is
familiar with the primate visual system, especially the circuitries
involved in perceptual decision making. UCLA has a thriving
neuroscience community providing exciting opportunities for
intellectual development for the candidate. To apply please send a
brief statement describing research interests, a curriculum vitae,
bibliography and at least 2 letters of reference to: Dr. Hakwan Lau
(hakwan@gmail.com) and Dr Michele Basso (mbasso@mednet.ucla.edu),
before Dec 7, 2012 (or as soon as possible).
Hakwan Lau, D.Phil
http://www.fil.ion.ucl.ac.uk/~hclau/
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Comp-neuro@neuroinf.org
http://www.neuroinf.org/mailman/listinfo/comp-neuro
[Comp-neuro] Theoretical and Computational Neuroscience Graduate Studies in Houston
The Theoretical and Computational Neuroscience track of the Neuroscience program at UT Medical School in Houston
The Neuroscience program at the University of Texas Medical School in Houston now offers a Theoretical and Computational Neuroscience Track<http://nba.uth.tmc.edu/gradprog/program/trackTheoreticalComputation.htm>. The goal of the specialization is to train the next generation of neuroscientists with the broad range of computational and analytical skills that are essential to understand the organization and function of complex neural systems. The specialization is intended for students with backgrounds in neuroscience, physics, chemistry, biology, psychology, computer science, engineering, and mathematics.
The specialization allows Neuroscience students to concentrate on a focused program of rigorous course work in both the theoretical and experimental aspects ofcomputational neuroscience. Students are encouraged to pursue thesis research that includes both an experimental and a computational component. Students may have two mentors, a primary and a secondary mentor, one being a theorist and the other an experimentalist. The Neuroscience program has an excellent group of associated faculty members and many of them have a strong interest and an active ongoing program in computational neuroscience.
The theoretical group at UTHSC-H is part of a larger group that includes several universities and medical schools in the Houston area, the Gulf Coast Consortium in theoretical and computational neuroscience (GCC-TCN)<http://gulfcoastconsortia.org/Research/Gulf_Coast_Consortium_for_Theoretical_and_Computational_Neuroscience.aspx>. Many of the courses offered are combined courses across these different institutions, and this provides a larger community of faculty and students that are interested in similar topics. Through the GCC-TCN it is possible to obtain additional training grants, as well as have joint mentors from other universities and disciplines.
Applications should go directly to the graduateschool of biological sciences (GSBS)<http://www.uthouston.edu/gsbs/> if you have any questions about the program please write me an email (harel.shouval@uth.tmc.edu<mailto:harel.shouval@uth.tmc.edu>).
Harel Shouval
Associate Professor, Department of Neurobiology and Anatomy
The University of Texas Medical School at Houston
http://nba.uth.tmc.edu/resources/faculty/members/shouval.htm
http://nba.uth.tmc.edu/homepage/shouval/
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The Neuroscience program at the University of Texas Medical School in Houston now offers a Theoretical and Computational Neuroscience Track<http://nba.uth.tmc.edu/gradprog/program/trackTheoreticalComputation.htm>. The goal of the specialization is to train the next generation of neuroscientists with the broad range of computational and analytical skills that are essential to understand the organization and function of complex neural systems. The specialization is intended for students with backgrounds in neuroscience, physics, chemistry, biology, psychology, computer science, engineering, and mathematics.
The specialization allows Neuroscience students to concentrate on a focused program of rigorous course work in both the theoretical and experimental aspects ofcomputational neuroscience. Students are encouraged to pursue thesis research that includes both an experimental and a computational component. Students may have two mentors, a primary and a secondary mentor, one being a theorist and the other an experimentalist. The Neuroscience program has an excellent group of associated faculty members and many of them have a strong interest and an active ongoing program in computational neuroscience.
The theoretical group at UTHSC-H is part of a larger group that includes several universities and medical schools in the Houston area, the Gulf Coast Consortium in theoretical and computational neuroscience (GCC-TCN)<http://gulfcoastconsortia.org/Research/Gulf_Coast_Consortium_for_Theoretical_and_Computational_Neuroscience.aspx>. Many of the courses offered are combined courses across these different institutions, and this provides a larger community of faculty and students that are interested in similar topics. Through the GCC-TCN it is possible to obtain additional training grants, as well as have joint mentors from other universities and disciplines.
Applications should go directly to the graduateschool of biological sciences (GSBS)<http://www.uthouston.edu/gsbs/> if you have any questions about the program please write me an email (harel.shouval@uth.tmc.edu<mailto:harel.shouval@uth.tmc.edu>).
Harel Shouval
Associate Professor, Department of Neurobiology and Anatomy
The University of Texas Medical School at Houston
http://nba.uth.tmc.edu/resources/faculty/members/shouval.htm
http://nba.uth.tmc.edu/homepage/shouval/
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[Comp-neuro] France-China workshop | 3-5 december 2012, Paris, Université Paris Descartes
France-China 2nd Workshop on
Network Dynamics and Synaptic Plasticity in Central Nervous System
3-5, December 2012
University Paris Descartes
45 Rue des Saints-Pères
75006, Paris, France
Participants : China : Guoqiang Bi (Hefei), David Cai (Shanghai),
Yuanyuan Mi (Suzhou), Sen Song (Beijing), Huizhong Tao (Los Angeles),
Yun Wang (Boston), Si Wu (Beijing), Wei Wu (Hefei),
Dongzhuo Zhou (Shanghai) ; France : Frédéric Alexandre (Bordeaux),
Thomas Boraud (Bordeaux), Sophie Deneve (Paris), Boris Gutkin (Paris),
David Hansel (Paris), Arthur Leblois (Paris),
Carole Levenes (Paris), German Mato (Bariloche/Paris), Gianluigi
Mongillo (Paris), Morgane Pidoux (Paris), Dan Shulz (Gif sur Yvette),
Carl van Vreeswijk (Paris)
Organizing committee : David Hansel, Carole Levenes & Guoqiang Bi
Sponsored by: Ecole des Neurosciences de Paris,
UMR8119-CNRS-Université Paris Descartes, ANR-BALWM, Institut de
Neurosciences et de la Cognition (Université Paris-Descartes)
All talks will take place at the "Salle des thèses" in the Saints
Pères site of the University Paris Descartes, Batiment Jacob, 45 rue
des Saints-Pères .
Contacts: david.hansel@parisdescartes.fr; carole.levenes@parisdescartes.fr
To download the program and the abstracts:
http://neurophys.biomedicale.univ-paris5.fr/-2012-France-China-2nd-Workshop-.html?lang=en
--
---------------------------------------
David Hansel
Directeur de Recherche au CNRS
Laboratory of Neurophysics and Physiology - UMR 8119 CNRS
45 rue des Saints Peres 75270 Paris Cedex 06
Tel (33).1.42.86.22.71 - Fax (33).1.49.27.90.62
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Network Dynamics and Synaptic Plasticity in Central Nervous System
3-5, December 2012
University Paris Descartes
45 Rue des Saints-Pères
75006, Paris, France
Participants : China : Guoqiang Bi (Hefei), David Cai (Shanghai),
Yuanyuan Mi (Suzhou), Sen Song (Beijing), Huizhong Tao (Los Angeles),
Yun Wang (Boston), Si Wu (Beijing), Wei Wu (Hefei),
Dongzhuo Zhou (Shanghai) ; France : Frédéric Alexandre (Bordeaux),
Thomas Boraud (Bordeaux), Sophie Deneve (Paris), Boris Gutkin (Paris),
David Hansel (Paris), Arthur Leblois (Paris),
Carole Levenes (Paris), German Mato (Bariloche/Paris), Gianluigi
Mongillo (Paris), Morgane Pidoux (Paris), Dan Shulz (Gif sur Yvette),
Carl van Vreeswijk (Paris)
Organizing committee : David Hansel, Carole Levenes & Guoqiang Bi
Sponsored by: Ecole des Neurosciences de Paris,
UMR8119-CNRS-Université Paris Descartes, ANR-BALWM, Institut de
Neurosciences et de la Cognition (Université Paris-Descartes)
All talks will take place at the "Salle des thèses" in the Saints
Pères site of the University Paris Descartes, Batiment Jacob, 45 rue
des Saints-Pères .
Contacts: david.hansel@parisdescartes.fr; carole.levenes@parisdescartes.fr
To download the program and the abstracts:
http://neurophys.biomedicale.univ-paris5.fr/-2012-France-China-2nd-Workshop-.html?lang=en
--
---------------------------------------
David Hansel
Directeur de Recherche au CNRS
Laboratory of Neurophysics and Physiology - UMR 8119 CNRS
45 rue des Saints Peres 75270 Paris Cedex 06
Tel (33).1.42.86.22.71 - Fax (33).1.49.27.90.62
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[Comp-neuro] Biological Cybernetics: vol 106, number 10 --- Table of Content
Biological Cybernetics: vol 106, number 10 --- Table of Content
Original papers:
"Determining all parameters necessary to build Hill-type muscle models
from experiments on single muscles"
Marcus Blümel, Scott L. Hooper, Christoph Guschlbauerc, William E. White
& Ansgar Büschges
http://link.springer.com/article/10.1007/s00422-012-0531-5
"Hill-type muscle model parameters determined from experiments on single
muscles show large animal-to-animal variation
Marcus Blümel, Christoph Guschlbauer, Silvia Daun-Gruhn, Scott L. Hooper
& Ansgar Büschges"
http://link.springer.com/article/10.1007/s00422-012-0530-6
"Using individual-muscle specific instead of across-muscle mean data
halves muscle simulation error"
Marcus Blümel, Christoph Guschlbauer, Scott L. Hooper & Ansgar Büschges
http://link.springer.com/article/10.1007/s00422-011-0460-8
"Bifurcation control of the Morris–Lecar neuron model via a dynamic
state-feedback control"
Le Hoa Nguyen, Keum-Shik Hong & Seonghun Park
http://link.springer.com/article/10.1007/s00422-012-0508-4
"The role of feedback in morphological computation with compliant bodies"
Helmut Hauser, Auke J. Ijspeert, Rudolf M. Füchslin, Rolf Pfeifer &
Wolfgang Maass
http://link.springer.com/article/10.1007/s00422-012-0516-4
----
Biological Cybernetics, all issues:
http://www.springerlink.com/content/100465/
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Original papers:
"Determining all parameters necessary to build Hill-type muscle models
from experiments on single muscles"
Marcus Blümel, Scott L. Hooper, Christoph Guschlbauerc, William E. White
& Ansgar Büschges
http://link.springer.com/article/10.1007/s00422-012-0531-5
"Hill-type muscle model parameters determined from experiments on single
muscles show large animal-to-animal variation
Marcus Blümel, Christoph Guschlbauer, Silvia Daun-Gruhn, Scott L. Hooper
& Ansgar Büschges"
http://link.springer.com/article/10.1007/s00422-012-0530-6
"Using individual-muscle specific instead of across-muscle mean data
halves muscle simulation error"
Marcus Blümel, Christoph Guschlbauer, Scott L. Hooper & Ansgar Büschges
http://link.springer.com/article/10.1007/s00422-011-0460-8
"Bifurcation control of the Morris–Lecar neuron model via a dynamic
state-feedback control"
Le Hoa Nguyen, Keum-Shik Hong & Seonghun Park
http://link.springer.com/article/10.1007/s00422-012-0508-4
"The role of feedback in morphological computation with compliant bodies"
Helmut Hauser, Auke J. Ijspeert, Rudolf M. Füchslin, Rolf Pfeifer &
Wolfgang Maass
http://link.springer.com/article/10.1007/s00422-012-0516-4
----
Biological Cybernetics, all issues:
http://www.springerlink.com/content/100465/
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[Comp-neuro] PhD position at UCL Ear Institute
Dear Colleagues,
I am writing to point your attention to PhD studentship (available for an immediate start) opening at the UCL Ear Institute and would be grateful if you could distribute the advert to relevant members of your institution.
A 3 year PhD studentship in auditory cognitive neuroscience is available as part of a research collaboration between the UCL Ear Institute (London, UK) and NTT Communication Science Labs (Nippon Telegraph and Telephone corporation, Atsugi, Japan). The student will be based at the UCL Ear Institute and supervised by Dr. Maria Chait. They will also be working with Prof. Makio Kashino and Dr. Shigeto Furukawa (NTT) and the research program will involve experiments conducted both at UCL and in Japan, requiring occasional travel. The project will use psychophysics, eye tracking, autonomic response measures and MEG functional brain imaging to investigate which features of sound are perceptually salient. Namely, those sounds that automatically capture attention in a busy scene, even when listeners’ initial perceptual focus is elsewhere.
The UCL Ear Institute provides state-of-the-art research facilities across a wide range of disciplines and is one of the foremost centres for hearing, speech and language-related research within Europe.
Key Requirements
The position is only available for UK/EU passport holders.
Applicants should hold a 1St class, or upper 2nd bachelor’s degree (masters degree an advantage) in an engineering or scientific subject. Previous experience with auditory research, functional brain imaging, neuroscience and/or acoustics is desirable.
For an informal discussion, or to submit an application please contact Dr. Maria Chait (m.chait@ucl.ac.uk). Applicants should submit a supporting statement, a CV, and the details of two recommenders. Application review begins December 2012 and will continue until the position is filled. The studentship includes fees and a yearly stipend (about £16000; tax free).
Maria Chait PhD
Senior Lecturer
UCL Ear Institute
332 Gray's Inn Road
London WC1X 8EE
[visionlist] Modelling software + major V1 modelling review paper
I am pleased to announce the release of a set of software packages for
modelling neural regions and systems, along with a new review paper
that ties together work from dozens of projects with collaborators
using this software to model the primary visual cortex.
The review paper shows how these separate projects add up into what is
hoped to be the most complete model of the development of functional
properties of V1 neurons to date:
James A. Bednar.
Building a mechanistic model of the development and function of the primary visual cortex.
Journal of Physiology-Paris, 106:194-211, 2012.
http://dx.doi.org/10.1016/j.jphysparis.2011.12.001
http://homepages.inf.ed.ac.uk/jbednar/papers/bednar.jpp12.pdf
The freely available, cross-platform (Linux, Mac, Windows) Python
software packages include:
Param/ParamTk: Handling parameters used in scientific programs
ImaGen: Generating input patterns and connection patterns
Topographica: Modelling interconnected neural regions
More details about each package and the review paper are included below.
Links to 31 publications using this software so far are available here:
http://topographica.org/Home/pubs.html
Thanks to the 30+ collaborators who have contributed to the research
and software development reported here, each cited in the review
paper or listed on topographica.org. We would very much appreciate
any feedback, suggestions, or ideas for future collaboration.
Jim
James A. Bednar, Ph.D.
Senior Lecturer, University of Edinburgh
Director, Computational Systems Neuroscience Group
http://homepages.inf.ed.ac.uk/jbednar/research.html
Director, Edinburgh Doctoral Training Centre in
Neuroinformatics and Computational Neuroscience
http://anc.ed.ac.uk/dtc
_______________________________________________________________________________
OPEN-SOURCE SOFTWARE PACKAGES
Param 1.0 released 7/2012 (http://ioam.github.com/param/):
The Param library makes it simple to add support for Parameters;
Param has no dependencies and is very lightweight, so that it can
be used with any Python program. A Parameter is a special type of
Python attribute extended to have features such as type and range
checking, dynamically generated values, documentation strings,
default values, etc., each of which is inherited from parent
classes if not specified in a subclass. Parameters are extremely
useful for writing scientific software, making it clear which
values are intended to be changed in practice and avoiding
potentially dangerous user errors.
ParamTk 0.8 released 7/2012 (http://ioam.github.com/paramtk/):
Optional extension to Param that provides a GUI for editing
parameter values for your objects without requiring any
GUI-specific coding.
ImaGen 1.0 released 7/2012 (http://ioam.github.com/imagen/):
Provies comprehensive support for creating resolution-independent
spatial pattern distributions. ImaGen consists of a large library
of primarily two-dimensional patterns, including mathematical
functions, geometric primitives, images read from files, and many
ways to combine or select from any other patterns. These patterns
can be used in any Python program that needs configurable patterns
or a series of patterns, with only a small amount of user-level
code to specify or use each pattern.
Topographica 0.9.8 released 11/2012 (http://topographica.org/):
Topographica allows researchers to set up models of complete neural
regions and systems relatively easily, because it takes care of a
lot of the otherwise-painful details of spatial coordinate systems,
mapping between brain regions and between layers in the same
region, scaling between different sampling densities, having
spatially restricted patterns of connectivity, specifying input and
weight patterns (via ImaGen), and measuring tuning curves,
receptive fields, and maps. Topographica is a general-purpose
object-oriented event-driven simulator that provides extensive
flexibility, with families of parameterized objects that can be
customized and adapted for new modelling projects. Current models
primarily focus on the visual system, but they are implemented
using generic primitives that have also been used for somatosensory
and auditory cortex modelling, as well as subcortical and
motor-output models.
_______________________________________________________________________________
The review paper (citation below) describes the GCAL model, which
shows how a relatively small number of simple biological mechanisms,
based on Hebbian learning and homeostatic plasticity, can lead an
unorganized neural region to develop:
- Receptive fields selective for orientation, ocular dominance,
motion direction, spatial frequency, disparity, and color
- Preferences for each of these organized into realistic topographic
maps
- Lateral connections between neurons that reflect the structure of
the maps, as found experimentally
- Both simple and complex cells
The resulting neurons exhibit:
- Realistic surround modulation effects, including their
diversity, caused by interactions between these neurons
- Contrast-gain control and contrast-invariant tuning, ensuring that
they retain selectivity robustly
- Long-term and short-term plasticity (e.g. aftereffects),
emerging from mechanisms originally implemented for development
These properties each arise from an initially undifferentiated
cortical region model, suggesting that the mechanisms involved will
also explain a large variety of cortical phenomena across different
areas and modalities.
@Article{bednar:jpp12,
title = "Building a Mechanistic Model of the Development and
Function of the Primary Visual Cortex",
author = "James A. Bednar",
journal = "Journal of Physiology - Paris",
year = 2012,
volume = 106,
pages = "194--211",
url = "http://dx.doi.org/10.1016/j.jphysparis.2011.12.001",
urlalt = "http://homepages.inf.ed.ac.uk/jbednar/papers/bednar.jpp12.pdf",
abstract = "Researchers have used a very wide range of different
experimental and theoretical approaches to help
understand mammalian visual systems. These
approaches tend to have quite different assumptions,
strengths, and weaknesses. Computational models of
the visual cortex, in particular, have typically
implemented either a proposed circuit for part of
the visual cortex of the adult, assuming a very
specific wiring pattern based on findings from
adults, or else attempted to explain the long-term
development of a visual cortex region from an
initially undifferentiated starting point. Previous
models of adult V1 have been able to account for
many of the measured properties of V1 neurons, while
not explaining how these properties arise or why
neurons have those properties in particular.
Previous developmental models have been able to
reproduce the overall organization of specific
feature maps in V1, such as orientation maps, but
the neurons in the simulated maps behave quite
unlike real V1 neurons, and in many cases are not
even testable on actual visual stimuli because the
developmental models are so abstract.
In this review of results from a large set of models
developed from shared principles and a set of
underlying software components, I show how these
models represent a single, consistent explanation
for a wide body of experimental evidence, and form a
compact hypothesis for much of the development and
behavior of neurons in the visual cortex. The
models are the first developmental models with
wiring consistent with V1, the first to have
realistic behavior with respect to visual contrast,
the first to include all of the demonstrated visual
feature dimensions, and the first to have wiring
compatible with anatomical results. The goal is to
have a comprehensive explanation for why V1 is wired
as it is in the adult, and how that circuitry leads
to the observed behavior of the neurons during
visual tasks.",
}
--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
_______________________________________________
visionlist mailing list
visionlist@visionscience.com
http://visionscience.com/mailman/listinfo/visionlist
modelling neural regions and systems, along with a new review paper
that ties together work from dozens of projects with collaborators
using this software to model the primary visual cortex.
The review paper shows how these separate projects add up into what is
hoped to be the most complete model of the development of functional
properties of V1 neurons to date:
James A. Bednar.
Building a mechanistic model of the development and function of the primary visual cortex.
Journal of Physiology-Paris, 106:194-211, 2012.
http://dx.doi.org/10.1016/j.jphysparis.2011.12.001
http://homepages.inf.ed.ac.uk/jbednar/papers/bednar.jpp12.pdf
The freely available, cross-platform (Linux, Mac, Windows) Python
software packages include:
Param/ParamTk: Handling parameters used in scientific programs
ImaGen: Generating input patterns and connection patterns
Topographica: Modelling interconnected neural regions
More details about each package and the review paper are included below.
Links to 31 publications using this software so far are available here:
http://topographica.org/Home/pubs.html
Thanks to the 30+ collaborators who have contributed to the research
and software development reported here, each cited in the review
paper or listed on topographica.org. We would very much appreciate
any feedback, suggestions, or ideas for future collaboration.
Jim
James A. Bednar, Ph.D.
Senior Lecturer, University of Edinburgh
Director, Computational Systems Neuroscience Group
http://homepages.inf.ed.ac.uk/jbednar/research.html
Director, Edinburgh Doctoral Training Centre in
Neuroinformatics and Computational Neuroscience
http://anc.ed.ac.uk/dtc
_______________________________________________________________________________
OPEN-SOURCE SOFTWARE PACKAGES
Param 1.0 released 7/2012 (http://ioam.github.com/param/):
The Param library makes it simple to add support for Parameters;
Param has no dependencies and is very lightweight, so that it can
be used with any Python program. A Parameter is a special type of
Python attribute extended to have features such as type and range
checking, dynamically generated values, documentation strings,
default values, etc., each of which is inherited from parent
classes if not specified in a subclass. Parameters are extremely
useful for writing scientific software, making it clear which
values are intended to be changed in practice and avoiding
potentially dangerous user errors.
ParamTk 0.8 released 7/2012 (http://ioam.github.com/paramtk/):
Optional extension to Param that provides a GUI for editing
parameter values for your objects without requiring any
GUI-specific coding.
ImaGen 1.0 released 7/2012 (http://ioam.github.com/imagen/):
Provies comprehensive support for creating resolution-independent
spatial pattern distributions. ImaGen consists of a large library
of primarily two-dimensional patterns, including mathematical
functions, geometric primitives, images read from files, and many
ways to combine or select from any other patterns. These patterns
can be used in any Python program that needs configurable patterns
or a series of patterns, with only a small amount of user-level
code to specify or use each pattern.
Topographica 0.9.8 released 11/2012 (http://topographica.org/):
Topographica allows researchers to set up models of complete neural
regions and systems relatively easily, because it takes care of a
lot of the otherwise-painful details of spatial coordinate systems,
mapping between brain regions and between layers in the same
region, scaling between different sampling densities, having
spatially restricted patterns of connectivity, specifying input and
weight patterns (via ImaGen), and measuring tuning curves,
receptive fields, and maps. Topographica is a general-purpose
object-oriented event-driven simulator that provides extensive
flexibility, with families of parameterized objects that can be
customized and adapted for new modelling projects. Current models
primarily focus on the visual system, but they are implemented
using generic primitives that have also been used for somatosensory
and auditory cortex modelling, as well as subcortical and
motor-output models.
_______________________________________________________________________________
The review paper (citation below) describes the GCAL model, which
shows how a relatively small number of simple biological mechanisms,
based on Hebbian learning and homeostatic plasticity, can lead an
unorganized neural region to develop:
- Receptive fields selective for orientation, ocular dominance,
motion direction, spatial frequency, disparity, and color
- Preferences for each of these organized into realistic topographic
maps
- Lateral connections between neurons that reflect the structure of
the maps, as found experimentally
- Both simple and complex cells
The resulting neurons exhibit:
- Realistic surround modulation effects, including their
diversity, caused by interactions between these neurons
- Contrast-gain control and contrast-invariant tuning, ensuring that
they retain selectivity robustly
- Long-term and short-term plasticity (e.g. aftereffects),
emerging from mechanisms originally implemented for development
These properties each arise from an initially undifferentiated
cortical region model, suggesting that the mechanisms involved will
also explain a large variety of cortical phenomena across different
areas and modalities.
@Article{bednar:jpp12,
title = "Building a Mechanistic Model of the Development and
Function of the Primary Visual Cortex",
author = "James A. Bednar",
journal = "Journal of Physiology - Paris",
year = 2012,
volume = 106,
pages = "194--211",
url = "http://dx.doi.org/10.1016/j.jphysparis.2011.12.001",
urlalt = "http://homepages.inf.ed.ac.uk/jbednar/papers/bednar.jpp12.pdf",
abstract = "Researchers have used a very wide range of different
experimental and theoretical approaches to help
understand mammalian visual systems. These
approaches tend to have quite different assumptions,
strengths, and weaknesses. Computational models of
the visual cortex, in particular, have typically
implemented either a proposed circuit for part of
the visual cortex of the adult, assuming a very
specific wiring pattern based on findings from
adults, or else attempted to explain the long-term
development of a visual cortex region from an
initially undifferentiated starting point. Previous
models of adult V1 have been able to account for
many of the measured properties of V1 neurons, while
not explaining how these properties arise or why
neurons have those properties in particular.
Previous developmental models have been able to
reproduce the overall organization of specific
feature maps in V1, such as orientation maps, but
the neurons in the simulated maps behave quite
unlike real V1 neurons, and in many cases are not
even testable on actual visual stimuli because the
developmental models are so abstract.
In this review of results from a large set of models
developed from shared principles and a set of
underlying software components, I show how these
models represent a single, consistent explanation
for a wide body of experimental evidence, and form a
compact hypothesis for much of the development and
behavior of neurons in the visual cortex. The
models are the first developmental models with
wiring consistent with V1, the first to have
realistic behavior with respect to visual contrast,
the first to include all of the demonstrated visual
feature dimensions, and the first to have wiring
compatible with anatomical results. The goal is to
have a comprehensive explanation for why V1 is wired
as it is in the adult, and how that circuitry leads
to the observed behavior of the neurons during
visual tasks.",
}
--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
_______________________________________________
visionlist mailing list
visionlist@visionscience.com
http://visionscience.com/mailman/listinfo/visionlist
[Comp-neuro] Modelling software + major V1 modelling review paper
I am pleased to announce the release of a set of software packages for
modelling neural regions and systems, along with a new review paper
that ties together work from dozens of projects with collaborators
using this software to model the primary visual cortex.
The review paper shows how these separate projects add up into what is
hoped to be the most complete model of the development of functional
properties of V1 neurons to date:
James A. Bednar.
Building a mechanistic model of the development and function of the primary visual cortex.
Journal of Physiology-Paris, 106:194-211, 2012.
http://dx.doi.org/10.1016/j.jphysparis.2011.12.001
http://homepages.inf.ed.ac.uk/jbednar/papers/bednar.jpp12.pdf
The freely available, cross-platform (Linux, Mac, Windows) Python
software packages include:
Param/ParamTk: Handling parameters used in scientific programs
ImaGen: Generating input patterns and connection patterns
Topographica: Modelling interconnected neural regions
More details about each package and the review paper are included below.
Links to 31 publications using this software so far are available here:
http://topographica.org/Home/pubs.html
Thanks to the 30+ collaborators who have contributed to the research
and software development reported here, each cited in the review
paper or listed on topographica.org. We would very much appreciate
any feedback, suggestions, or ideas for future collaboration.
Jim
James A. Bednar, Ph.D.
Senior Lecturer, University of Edinburgh
Director, Computational Systems Neuroscience Group
http://homepages.inf.ed.ac.uk/jbednar/research.html
Director, Edinburgh Doctoral Training Centre in
Neuroinformatics and Computational Neuroscience
http://anc.ed.ac.uk/dtc
_______________________________________________________________________________
OPEN-SOURCE SOFTWARE PACKAGES
Param 1.0 released 7/2012 (http://ioam.github.com/param/):
The Param library makes it simple to add support for Parameters;
Param has no dependencies and is very lightweight, so that it can
be used with any Python program. A Parameter is a special type of
Python attribute extended to have features such as type and range
checking, dynamically generated values, documentation strings,
default values, etc., each of which is inherited from parent
classes if not specified in a subclass. Parameters are extremely
useful for writing scientific software, making it clear which
values are intended to be changed in practice and avoiding
potentially dangerous user errors.
ParamTk 0.8 released 7/2012 (http://ioam.github.com/paramtk/):
Optional extension to Param that provides a GUI for editing
parameter values for your objects without requiring any
GUI-specific coding.
ImaGen 1.0 released 7/2012 (http://ioam.github.com/imagen/):
Provies comprehensive support for creating resolution-independent
spatial pattern distributions. ImaGen consists of a large library
of primarily two-dimensional patterns, including mathematical
functions, geometric primitives, images read from files, and many
ways to combine or select from any other patterns. These patterns
can be used in any Python program that needs configurable patterns
or a series of patterns, with only a small amount of user-level
code to specify or use each pattern.
Topographica 0.9.8 released 11/2012 (http://topographica.org/):
Topographica allows researchers to set up models of complete neural
regions and systems relatively easily, because it takes care of a
lot of the otherwise-painful details of spatial coordinate systems,
mapping between brain regions and between layers in the same
region, scaling between different sampling densities, having
spatially restricted patterns of connectivity, specifying input and
weight patterns (via ImaGen), and measuring tuning curves,
receptive fields, and maps. Topographica is a general-purpose
object-oriented event-driven simulator that provides extensive
flexibility, with families of parameterized objects that can be
customized and adapted for new modelling projects. Current models
primarily focus on the visual system, but they are implemented
using generic primitives that have also been used for somatosensory
and auditory cortex modelling, as well as subcortical and
motor-output models.
_______________________________________________________________________________
The review paper (citation below) describes the GCAL model, which
shows how a relatively small number of simple biological mechanisms,
based on Hebbian learning and homeostatic plasticity, can lead an
unorganized neural region to develop:
- Receptive fields selective for orientation, ocular dominance,
motion direction, spatial frequency, disparity, and color
- Preferences for each of these organized into realistic topographic
maps
- Lateral connections between neurons that reflect the structure of
the maps, as found experimentally
- Both simple and complex cells
The resulting neurons exhibit:
- Realistic surround modulation effects, including their
diversity, caused by interactions between these neurons
- Contrast-gain control and contrast-invariant tuning, ensuring that
they retain selectivity robustly
- Long-term and short-term plasticity (e.g. aftereffects),
emerging from mechanisms originally implemented for development
These properties each arise from an initially undifferentiated
cortical region model, suggesting that the mechanisms involved will
also explain a large variety of cortical phenomena across different
areas and modalities.
@Article{bednar:jpp12,
title = "Building a Mechanistic Model of the Development and
Function of the Primary Visual Cortex",
author = "James A. Bednar",
journal = "Journal of Physiology - Paris",
year = 2012,
volume = 106,
pages = "194--211",
url = "http://dx.doi.org/10.1016/j.jphysparis.2011.12.001",
urlalt = "http://homepages.inf.ed.ac.uk/jbednar/papers/bednar.jpp12.pdf",
abstract = "Researchers have used a very wide range of different
experimental and theoretical approaches to help
understand mammalian visual systems. These
approaches tend to have quite different assumptions,
strengths, and weaknesses. Computational models of
the visual cortex, in particular, have typically
implemented either a proposed circuit for part of
the visual cortex of the adult, assuming a very
specific wiring pattern based on findings from
adults, or else attempted to explain the long-term
development of a visual cortex region from an
initially undifferentiated starting point. Previous
models of adult V1 have been able to account for
many of the measured properties of V1 neurons, while
not explaining how these properties arise or why
neurons have those properties in particular.
Previous developmental models have been able to
reproduce the overall organization of specific
feature maps in V1, such as orientation maps, but
the neurons in the simulated maps behave quite
unlike real V1 neurons, and in many cases are not
even testable on actual visual stimuli because the
developmental models are so abstract.
In this review of results from a large set of models
developed from shared principles and a set of
underlying software components, I show how these
models represent a single, consistent explanation
for a wide body of experimental evidence, and form a
compact hypothesis for much of the development and
behavior of neurons in the visual cortex. The
models are the first developmental models with
wiring consistent with V1, the first to have
realistic behavior with respect to visual contrast,
the first to include all of the demonstrated visual
feature dimensions, and the first to have wiring
compatible with anatomical results. The goal is to
have a comprehensive explanation for why V1 is wired
as it is in the adult, and how that circuitry leads
to the observed behavior of the neurons during
visual tasks.",
}
--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
_______________________________________________
Comp-neuro mailing list
Comp-neuro@neuroinf.org
http://www.neuroinf.org/mailman/listinfo/comp-neuro
modelling neural regions and systems, along with a new review paper
that ties together work from dozens of projects with collaborators
using this software to model the primary visual cortex.
The review paper shows how these separate projects add up into what is
hoped to be the most complete model of the development of functional
properties of V1 neurons to date:
James A. Bednar.
Building a mechanistic model of the development and function of the primary visual cortex.
Journal of Physiology-Paris, 106:194-211, 2012.
http://dx.doi.org/10.1016/j.jphysparis.2011.12.001
http://homepages.inf.ed.ac.uk/jbednar/papers/bednar.jpp12.pdf
The freely available, cross-platform (Linux, Mac, Windows) Python
software packages include:
Param/ParamTk: Handling parameters used in scientific programs
ImaGen: Generating input patterns and connection patterns
Topographica: Modelling interconnected neural regions
More details about each package and the review paper are included below.
Links to 31 publications using this software so far are available here:
http://topographica.org/Home/pubs.html
Thanks to the 30+ collaborators who have contributed to the research
and software development reported here, each cited in the review
paper or listed on topographica.org. We would very much appreciate
any feedback, suggestions, or ideas for future collaboration.
Jim
James A. Bednar, Ph.D.
Senior Lecturer, University of Edinburgh
Director, Computational Systems Neuroscience Group
http://homepages.inf.ed.ac.uk/jbednar/research.html
Director, Edinburgh Doctoral Training Centre in
Neuroinformatics and Computational Neuroscience
http://anc.ed.ac.uk/dtc
_______________________________________________________________________________
OPEN-SOURCE SOFTWARE PACKAGES
Param 1.0 released 7/2012 (http://ioam.github.com/param/):
The Param library makes it simple to add support for Parameters;
Param has no dependencies and is very lightweight, so that it can
be used with any Python program. A Parameter is a special type of
Python attribute extended to have features such as type and range
checking, dynamically generated values, documentation strings,
default values, etc., each of which is inherited from parent
classes if not specified in a subclass. Parameters are extremely
useful for writing scientific software, making it clear which
values are intended to be changed in practice and avoiding
potentially dangerous user errors.
ParamTk 0.8 released 7/2012 (http://ioam.github.com/paramtk/):
Optional extension to Param that provides a GUI for editing
parameter values for your objects without requiring any
GUI-specific coding.
ImaGen 1.0 released 7/2012 (http://ioam.github.com/imagen/):
Provies comprehensive support for creating resolution-independent
spatial pattern distributions. ImaGen consists of a large library
of primarily two-dimensional patterns, including mathematical
functions, geometric primitives, images read from files, and many
ways to combine or select from any other patterns. These patterns
can be used in any Python program that needs configurable patterns
or a series of patterns, with only a small amount of user-level
code to specify or use each pattern.
Topographica 0.9.8 released 11/2012 (http://topographica.org/):
Topographica allows researchers to set up models of complete neural
regions and systems relatively easily, because it takes care of a
lot of the otherwise-painful details of spatial coordinate systems,
mapping between brain regions and between layers in the same
region, scaling between different sampling densities, having
spatially restricted patterns of connectivity, specifying input and
weight patterns (via ImaGen), and measuring tuning curves,
receptive fields, and maps. Topographica is a general-purpose
object-oriented event-driven simulator that provides extensive
flexibility, with families of parameterized objects that can be
customized and adapted for new modelling projects. Current models
primarily focus on the visual system, but they are implemented
using generic primitives that have also been used for somatosensory
and auditory cortex modelling, as well as subcortical and
motor-output models.
_______________________________________________________________________________
The review paper (citation below) describes the GCAL model, which
shows how a relatively small number of simple biological mechanisms,
based on Hebbian learning and homeostatic plasticity, can lead an
unorganized neural region to develop:
- Receptive fields selective for orientation, ocular dominance,
motion direction, spatial frequency, disparity, and color
- Preferences for each of these organized into realistic topographic
maps
- Lateral connections between neurons that reflect the structure of
the maps, as found experimentally
- Both simple and complex cells
The resulting neurons exhibit:
- Realistic surround modulation effects, including their
diversity, caused by interactions between these neurons
- Contrast-gain control and contrast-invariant tuning, ensuring that
they retain selectivity robustly
- Long-term and short-term plasticity (e.g. aftereffects),
emerging from mechanisms originally implemented for development
These properties each arise from an initially undifferentiated
cortical region model, suggesting that the mechanisms involved will
also explain a large variety of cortical phenomena across different
areas and modalities.
@Article{bednar:jpp12,
title = "Building a Mechanistic Model of the Development and
Function of the Primary Visual Cortex",
author = "James A. Bednar",
journal = "Journal of Physiology - Paris",
year = 2012,
volume = 106,
pages = "194--211",
url = "http://dx.doi.org/10.1016/j.jphysparis.2011.12.001",
urlalt = "http://homepages.inf.ed.ac.uk/jbednar/papers/bednar.jpp12.pdf",
abstract = "Researchers have used a very wide range of different
experimental and theoretical approaches to help
understand mammalian visual systems. These
approaches tend to have quite different assumptions,
strengths, and weaknesses. Computational models of
the visual cortex, in particular, have typically
implemented either a proposed circuit for part of
the visual cortex of the adult, assuming a very
specific wiring pattern based on findings from
adults, or else attempted to explain the long-term
development of a visual cortex region from an
initially undifferentiated starting point. Previous
models of adult V1 have been able to account for
many of the measured properties of V1 neurons, while
not explaining how these properties arise or why
neurons have those properties in particular.
Previous developmental models have been able to
reproduce the overall organization of specific
feature maps in V1, such as orientation maps, but
the neurons in the simulated maps behave quite
unlike real V1 neurons, and in many cases are not
even testable on actual visual stimuli because the
developmental models are so abstract.
In this review of results from a large set of models
developed from shared principles and a set of
underlying software components, I show how these
models represent a single, consistent explanation
for a wide body of experimental evidence, and form a
compact hypothesis for much of the development and
behavior of neurons in the visual cortex. The
models are the first developmental models with
wiring consistent with V1, the first to have
realistic behavior with respect to visual contrast,
the first to include all of the demonstrated visual
feature dimensions, and the first to have wiring
compatible with anatomical results. The goal is to
have a comprehensive explanation for why V1 is wired
as it is in the adult, and how that circuitry leads
to the observed behavior of the neurons during
visual tasks.",
}
--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
_______________________________________________
Comp-neuro mailing list
Comp-neuro@neuroinf.org
http://www.neuroinf.org/mailman/listinfo/comp-neuro
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