[comp-neuro] Worskhop on Mathematical Neuroscience

Jonathan Rubin rubin at math.pitt.edu
Tue May 15 04:29:17 CEST 2007


FIRST ANNOUNCEMENT AND CALL FOR PARTICIPANTS

WORKSHOP ON MATHEMATICAL NEUROSCIENCE

Including two focus sessions:
1) AUDITION
2) PARKINSONIAN TREMOR AND DEEP BRAIN STIMULATION

Centre de Recherches Mathématiques, Université de Montréal, Montréal, Canada

September 16-19, 2007

SPONSORED BY NSERC (CRM), MITACS AND MATHEON

CONFERENCE URL: http://www.crm.umontreal.ca/Neuro07/index_e.shtml

Organizers: S. Coombes (Nottingham), A. Longtin (Ottawa), J. Rubin (Pittsburgh)

The goal of this workshop is to provide an overview of the current
state of research in mathematical approaches to neuroscience.  This
vibrant area, seeded by successes in understanding nerve action
potentials, dendritic processing, and the neural basis of EEG, has
moved on to encompass increasingly sophisticated tools of modern
applied mathematics.  Included among these are Evans functions
techniques for studying wave stability and bifurcation in tissue level
models of synaptic and EEG activity, heteroclinic cycling in theories
of olfactory coding, the use of geometric singular perturbation theory
in understanding rhythmogenesis, stochastic differential equations
describing inherent sources of neuronal noise, spike-density
approaches to modelling network evolution, weakly nonlinear analysis
of pattern formation, and the role of canards in organising neural
dynamics.

Importantly, the workshop will also address the novel application of
such techniques in two half-day sessions, one on AUDITION and the
other on PARKINSONIAN TREMOR AND DEEP BRAIN STIMULATION.  Hence,
participants will be drawn from both the mathematical and experimental
sciences.

A further aim of this workshop will be to encourage other applied
mathematicians into this thriving area of research where their work
can have an impact on both experimental and computational
neuroscience.  Indeed a major challenge for the mathematical
neuroscience community is to complement new biological understanding
of network function with a mathematical understanding of dynamics for
computation.  In particular this will require studies that go beyond
the mathematically tractable cases of highly symmetric and homogeneous
networks and for us to understand the role that noise,
inhomogeneities, delays, and feedback have to play in shaping the
dynamic states of biological neural networks.




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