Asymptotic reduction of neuroscience models

Document Type

Presentation Abstract

Presentation Date

4-17-2007

Abstract

Neuroscience models are extremely complex, they usually contain a large number of parameters that must be determined using experimental data. One of the questions that often arises may be formulated as follows: How can one find a model with the minimal number of parameters that can be reliably estimated from the available data. We will discuss the general idea of asymptotic model reduction approach that addresses this question. We will illustrate this general idea with a particular example of a complex model reduction.

Additional Details

One of two presentations given during this session. The other presentation was "Synaptic clearance of neurotransmitter and asymptotic reduction of neuroscience models" by Michael Kavanaugh.

Tuesday, 17 April 2007
4:10 p.m. in Math 109

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