4:00 p.m., Monday (Jan. 13th)

Math 203

Duane Nykamp

Department of Mathematics, UCLA

Reconstructing the coupling of neurons from spike times

Reconstructing the connectivity patterns of neural networks in higher organisms has been a formidable challenge. Most neurophysiology data consist only of spike times, and current analysis methods are unable to resolve the ambiguity in connectivity patterns that could lead to such data. I present a new method that can determine the presence of a connection between two neurons from the spike times of the neurons in response to spatiotemporal white noise. The method successfully distinguishes such a direct connection from common input originating from other, unmeasured neurons. Although the method is based on a highly idealized linear-nonlinear approximation of neural response, simulations demonstrate that the approach can work with a more realistic, integrate-and-fire neuron model. I propose that the approach exemplified by this analysis may yield viable tools for reconstructing neural networks from data gathered in neurophysiology experiments.

Refreshments will be served at 3:45 p.m. in the Faculty Lounge, Math Annex (Room 1115).

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