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).