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