Reconstructing Stimulus-Driven Neural Networks from Spike Times

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Duane Nykamp


We present a method to distinguish direct connections between two neu- rons from common input originating from other, unmeasured neurons. The distinction is computed from the spike times of the two neurons in response to a white noise stimulus. Although the method is based on a highly idealized linear-nonlinear approximation of neural response, we demonstrate via simulation that the approach can work with a more re- alistic, integrate-and-fire neuron model. We propose that the approach exemplified by this analysis may yield viable tools for reconstructing stimulus-driven neural networks from data gathered in neurophysiology experiments.