Reconstructing Stimulus-Driven Neural Networks from Spike Times
Abstract
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.
Cite
Text
Nykamp. "Reconstructing Stimulus-Driven Neural Networks from Spike Times." Neural Information Processing Systems, 2002.Markdown
[Nykamp. "Reconstructing Stimulus-Driven Neural Networks from Spike Times." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/nykamp2002neurips-reconstructing/)BibTeX
@inproceedings{nykamp2002neurips-reconstructing,
title = {{Reconstructing Stimulus-Driven Neural Networks from Spike Times}},
author = {Nykamp, Duane Q.},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {325-332},
url = {https://mlanthology.org/neurips/2002/nykamp2002neurips-reconstructing/}
}