What Can a Single Neuron Compute?
Abstract
In this paper we formulate a description of the computation per(cid:173) formed by a neuron as a combination of dimensional reduction and nonlinearity. We implement this description for the Hodgkin(cid:173) Huxley model, identify the most relevant dimensions and find the nonlinearity. A two dimensional description already captures a significant fraction of the information that spikes carry about dy(cid:173) namic inputs. This description also shows that computation in the Hodgkin-Huxley model is more complex than a simple integrate(cid:173) and-fire or perceptron model.
Cite
Text
Arcas et al. "What Can a Single Neuron Compute?." Neural Information Processing Systems, 2000.Markdown
[Arcas et al. "What Can a Single Neuron Compute?." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/arcas2000neurips-single/)BibTeX
@inproceedings{arcas2000neurips-single,
title = {{What Can a Single Neuron Compute?}},
author = {Arcas, Blaise Agüera y and Fairhall, Adrienne L. and Bialek, William},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {75-81},
url = {https://mlanthology.org/neurips/2000/arcas2000neurips-single/}
}