Divisive Normalization, Line Attractor Networks and Ideal Observers
Abstract
Gain control by divisive inhibition, a.k.a. divisive normalization, has been proposed to be a general mechanism throughout the vi(cid:173) sual cortex. We explore in this study the statistical properties of this normalization in the presence of noise. Using simulations, we show that divisive normalization is a close approximation to a maximum likelihood estimator, which, in the context of population coding, is the same as an ideal observer. We also demonstrate ana(cid:173) lytically that this is a general property of a large class of nonlinear recurrent networks with line attractors. Our work suggests that divisive normalization plays a critical role in noise filtering, and that every cortical layer may be an ideal observer of the activity in the preceding layer.
Cite
Text
Denève et al. "Divisive Normalization, Line Attractor Networks and Ideal Observers." Neural Information Processing Systems, 1998.Markdown
[Denève et al. "Divisive Normalization, Line Attractor Networks and Ideal Observers." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/deneve1998neurips-divisive/)BibTeX
@inproceedings{deneve1998neurips-divisive,
title = {{Divisive Normalization, Line Attractor Networks and Ideal Observers}},
author = {Denève, Sophie and Pouget, Alexandre and Latham, Peter E.},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {104-110},
url = {https://mlanthology.org/neurips/1998/deneve1998neurips-divisive/}
}