Format

Send to

Choose Destination
Proc Natl Acad Sci U S A. 2002 Jun 25;99(13):8974-9. Epub 2002 Jun 11.

Computational subunits of visual cortical neurons revealed by artificial neural networks.

Author information

1
Division of Neurobiology, Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA.

Abstract

A crucial step toward understanding visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. Many neurons in the visual cortex exhibit nonlinear responses, making it difficult to characterize their stimulus-response relationships. Here, we recorded the responses of primary visual cortical neurons of the cat to spatiotemporal random-bar stimuli and trained artificial neural networks to predict the response of each neuron. The random initial connections in the networks consistently converged to regular patterns. Analyses of these connection patterns showed that the response of each complex cell to the random-bar stimuli could be well approximated by the sum of a small number of subunits resembling simple cells. The direction selectivity of each complex cell measured with drifting gratings was also well predicted by the combination of these subunits, indicating the generality of the model. These results are consistent with a simple functional model for complex cells and demonstrate the usefulness of the neural network method for revealing the stimulus-response transformations of nonlinear neurons.

PMID:
12060706
PMCID:
PMC124408
DOI:
10.1073/pnas.122173799
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center