A neural network model on self-organizing emergence of simple-cell receptive field with orientation selectivity in visual cortex
- 19 Downloads
In order to probe into the self-organizing emergence of simple cell orientation selectivity, we tried to construct a neural network model that consists of LGN neurons and simple cells in visual cortex and obeys the Hebbian learning rule. We investigated the neural coding and representation of simple cells to a natural image by means of this model. The results show that the structures of their receptive fields are determined by the preferred orientation selectivity of simple cells. However, they are also decided by the emergence of self-organization in the unsupervision learning process. This kind of orientation selectivity results from dynamic self-organization based on the interactions between LGN and cortex.
Keywordsreceptive field orientation selectivity dynamic self-organization neural sparse coding unsupervision learning
Unable to display preview. Download preview PDF.
- 3.Shou, T. D., Brain Mechanisms of Visual Information Processing (in Chinese), Shanghai: Shanghai Science-Technology and Education Press, 1997, 188–197.Google Scholar
- 7.Rolls, E. T., Tovee, M. J., Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex, J. Neurophysiology, 1995, 73: 713–726.Google Scholar
- 11.Field, D. J., Relations between the statistics of natural images and the response properties of cortical cells, Journal of the Optical Society of America A, 1987, 4: 2379–2394.Google Scholar
- 16.Champman, B., Zahs, K. R., Stryker, M. P., Relation of cortical cell orientation selectivity to alignment of receptive fields of the geniculocortical afferents that arborize within a single orientation column in ferret visual cortex, J. Neurosci., 1991, 11: 1347–1358.Google Scholar