- Title
- Are sparse-coding simple cell receptive field models physiologically plausible?
- Creator
- Watters, Paul
- Date
- 2006
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/37843
- Identifier
- vital:3156
- Identifier
- ISSN:0219-6352
- Abstract
- Olshausen and Field (1996) developed a simple cell receptive field model for natural scene processing in V1, based on unsupervised learning and non-orthogonal basis function optimization of an overcomplete representation of visual space. The model was originally tested with an ensemble of whitened natural scenes, simulating pre-cortical filtering in the retinal ganglia and lateral geniculate nucleus, and the basis functions qualitatively resembled the orientation-specific responses of V1 simple cells in the spatial domain. In this study, the quantitative tuning responses of the basis functions in the spectral domain are estimated using a Gaussian model, to determine their goodness-of-fit to the known bandwidths of simple cells in primate V1. Five simulation experiments which examined key features of the model are reported: changing the size of the basis functions; using a complete versus over-complete representation; changing the sparseness factor; using a variable learning rate; and mapping the basis functions with a whitening spatial function. The key finding of this study is that across all image themes, basis function sizes, number of basis functions, sparseness factors and learning rates, the spatial-frequency tuning did not closely resemble that of primate area 17 — the model results more closely resembled the unclassified cat neurones of area 19 with a single exception, and not area 17 as predicted. [ABSTRACT FROM AUTHOR]
- Publisher
- World Scientific Publishing Company
- Relation
- Journal of Integrative Neuroscience Vol. 5, no. 3 (2006), p. 333-353
- Rights
- Copyright World Scientific Publishing Company
- Rights
- This metadata is freely available under a CCO license
- Subject
- 1109 Neurosciences; 1702 Cognitive Science; Basis functions; Natural scenes; Simple cells; Sparse coding
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