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Neurally plausible sparse coding via competitive algorithms
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rozell, Christopher J. Johnson, Don H. Baraniuk, Richard G. Olshausen, Bruno A. |
| Copyright Year | 2007 |
| Abstract | Christopher J. Rozell, Don H. Johnson, Richard G. Baraniuk, and Bruno A. Olshausen Rice University, University of California, Berkeley Recent evidence indicates that many sensory systems employ sparse population codes [1]. However, neurally plausible mechanisms capable of efficiently finding sparse approximations are currently unknown. Signal processing researchers often employ suboptimal greedy sparse approximation algorithms [2] that iteratively select the single best vector. Though these algorithms work well in practice, they have two significant drawbacks making them implausible for neural systems: they would be difficult to implement in parallel architectures, and they have erratic temporal variations when coding smooth time-varying stimuli. We have developed and studied a new class of neurally plausible sparse approximation algorithms based on |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cosyne.org/c/images/b/b0/Cosyne-poster-II-73.pdf |
| Alternate Webpage(s) | http://dsp.rice.edu/sites/dsp.rice.edu/files/publications/conference-paper/2007/neurally-cosyne-2007.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
