Name
Abstract | ||
|---|---|---|
Neurophysiologists are often faced with the problem of evaluating the quality of a code for a sensory or motor variable, either to relate it to the performance of the animal in a simple discrimination task or to compare the codes at various stages along the neuronal pathway. One common belief that has emerged from such studies is that sharpening of tuning curves improves the quality of the code, although only to a certain point; sharpening beyond that is believed to be harmful. We show that this belief relies on either problematic technical analysis or improper assumptions about the noise. We conclude that one cannot tell, in the general case, whether narrow tuning curves are better than wide ones; the answer depends critically on the covariance of the noise. The same conclusion applies to other manipulations of the tuning curve profiles such as gain increase. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1162/089976699300016818 | Neural Computation |
Keywords | DocType | Volume |
population code | Journal | 11 |
Issue | ISSN | Citations |
1 | 0899-7667 | 25 |
PageRank | References | Authors |
4.56 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandre Pouget | 1 | 237 | 44.18 |
| Sophie Denève | 2 | 172 | 17.55 |
| jeanchristophe ducom | 3 | 79 | 7.73 |
| Peter E. Latham | 4 | 268 | 34.55 |