Name
Abstract | ||
|---|---|---|
Based on the energy model for disparity-tuned neurons, we calculate probability density functions of complex cell activity for random-dot stimuli. We investigate the effects of normalization and give analytical expressions for the disparity tuning curve and its variance. We show that while normalized and non-normalized complex cells have similar tuning curves, the variance is significantly lower for normalized complex cells, which makes disparity estimation more reliable. The results of the analytical calculations are compared to computer simulations. |
Year | Venue | Keywords |
|---|---|---|
2007 | ICANN (1) | analytical model,disparity-tuned neuron,analytical calculation,non-normalized complex cell,complex cell activity,similar tuning curve,disparity estimation,divisive normalization,computer simulation,analytical expression,normalized complex cell,disparity-tuned complex cell,energy model,probability density function |
Field | DocType | Volume |
Normalization (statistics),Expression (mathematics),Complex cell,Simple cell,Artificial intelligence,Probability density function,Machine learning,Mathematics | Conference | 4668 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-74689-7 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wolfgang Stürzl | 1 | 59 | 10.23 |
| Hanspeter A. Mallot | 2 | 531 | 72.43 |
| Alois Knoll Knoll | 3 | 1700 | 271.32 |