Name
Abstract | ||
|---|---|---|
We investigate in this paper the capabilities of learning sparse representations from model cells that respond to curvatures. Sparse coding has been successful at generating receptive fields similar to those of simples cells in area V1 from natural images. We are interested here in neurons from intermediate areas, such as V2 and V4. Neurons on those areas are known to respond to corners and curvatures. Endstopped cells (also known as hypercomplex) are hypothesized to be selective to curvatures and are greatly represented in area V2. We propose here a sparse coding learning approach where the input is not images, nor simple cells, but curvature selective cells. We show that by learning a sparse code of endstopped cells we can obtain different degrees of curvature representations. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-44781-0_55 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT II |
Keywords | Field | DocType |
Sparse coding, Endstopped cells, Curvature, Restricted Boltzmann Machine | Receptive field,Restricted Boltzmann machine,Curvature,Pattern recognition,Neural coding,Computer science,Hypercomplex number,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
9887 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Jose Rodríguez-Sánchez | 1 | 134 | 16.30 |
| Sabine Oberleiter | 2 | 0 | 0.34 |
| Hanchen Xiong | 3 | 14 | 4.40 |
| Justus H. Piater | 4 | 543 | 61.56 |