| Paper: | PS-1A.23 |
| Session: | Poster Session 1A |
| Location: | Symphony/Overture |
| Session Time: | Thursday, September 6, 16:30 - 18:30 |
| Presentation Time: | Thursday, September 6, 16:30 - 18:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Laterally connected neural field provides precise centroid estimates |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1258-0 |
| Authors: |
Sebastian Waz, Charles Chubb, University of California, Irvine, United States |
| Abstract: |
The convolution operator is an essential tool in digital signal processing. Among many other things, it grants tremendous perceptual ability to a popular class of deep learning models known as convolutional neural networks; however, the convolution operator's neural implementation typically requires dense connectivity, so it also diminishes the biological plausibility of such models. We found that the convolution of an input vector with a parabolic function can be performed without dense connectivity. Specifically, this operation can be performed by a laterally connected (i.e., biologically plausible) neural network that evolves in continuous time (a ``neural field''). This particular convolution is shown to have useful properties for centroid estimation. Rapid and precise centroid estimation is known to take place early in the human visual system, but to date, this computation lacks an adequate biologically plausible neural model. Using the aforementioned results, we present such a model. |
