Adaptive thresholds for neural networks with synaptic noise
| dc.creator | Bolle, D. | |
| dc.creator | Heylen, R. | |
| dc.date | 2007-08-02 | |
| dc.date.accessioned | 2026-07-07T08:21:54Z | |
| dc.date.available | 2026-07-07T08:21:54Z | |
| dc.description | The inclusion of a macroscopic adaptive threshold is studied for the retrieval dynamics of both layered feedforward and fully connected neural network models with synaptic noise. These two types of architectures require a different method to be solved numerically. In both cases it is shown that, if the threshold is chosen appropriately as a function of the cross-talk noise and of the activity of the stored patterns, adapting itself automatically in the course of the recall process, an autonomous functioning of the network is guaranteed. This self-control mechanism considerably improves the quality of retrieval, in particular the storage capacity, the basins of attraction and the mutual information content. | |
| dc.description | 12 pages, 10 figures | |
| dc.identifier | https://arxiv.org/abs/0708.0328 | |
| dc.identifier | http://arxiv.org/abs/0708.0328 | |
| dc.identifier | International Journal of Neural Systems, Vol. 17, No. 4 (2007) 241-252 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/135474 | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.subject | Statistical Mechanics | |
| dc.title | Adaptive thresholds for neural networks with synaptic noise | |
| dc.type | text |