Abstract
Nitric oxide (NO) is a newly discovered neuronal messenger which transmits information in brain by way of diffusion. This phenomenon suggests a non-localized form of learning in computational neural network models. Based on a new dynamical description of single neuron learning, we demonstrate that NO diffusion can speed up the learning as well as reduce noise when a neuron is storing a pattern. Based on this idea we present the theory and application of a competitive learning algorithm that simulates pattern identification and classification in neural networks.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1383-1384 |
| Number of pages | 2 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 3 |
| State | Published - 1997 |
| Event | Proceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, USA Duration: Oct 30 1997 → Nov 2 1997 |
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics