Parallel Processing and Learning: Variability and Chaos in Self-Organization of Activity in Groups of Neurons
Final rept. 15 Jan 1989-14 Jan 1992
OREGON STATE UNIV NEWPORT HATFIELD MARINE SCIENCE CENTER
Pagination or Media Count:
Simulations Processing of chaos and memory storage. In view of our previously published findings showing that motor patterns represent adaptive behaviors may be generated by chaotic activity, we have used computer simulations to examine the ability of simple networks to learn to process chaotic signals and to perform complex operations on them. These studies have shown that even simple networks can be used to understand how networks store information, much of which information can not have been obtained from the more complex biological systems. As one example, an important and unexpected finding is that networks having trainable thresholds, in addition to trainable synapses, can performs computations that trainable synapses alone can not, regardless of the number synapses that may be included in the network. Another finding is that when networks must learn several tasks simultaneously, the effective size network is self-limiting, and probably does not require special algorithmic rules for limiting the size of successfully computing neural connections.
- Computer Programming and Software