nips nips2000 nips2000-124 nips2000-124-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Silvia Scarpetta, Zhaoping Li, John A. Hertz
Abstract: We apply to oscillatory networks a class of learning rules in which synaptic weights change proportional to pre- and post-synaptic activity, with a kernel A(r) measuring the effect for a postsynaptic spike a time r after the presynaptic one. The resulting synaptic matrices have an outer-product form in which the oscillating patterns are represented as complex vectors. In a simple model, the even part of A(r) enhances the resonant response to learned stimulus by reducing the effective damping, while the odd part determines the frequency of oscillation. We relate our model to the olfactory cortex and hippocampus and their presumed roles in forming associative memories and input representations. 1
[1] H Markram, J Lubke, M Frotscher, and B Sakmann, Science 275 213 (1997).
[2] J C Magee and D Johnston, Science 275 209 (1997).
[3] D Debanne, B H Gahwiler, and S M Thompson, J Physiol507 237 (1998) .
[4] G Q Bi and M M Poo, J Neurosci 18 10464 (1998).
[5] Z Li and J Hertz, Network: Computation in Neural Systems 11 83-102 (2000).
[6] Z Li and J J Hopfield, Biol Cybern 61 379-92 (1989) .
[7] M E Hasselmo, Neural Comp 5 32-44 (1993).