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ISSN
0899-7667
E-ISSN
1530-888X
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2.21

Neural Computation

April 1, 2002, Vol. 14, No. 4, Pages 873-888
(doi: 10.1162/089976602317318992)
© 2002 Massachusetts Institute of Technology
Redistribution of Synaptic Efficacy Supports Stable Pattern Learning in Neural Networks
Article PDF (151.32 KB)
Abstract

Markram and Tsodyks, by showing that the elevated synaptic efficacy observed with single-pulse long-term potentiation (LTP) measurements disappears with higher-frequency test pulses, have critically challenged the conventional assumption that LTP reflects a general gain increase. This observed change in frequency dependence during synaptic potentiation is called redistribution of synaptic efficacy (RSE). RSE is here seen as the local realization of a global design principle in a neural network for pattern coding. The underlying computational model posits an adaptive threshold rather than a multiplicative weight as the elementary unit of long-term memory. A distributed instar learning law allows thresholds to increase only monotonically, but adaptation has a bidirectional effect on the model postsynaptic potential. At each synapse, threshold increases implement pattern selectivity via a frequency-dependent signal component, while a complementary frequency-independent component nonspecifically strengthens the path. This synaptic balance produces changes in frequency dependence that are robustly similar to those observed by Markram and Tsodyks. The network design therefore suggests a functional purpose for RSE, which, by helping to bound total memory change, supports a distributed coding scheme that is stable with fast as well as slow learning. Multiplicative weights have served as a cornerstone for models of physiological data and neural systems for decades. Although the model discussed here does not implement detailed physiology of synaptic transmission, its new learning laws operate in a network architecture that suggests how recently discovered synaptic computations such as RSE may help produce new network capabilities such as learning that is fast, stable, and distributed.