Monthly
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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

June 2009, Vol. 21, No. 6, Pages 1749-1775.
(doi: 10.1162/neco.2009.02-08-708)
© 2009 Massachusetts Institute of Technology
Adaptive Synchronization of Activities in a Recurrent Network
Article PDF (1.74 MB)
Abstract

Predictive learning rules, where synaptic changes are driven by the difference between a random input and its reconstruction derived from internal variables, have proven to be very stable and efficient. However, it is not clear how such learning rules could take place in biological synapses. Here we propose an implementation that exploits the synchronization of neural activities within a recurrent network. In this framework, the asymmetric shape of spike-timing-dependent plasticity (STDP) can be interpreted as a self-stabilizing mechanism. Our results suggest a novel hypothesis concerning the computational role of neural synchrony and oscillations.