Stable learning in stochastic network states.
Sami El Boustani, Pierre Yger, Yves Frégnac and Alain
Journal of Neuroscience 32: 194-214, 2012.
The mammalian cerebral cortex is characterized in vivo by
irregular spontaneous activity, but how this ongoing dynamics
affects signal processing and learning remains unknown. The
associative plasticity rules demonstrated in vitro, mostly in
silent networks, are based on the detection of correlations
between pre- and post-synaptic activity, and hence are sensitive to
spontaneous activity and spurious correlations. Therefore, they
cannot operate in realistic network states. Here, we present a new
class of spike timing dependent plasticity learning rules with local
floating plasticity thresholds, the slow dynamics of which accounts
for metaplasticity (mSTDP). This novel algorithm is shown both to
correctly predict homeostasis in synaptic weights and to solve the
problem of asymptotic stable learning in noisy states. It is shown to
naturally encompass many other known types of learning rule,
unifying them into a single coherent framework. The mixed
pre-synaptic and post-synaptic dependency of the floating
plasticity threshold is justified by a cascade of known molecular
pathways, which leads to experimentally testable predictions.
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