Stable learning in stochastic network states.
Sami El Boustani, Pierre Yger, Yves Frégnac and Alain Destexhe

Journal of Neuroscience 32: 194-214, 2012.

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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.