Stable Learning in Stochastic Network States

By Sami El Boustani, Pierre Yger, Yves Frégnac, Alain Destexhe. 2012

36 citations recorded by the source corpus.

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 presynaptic and postsynaptic 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 account for metaplasticity. This novel algorithm is shown to both correctly predict homeostasis in synaptic weights and 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 presynaptic and postsynaptic dependency of the floating plasticity threshold is justified by a cascade of known molecular pathways, which leads to experimentally testable predictions.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “the underlying plasticity models are augmented by RCPs [31, 50, 51, 68, 82].”
The Convallis Rule for Unsupervised Learning in Cortical Networks (2013, methodology): “An illustration of how pre-and post-synaptic activity lead to weight changes under this rule is shown in Figure 1C. Physiologically, such an integration mechanism could be instantiated via self-exciting kinases as suggested previously [22].”
Models of Metaplasticity: A Review of Concepts (2015, methodology): “Another direction of research (Senn et al., 2001; El Boustani et al., 2012) introduced non-linearity in the effect of the Hebbian term, by turning it on and off depending on the past preand post-synaptic activity of the neuron.”
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector (2013, methodology): “It is striking, and in agreement with what we report here, that the timescales they find are on the order of 1 s [43].”