Stable learning in stochastic network states
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 correlation...
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Veröffentlicht in: | The Journal of neuroscience 2012-01, Vol.32 (1), p.194-214 |
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creator | El Boustani, Sami Yger, Pierre Frégnac, Yves Destexhe, Alain |
description | 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. |
doi_str_mv | 10.1523/jneurosci.2496-11.2012 |
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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. 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subjects | Action Potentials Action Potentials - physiology Algorithms Animals Cerebral Cortex Cerebral Cortex - physiology Humans Learning Learning - physiology Life Sciences Models, Neurological Nerve Net Nerve Net - physiology Neural Networks (Computer) Neuronal Plasticity Neuronal Plasticity - physiology Neurons Neurons - physiology Neurons and Cognition Stochastic Processes |
title | Stable learning in stochastic network states |
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