Stable learning in stochastic network states

by Sami El Boustani, Pierre Yger, Yves Frégnac, Alain Destexhe
Abstract:
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.
Reference:
Sami El Boustani, Pierre Yger, Yves Frégnac, Alain Destexhe, 2012.Stable learning in stochastic network states, The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 32.
Bibtex Entry:
@article{ElBoustani2012,
 abstract = {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.},
 author = {El Boustani, Sami
and Yger, Pierre
and Fr{'e}gnac, Yves
and Destexhe, Alain},
 day = {04},
 doi = {10.1523/JNEUROSCI.2496-11.2012},
 issn = {1529-2401},
 journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
 keyword = {Stochastic Processes},
 language = {eng},
 month = {Jan},
 number = {1},
 pages = {194--214},
 pmid = {22219282},
 title = {Stable learning in stochastic network states.},
 volume = {32},
 year = {2012}
}