title: Fast Learning with Weak Synaptic Plasticity.
abstract: New sensory stimuli can be learned with a single or a few presentations. Similarly, the responses of cortical neurons to a stimulus have been shown to increase reliably after just a few repetitions. Long-term memory is thought to be mediated by synaptic plasticity, but in vitro experiments in cortical cells typically show very small changes in synaptic strength after a pair of presynaptic and postsynaptic spikes. Thus, it is traditionally thought that fast learning requires stronger synaptic changes, possibly because of neuromodulation. Here we show theoretically that weak synaptic plasticity can, in fact, support fast learning, because of the large number of synapses N onto a cortical neuron. In the fluctuation-driven regime characteristic of cortical neurons in vivo, the size of membrane potential fluctuations grows only as √N, whereas a single output spike leads to potentiation of a number of synapses proportional to N. Therefore, the relative effect of a single spike on synaptic potentiation grows as √N. This leverage effect requires precise spike timing. Thus, the large number of synapses onto cortical neurons allows fast learning with very small synaptic changes. Significance statement: Long-term memory is thought to rely on the strengthening of coactive synapses. This physiological mechanism is generally considered to be very gradual, and yet new sensory stimuli can be learned with just a few presentations. Here we show theoretically that this apparent paradox can be solved when there is a tight balance between excitatory and inhibitory input. In this case, small synaptic modifications applied to the many synapses onto a given neuron disrupt that balance and produce a large effect even for modifications induced by a single stimulus. This effect makes fast learning possible with small synaptic changes and reconciles physiological and behavioral observations.
authors.affiliation: Institut d'Etudes de la Cognition, Ecole Normale Supérieure, 75005 Paris, France, Sorbonne Université, UPMC Université Paris 06 UMRS968, 75006 Paris, France, and Institut de la Vision, INSERM U968, CNRS UMR7210, 75012 Paris, France pierre.yger@inserm.fr.
authors.forename: Pierre
authors.initials: P
authors.lastname: Yger
authors.affiliation: Sorbonne Université, UPMC Université Paris 06 UMRS968, 75006 Paris, France, and Institut de la Vision, INSERM U968, CNRS UMR7210, 75012 Paris, France.
authors.forename: Marcel
authors.identifier: http://orcid.org/0000-0002-2648-4790
authors.initials: M
authors.lastname: Stimberg
authors.affiliation: Sorbonne Université, UPMC Université Paris 06 UMRS968, 75006 Paris, France, and Institut de la Vision, INSERM U968, CNRS UMR7210, 75012 Paris, France.
authors.forename: Romain
authors.initials: R
authors.lastname: Brette
mesh_terms: D000465:Algorithms; D002540:Cerebral Cortex / Q000502:physiology; D003198:Computer Simulation; D055724:Electrophysiological Phenomena / Q000502:physiology; D019706:Excitatory Postsynaptic Potentials / Q000502:physiology; D056228:Feedback, Sensory; D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology; D013569:Synapses / Q000502:physiology*
keywords: learning; network; perceptual learning; spike-timing-dependent plasticity