Engrams of Fast Learning

Piette, Charlotte and Touboul, Jonathan and Venance, Laurent (2020) Engrams of Fast Learning. Frontiers in Cellular Neuroscience, 14. ISSN 1662-5102

[thumbnail of pubmed-zip/versions/2/package-entries/fncel-14-575915-r1/fncel-14-575915.pdf] Text
pubmed-zip/versions/2/package-entries/fncel-14-575915-r1/fncel-14-575915.pdf - Published Version

Download (1MB)

Abstract

Fast learning designates the behavioral and neuronal mechanisms underlying the acquisition of a long-term memory trace after a unique and brief experience. As such it is opposed to incremental, slower reinforcement or procedural learning requiring repetitive training. This learning process, found in most animal species, exists in a large spectrum of natural behaviors, such as one-shot associative, spatial, or perceptual learning, and is a core principle of human episodic memory. We review here the neuronal and synaptic long-term changes associated with fast learning in mammals and discuss some hypotheses related to their underlying mechanisms. We first describe the variety of behavioral paradigms used to test fast learning memories: those preferentially involve a single and brief (from few hundred milliseconds to few minutes) exposures to salient stimuli, sufficient to trigger a long-lasting memory trace and new adaptive responses. We then focus on neuronal activity patterns observed during fast learning and the emergence of long-term selective responses, before documenting the physiological correlates of fast learning. In the search for the engrams of fast learning, a growing body of evidence highlights long-term changes in gene expression, structural, intrinsic, and synaptic plasticities. Finally, we discuss the potential role of the sparse and bursting nature of neuronal activity observed during the fast learning, especially in the induction plasticity mechanisms leading to the rapid establishment of long-term synaptic modifications. We conclude with more theoretical perspectives on network dynamics that could enable fast learning, with an overview of some theoretical approaches in cognitive neuroscience and artificial intelligence.

Item Type: Article
Subjects: STM Digital Press > Medical Science
Depositing User: Unnamed user with email support@stmdigipress.com
Date Deposited: 20 May 2023 06:01
Last Modified: 19 Sep 2024 09:31
URI: http://publications.articalerewriter.com/id/eprint/880

Actions (login required)

View Item
View Item