Engrams of Fast Learning

Fast learning designates the behavioural 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 i...

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Veröffentlicht in:Frontiers in cellular neuroscience 2020-10, Vol.14, p.575915-575915
Hauptverfasser: Piette, Charlotte, Touboul, Jonathan, Venance, Laurent
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Sprache:eng
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Zusammenfassung:Fast learning designates the behavioural 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 behaviours, 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 behavioural paradigms used to test fast learning memories: those preferentially involve a single and brief (from few hundred millisecondss to few minutes) exposure 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 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 induction plasticity mechanisms leading to the rapid establishment of long-term synaptic modifications, in light of the sparse and bursting nature of neuronal activity observed during the fast learning experience. We conclude with more theoretical perspectives on network dynamics that could enable fast learning, with an overview of the theoretical propositions originating from cognitive neuroscience and artificial intelligence models.
ISSN:1662-5102
1662-5102
DOI:10.3389/fncel.2020.575915