Evidence map›Paper›PMID 33250712›Full record

ReviewFrontiers in cellular neuroscience2020

Engrams of Fast Learning.

Charlotte Piette, Jonathan Touboul, Laurent Venance

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in cellular neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Lactate supply overtakes glucose when neural computational and cognitive loads scale up.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  8. Review
  9. Writing Abilities in Compulsive Prisoners.Frontiers in psychology · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 2 countries.

Charlotte PietteCenter for Interdisciplinary Research in Biology, College de France, INSERM U1050, CNRS UMR7241, Université PSL, Paris, France.
Jonathan TouboulDepartment of Mathematics and Volen National Center for Complex Systems, Brandeis University, Waltham, MA, United States.
Laurent VenanceCenter for Interdisciplinary Research in Biology, College de France, INSERM U1050, CNRS UMR7241, Université PSL, Paris, France.
Brandeis University · USCentre National de la Recherche Scientifique · FRCollège de France · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencefast learningmemory engramneurocomputational modelsneuromodulationone-shot learning (OSL)synaptic plasticity (LTP/LTD)

Identifiers

PMID33250712
PMCPMC7676431
OpenAlexW3096557772

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.