Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 710

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

A Physiological Mechanism for Hebb's Postulate of Learning

Open access
Stent, Gunther S.·1973·Proceedings of the National Academy of Sciences

Abstract

Hebb's postulate of learning envisages that activation or inactivation of extant synaptic contacts in plastic neural networks depends on the synchronous impulse activity of pre- and postsynaptic nerve cells. The physiological mechanism proposed here for this process posits that at synapses acting according to Hebb's postulate, the receptors for the neurotransmitter are eliminated from the postsynaptic membrane by the transient reversals of the sign of membrane polarization that occur during action potential impulses in the postsynaptic cell. But, since the release of neurotransmitter drives the membrane potential of the synaptic zone towards a level about half-way between the negative-inside resting potential and the positive-inside action potential, it would follow that the membrane patches surrounding the receptors of a synapse whose activity has contributed to setting off the postsynaptic impulse would be spared the full extent of the noxious polarity reversal. This mechanism can account for a neurophysiologically documented example of the operation of Hebb's postulate, namely the plasticity of the connections between fourth- and fifth-order neurons in the visual cortex of cats.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.70.4.997

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 21 references

Cited by

See all 710

Metadata

Authors
Stent, G. S.
Venue
Proceedings of the National Academy of Sciences
Published
1973-04
Volume
70
Issue
4
Pages
997-1001
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2019203033
Cited by
710
References
21
FWCI
2.29
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A Physiological Mechanism for Hebb's Postulate of Learning. https://app.cochat.ai/papers/10.1073/pnas.70.4.997
The paper
Stent, G. S. (1973). A Physiological Mechanism for Hebb's Postulate of Learning. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.70.4.997

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.