Cited by 340Neural dynamics and brain functionAdvanced Memory and Neural ComputingNeuroscience and Neuropharmacology Research

Learning in spiking neural networks by reinforcement of stochastic synaptic transmission

Open access
Seung, H.Sebastian·2003·Neuron

Abstract

It is well-known that chemical synaptic transmission is an unreliable process, but the function of such unreliability remains unclear. Here I consider the hypothesis that the randomness of synaptic transmission is harnessed by the brain for learning, in analogy to the way that genetic mutation is utilized by Darwinian evolution. This is possible if synapses are "hedonistic," responding to a global reward signal by increasing their probabilities of vesicle release or failure, depending on which action immediately preceded reward. Hedonistic synapses learn by computing a stochastic approximation to the gradient of the average reward. They are compatible with synaptic dynamics such as short-term facilitation and depression and with the intricacies of dendritic integration and action potential generation. A network of hedonistic synapses can be trained to perform a desired computation by administering reward appropriately, as illustrated here through numerical simulations of integrate-and-fire model neurons.

Integrity signals

Registry

Crossref · 10.1016/s0896-6273(03)00761-x

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Reinforcement learning models suggest that action values are learned at synapses onto neurons in a decision circuit, thereby influencing choice behavior.

    “Reinforcement learning models suggest that action values are learned at synapses onto neurons in a decision circuit, thereby influencing choice behavior (Seung, 2003; Wörgötter and Porr, 2005).”

    From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Seung, 2003

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Seung, H. S.
Affiliations
Howard Hughes Medical Institute (US)
Corresponding author
Seung, H.Sebastian
Venue
Neuron
Publisher
Elsevier BV
Published
2003-12
Volume
40
Issue
6
Pages
1063-1073
ISSN
0896-6273 (ISSN-L)
OpenAlex
W2061897041
Cited by
340
References
49
Citations by year
2026: 5 · 2025: 9 · 2024: 6 · 2023: 10 · 2022: 17 · 2021: 17 · 2020: 12 · 2019: 20 · 2018: 23 · 2017: 17 · 2016: 16 · 2015: 17 · 2014: 17 · 2013: 21 · 2012: 23
FWCI
3.87
Type
journal article
Language
en
Open access
Locations
  • Neuron (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
Keywords
spiking neural networks, reward learning, stochastic approximation, gradient learning, short-term plasticity, dendritic integration, integrate-and-fire neurons, reinforcement learning
MeSH terms
Action Potentials, Learning, Synaptic Transmission, Reinforcement, Psychology, Stochastic Processes, Neural Networks, Computer

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