Learning in spiking neural networks by reinforcement of stochastic synaptic transmission
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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.
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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
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- Authors
- Seung, H. S.
- Affiliations
- Howard Hughes Medical Institute (US)
- Corresponding author
- Seung, H.Sebastian
- ORCID
- 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
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 3516 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- 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
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