Somato-dendritic synaptic plasticity and error-backpropagation in active dendrites
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In the last decade dendrites of cortical neurons have been shown to nonlinearly combine synaptic inputs by evoking local dendritic spikes. It has been suggested that these nonlinearities raise the computational power of a single neuron, making it comparable to a 2-layer network of point neurons. But how these nonlinearities can be incorporated into the synaptic plasticity to optimally support learning remains unclear. We present a theoretically derived synaptic plasticity rule for supervised and reinforcement learning that depends on the timing of the presynaptic, the dendritic and the postsynaptic spikes. For supervised learning, the rule can be seen as a biological version of the classical error-backpropagation algorithm applied to the dendritic case. When modulated by a delayed reward signal, the same plasticity is shown to maximize the expected reward in reinforcement learning for various coding scenarios. Our framework makes specific experimental predictions and highlights the unique advantage of active dendrites for implementing powerful synaptic plasticity rules that have access to downstream information via backpropagation of action potentials.
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Biological implementations of backpropagation based on the principle of neurons storing multiple variables have been proposed.
“Thus, there is the possibility for a neuron to store both its activation itself, and the error derivative of a cost function with respect to its activation, as required in backpropagation, and biological implementations of backpropagation based on this principle have been proposed (Körding and König, 2001; Schiess et al., 2016)20.”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Schiess et al., 2016
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- Authors
- Schiess, M., Urbanczik, R., & Senn, W.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Schiess, Mathieu
- Urbanczik, Robert
- Senn, Walter
- ORCID
- Senn, Walter
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-02-03
- Volume
- 12
- Issue
- 2
- Pages
- e1004638
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4739747
- OpenAlex
- W2265299158
- Cited by
- 75
- References
- 48
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 6 · 2022: 9 · 2021: 9 · 2020: 7 · 2019: 13 · 2018: 5 · 2017: 9 · 2016: 4
- FWCI
- 6.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- BORIS (University Library Bern) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Open Access CRIS of the University of Bern
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- dendritic spikes, active dendrites, synaptic plasticity, backpropagation, supervised learning, reinforcement learning, spike timing, delayed rewards, cortical neurons, dendritic computation, spike-timing-dependent plasticity
- MeSH terms
- Action Potentials, Algorithms, Dendrites, Models, Neurological, Neuronal Plasticity, Computational Biology
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 ›
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