The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
Learning by the Dendritic Prediction of Somatic Spiking
Open accessAbstract
Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“One way to biologically implement the hippocampal replay as a target or 'teaching signal' for the FF neurons is to 'nudge' these neurons in the soma, while the synapses projecting from the FF pathway to their dendrites learn to reproduce the somatic nudging [59].”
From How Adversarial REM Dreams May Facilitate Creativity, and Why We Become Aware of Them (2024) · cites this paper as [59]
In the citing paper
“Interestingly, such sub-networks might even be part of the same neuron and map onto “apical” vs. “basal” parts of the dendritic tree (Körding and König, 2001; Urbanczik and Senn, 2014).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Urbanczik and Senn, 2014
Citations
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
Short-Term Synaptic Plasticity
Zucker, R. S., & Regehr, W. G. (2002) · Annual Review of Physiology · doi:10.1146/annurev.physiol.64.092501.114547 · Cited by 5,068
Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997) · Science · doi:10.1126/science.275.5297.213 · Cited by 3,832
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811
Self-organized formation of topographically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · Cited by 2,995
Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
Song, S., Miller, K. D., & Abbott, L. F. (2000) · Nature Neuroscience · doi:10.1038/78829 · Cited by 2,794
Phase relationship between hippocampal place units and the EEG theta rhythm
O'Keefe, J. M., & Recce, M. L. (1993) · Hippocampus · doi:10.1002/hipo.450030307 · Cited by 2,643
The distinct modes of vision offered by feedforward and recurrent processing
Lamme, V. A. F., & Roelfsema, P. R. (2000) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01657-x · Cited by 2,338 · cited 1×
“This provides an angle on longstanding discussions on the relative importance of fast feedforward versus slower recurrent processing in the brain (Lamme and Roelfsema, 2000; Ganguli and Latham, 2009).”
Synaptic plasticity: taming the beast
Abbott, L. F., & Nelson, S. B. (2000) · Nature Neuroscience · doi:10.1038/81453 · Cited by 2,263 · cited 1×
“Theoretical studies on the function of STDP have mostly assumed point neurons (Abbott and Nelson, 2000; Song et al., 2000; Kempter et al., 2001; Gütig et al., 2003).”
Cited by
Sorted by citations
Synaptic Plasticity Forms and Functions
Magee, J. C., & Grienberger, C. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-090919-022842 · Cited by 806
“…of network innervate the distal dendritic regions in relative isolation from the feedforward input, allow error-like signals to be locally calculated within the dendrites of individual hidden-layer neurons (Körding & König 2001, Urbanczik & Senn 2014, Guerguiev et al. 2017, Sacramento et al. 2018).”
BackgroundRandom synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T., Cownden, D., Tweed, D., & Akerman, C. J. (2016) · Nature Communications · doi:10.1038/ncomms13276 · Cited by 971
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0277-3 · Cited by 908
Towards deep learning with segregated dendrites
Guerguiev, J., Lillicrap, T., & Richards, B. A. (2017) · eLife · doi:10.7554/elife.22901 · Cited by 422
“Our model builds on previous neural networks research (Lee et al., 2015; Lillicrap et al., 2016) as well as computational studies of supervised learning in multi-compartment neurons (Urbanczik and Senn, 2014; Körding and König, 2001; Spratling and Johnson, 2006).”
BackgroundMethodResultInfluentialWulfram Gerstner, B., Lehmann, M., Liakoni, V., Corneil, D., & Brea, J. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00053 · Cited by 354
“, 2010), a Hebbian two-factor rule if used in unsupervised learning (Urbanczik and Senn, 2014), but the same rule must be seen as a three-factor rule with a neuron-specific (non-global) third factor in the supervised setting.”
BackgroundMethodInfluential
Metadata
- Authors
- Urbanczik, R., & Senn, W.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Senn, Walter
- ORCID
- Senn, Walter
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 81
- Issue
- 3
- Pages
- 521-528
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2006814548
- Cited by
- 230
- References
- 31
- Citations by year
- 2026: 14 · 2025: 19 · 2024: 44 · 2023: 19 · 2022: 23 · 2021: 30 · 2020: 29 · 2019: 11 · 2018: 12 · 2017: 13 · 2016: 11 · 2015: 3 · 2014: 2
- FWCI
- 5.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion)
- 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
- spike-timing-dependent plasticity, dendritic potentials, compartmental neuron model, dendritic synapses, prediction error, stochastic fluctuations, supervised learning, unsupervised learning, reinforcement learning
- MeSH terms
- Action Potentials, Animals, Computer Simulation, Dendrites, Learning, Models, Neurological, Neuronal Plasticity, Neurons, Synapses, Time Factors
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
