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.
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“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
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Items 201–228 of 228
Prediction mismatch responses arise as corrections of a predictive spiking codePreprint
van Driel, K., Rudelt, L., Priesemann, V., & Mikulasch, F. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.16.567335
“Doing this would lead top-down weights to learn predictive connections between location and stimulus [24, 29], and bottom-up weights to learn efficient stimulus representations [36, 37].”
BackgroundInfluentialUncertainty-modulated prediction errors in cortical microcircuitsPreprint
Wilmes, K. A., Petrovici, M. A., Sachidhanandam, S., & Senn, W. (2024) · eLife · doi:10.7554/elife.95127.1
Self-citationБойко, А. В., Валерьевич, Б. А., & Михайлович, К. А. (2024) · Информационные и математические технологии в науке и управлении · doi:10.25729/esi.2024.34.2.004
TDSTDPPreprint
Liu, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.21.604454
“Specifically, TDSTDP, incorporating dendritic potential dependency[10, 25, 35] in Spike-Timing-Dependent Plasticity (STDP[5], a variation of the Heb-bian plasticity rule), is capable of calculating TD and propagating values back-ward through temporally ordered neural circuits.”
BackgroundResultInfluentialAsabuki, T., & Fukai, T. (2024) · eLife · doi:10.7554/elife.92712.1.sa0
Predictive learning rules generate a cortical-like replay of probabilistic sensory experiencesPreprint
Asabuki, T., & Fukai, T. (2024) · eLife · doi:10.7554/elife.92712.1
Uncertainty-modulated prediction errors in cortical microcircuitsPreprint
Wilmes, K. A., Petrovici, M. A., Sachidhanandam, S., & Senn, W. (2024) · eLife · doi:10.7554/elife.95127.2 · Cited by 1
Self-citationBredenberg, C., Normandin, F., Richards, B. A., & Lajoie, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.27.615483
“This formulation is consistent with anatomical wiring data [43], as well as several recent theoretical studies which propose a specialized learning role for top-down projections to the apical dendrites of pyramidal neurons [45, 46, 47, 48, 49, 50].”
Predictive learning rules generate a cortical-like replay of probabilistic sensory experiencesPreprint
Asabuki, T., & Fukai, T. (2024) · eLife · doi:10.7554/elife.92712.2
Parametrizing analog multi-compartment neurons with genetic algorithms
Stock, R., Kaiser, J., Müller, E., Schemmel, J., & Schmitt, S. (2024) · Open Research Europe · doi:10.12688/openreseurope.15775.2
Minimal Neuron Circuits—Part II: Integrators
Nabil, A., Kumar, T. N., & Almurib, H. A. F. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3572844
“Additionally, the proposed circuits can potentially be used to implement voltage-dependent synaptic plasticity rules [33], [34], which is not possible using neuron circuits that emulate the integrate-and-fire neuron model.”
Bredenberg, C., Normandin, F., Richards, B. A., & Lajoie, G. (2025) · eLife · doi:10.7554/elife.105968.1
Human-specific fast synaptic kinetics enable rapid detection of predictive featuresPreprint
Obi-Nagata, K., Suzuki, N., Kosugi, K., Ozawa, K., Sasaguri, H., Niimi, K., Miyamoto, K., Takao, M., Toda, M., Asabuki, T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.03.686448
“To understand the underlying mechanism, we compared the somato-dendritic prediction error over these models ( 26 ).”
Lagani, G., Falchi, F., Gennaro, C., & Amato, G. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5708652
Transient boosting of action potential backpropagation for few-shot temporal pattern learning
Sivori, G., & Fukai, T. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013777 · Cited by 2
“Our proposed biological rule was initially inspired on a machine-learning-based rule derived from the hypothesis that the dendritic synaptic activity learns to predict the somatic spike response [8,11,12,26].”
InfluentialBredenberg, C., Normandin, F., Richards, B., & Lajoie, G. (2026) · eLife · doi:10.7554/elife.105968.2
Mechanistic explanation of neuroplasticity using equivalent circuits
Nilsson, M. (2026) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2026.1716559 · Cited by 3
Learning sculpts orthogonal task manifolds for continual skill learning in recurrent networksPreprint
Liu, Z., Kurth, A. C., Osako, Y., & Asabuki, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.15.705283 · Cited by 1
A Dendro-Centric View of Cognition in the Behaving Brain
Chavlis, S., & Poirazi, P. (2026) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-090325-115846
Berger, S., & Agnes, E. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.27.714865
Local learning with highly analog memory devices
Bennett, C. H. (2018) · theses.fr (ABES) · doi:10.70675/e49026a5z4027z44f2za795z67ee03a38150
“This formulation yields an elementary relationship to models of dendritic learning; in [179], dendritic learning is modeled as a system which adapts and is punished or rewarded corresponding to the difference between its expected and global contribution to the global error.”
BackgroundNeuromorphic in-memory learning with analog integrated circuits and nanoscale memristive devices
Garg, N. (2024) · theses.fr (ABES) · doi:10.70675/a5f34c0ezcc3bz42c8z8a52z529a545221ca
Gillon, C. J., & Clopath, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.08.723802
“…studies have demonstrated how pyramidal neurons, the principal neurons in the cortex, are uniquely suited to supporting predictive learning [Urbanczik & Senn, 2014; Guerguiev et al., 2017; Sacramento et al., 2018; Hert¨ag & Sprekeler, 2020; Payeur et al., 2021; Mikulasch et al., 2023], as…”
Miehl, C., & Gjorgjieva, J. (2026) · Neuron · doi:10.1016/j.neuron.2026.04.034
Cortico-subcortical multi-head self-attention as a substrate for cognitive performancePreprint
Babu, V. K., Granier, A., Proix, T., Balvočius, T., Woodman, M., Saudargienė, A., Jirsa, V., & Senn, W. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.06.743205 · Cited by 1
“…of modern AI, was proposed to be implemented in local cortical microcircuits [31, 32], and the minimization of dendritic prediction errors was suggested as a core element connecting cellular features with general computation, e.g. formulated in the neuronal least-action principle [33, 34].”
InfluentialSelf-citationThe neuroscience of transformers
Peter König, B., & Negrello, M. (2026) · Cognitive Neurodynamics · doi:10.1007/s11571-026-10566-5 · Cited by 2
“Related theories assign apical dendrites roles in context-dependent processing, pre - diction, and credit assignment (Körding and König 2000; Urbanczik and Senn 2014; Larkum 2013; Phillips et al. 2018; Phillips and Silverstein 2013).”
Local processors: Active dendrites and artificial intelligence
Hu, Y., Glatz, N. S., & Palmer, L. M. (2026) · Current Research in Neurobiology · doi:10.1016/j.crneur.2026.100169
“Biologically inspired models have demonstrated how an error signal sent to the apical dendritic compartment could function as a local teaching signal by modulating the efficacy of relevant synapses (Urbanczik and Senn, 2014; Sacramento et al., 2018; Payeur et al., 2021).”
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
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