Predictive dendrites as a foundation for biological learning
- 1University of Oxford
Abstract
A fundamental challenge for neuroscience is to identify laws of synaptic plasticity that are consistent with experimental observations at the level of individual neurons, whilst also explaining the ability of neural networks to learn complex tasks. Here we investigate a candidate law in which neurons adjust synaptic strengths to minimize the mismatch between somatic and dendritic activity. We demonstrate that models based on this rule can effectively adjust synaptic weights across network layers to learn non-linear tasks. Moreover, minimizing mismatch between spiking activity and dendritic inputs leads to integrate-and-fire dynamics and spike-timing-dependent plasticity. The model reproduces the experimentally observed dependencies of plasticity upon initial synaptic weights and spike patterns, whilst also generating a series of testable predictions. Our results show that a single principle - minimization of somato-dendritic mismatch - links learning at the network level with fundamental properties of individual neurons, including membrane potential dynamics and canonical features of synaptic plasticity.
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BibTeX
@misc{song2026predictive,
title = {{Predictive dendrites as a foundation for biological learning}},
author = {Y. Song and F. Innocenti and N. Mirkhani and B. Bozkurt and M. Ivan and A. Aziz and T. Lukasiewicz and A. J. King and C. Clopath and C. Akerman and N. S. Harper and R. Bogacz},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.21.760638},
url = {https://www.biorxiv.org/content/10.64898/2026.10.21.760638v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Predictive dendrites as a foundation for biological learning
AU - Y. Song
AU - F. Innocenti
AU - N. Mirkhani
AU - B. Bozkurt
AU - M. Ivan
AU - A. Aziz
AU - T. Lukasiewicz
AU - A. J. King
AU - C. Clopath
AU - C. Akerman
AU - N. S. Harper
AU - R. Bogacz
PY - 2026
DA - 2026/10/21
PB - bioRxiv
DO - 10.64898/2026.10.21.760638
UR - https://www.biorxiv.org/content/10.64898/2026.10.21.760638v1
AB - A fundamental challenge for neuroscience is to identify laws of synaptic plasticity that are consistent with experimental observations at the level of individual neurons, whilst also explaining the ability of neural networks to learn complex tasks. Here we investigate a candidate law in which neurons adjust synaptic strengths to minimize the mismatch between somatic and dendritic activity. We demonstrate that models based on this rule can effectively adjust synaptic weights across network layers to learn non-linear tasks. Moreover, minimizing mismatch between spiking activity and dendritic inputs leads to integrate-and-fire dynamics and spike-timing-dependent plasticity. The model reproduces the experimentally observed dependencies of plasticity upon initial synaptic weights and spike patterns, whilst also generating a series of testable predictions. Our results show that a single principle - minimization of somato-dendritic mismatch - links learning at the network level with fundamental properties of individual neurons, including membrane potential dynamics and canonical features of synaptic plasticity.
ER -