UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Redundancy in synaptic connections enables neurons to learn optimally

Hiratani, N; Fukai, T; (2018) Redundancy in synaptic connections enables neurons to learn optimally. Proceedings of the National Academy of Sciences , 115 (29) E6871-E6879. 10.1073/pnas.1803274115. Green open access

[thumbnail of Hiratani_Redundancy in synaptic connections enables neurons to learn optimally_AAM.pdf.pdf]
Preview
Text
Hiratani_Redundancy in synaptic connections enables neurons to learn optimally_AAM.pdf.pdf - Accepted Version

Download (389kB) | Preview

Abstract

Recent experimental studies suggest that, in cortical microcircuits of the mammalian brain, the majority of neuron-to-neuron connections are realized by multiple synapses. However, it is not known whether such redundant synaptic connections provide any functional benefit. Here, we show that redundant synaptic connections enable near-optimal learning in cooperation with synaptic rewiring. By constructing a simple dendritic neuron model, we demonstrate that with multisynaptic connections synaptic plasticity approximates a sample-based Bayesian filtering algorithm known as particle filtering, and wiring plasticity implements its resampling process. Extending the proposed framework to a detailed single-neuron model of perceptual learning in the primary visual cortex, we show that the model accounts for many experimental observations. In particular, the proposed model reproduces the dendritic position dependence of spike-timing-dependent plasticity and the functional synaptic organization on the dendritic tree based on the stimulus selectivity of presynaptic neurons. Our study provides a conceptual framework for synaptic plasticity and rewiring.

Type: Article
Title: Redundancy in synaptic connections enables neurons to learn optimally
Open access status: An open access version is available from UCL Discovery
DOI: 10.1073/pnas.1803274115
Publisher version: https://doi.org/10.1073/pnas.1803274115
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: synaptic plasticity, connectomics, synaptogenesis, dendritic computation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10057242
Downloads since deposit
72Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item