Redundancy in synaptic connections enables neurons to learn optimally

Naoki Hiratani, Tomoki Fukai

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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.

Original languageEnglish
Pages (from-to)E6871-E6879
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number29
DOIs
StatePublished - Jul 17 2018

Keywords

  • Connectomics
  • Dendritic computation
  • Synaptic plasticity
  • Synaptogenesis

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