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Redundancy in synaptic connections enables neurons to learn optimally

View ORCID ProfileNaoki Hiratani, Tomoki Fukai
doi: https://doi.org/10.1101/127407
Naoki Hiratani
1Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wako, Saitama, Japan, 351-0198
2Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom, W1T 4JG
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  • For correspondence: N.Hiratani@gmail.com
Tomoki Fukai
1Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wako, Saitama, Japan, 351-0198
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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. Applying the proposed framework to a detailed single neuron model, we show that the model accounts for many experimental observations, including 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 novel conceptual framework for synaptic plasticity and rewiring.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 29, 2018.
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Redundancy in synaptic connections enables neurons to learn optimally
Naoki Hiratani, Tomoki Fukai
bioRxiv 127407; doi: https://doi.org/10.1101/127407
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Redundancy in synaptic connections enables neurons to learn optimally
Naoki Hiratani, Tomoki Fukai
bioRxiv 127407; doi: https://doi.org/10.1101/127407

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