Dendritic Computing: Branching Deeper into Machine Learning

Available online 14 October 2021

Review

Dendritic Computing: Branching Deeper into Machine Learning

Highlights

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We review studies on computational implications of dendritic nonlinearities in neurons.

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We discuss how synaptic plasticity can interact with dendritic nonlinearities for improved learning.

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We link these studies to applications in machine learning and deep learning.

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Thus, we propose a path to more powerful and more efficient machine learning methods and hardware.

Abstract

In this paper, we discuss the nonlinear computational power provided by dendrites in biological and artificial neurons. We start by briefly presenting biological evidence about the type of dendritic nonlinearities, respective plasticity rules and their effect on biological learning as assessed by computational models. Four major computational implications are identified as improved expressivity, more efficient use of resources, utilizing internal learning signals, and enabling continual learning. We then discuss examples of how dendritic computations have been used to solve real-world classification problems with performance reported on well known data sets used in machine learning. The works are categorized according to the three primary methods of plasticity used–structural plasticity, weight plasticity, or plasticity of synaptic delays. Finally, we show the recent trend of confluence between concepts of deep learning and dendritic computations and highlight some future research directions.

Keywords

Non-linear dendrites

Plasticity

Rewiring

Expressivity

Maxout networks

Machine learning

Deep Neural Networks

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© 2021 IBRO. Published by Elsevier Ltd. All rights reserved.

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