Dendritic computing: Branching deeper into machine learning

Jyotibdha Acharya*, Arindam Basu, Robert Legenstein, Thomas Limbacher, Panayiota Poirazi, Xundong Wu

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftReview eines Fachbereichs (Review article)Begutachtung

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.
Originalspracheenglisch
Seiten (von - bis)275-289
Seitenumfang15
FachzeitschriftNeuroscience
Jahrgang489
DOIs
PublikationsstatusVeröffentlicht - 1 Mai 2022

ASJC Scopus subject areas

  • Neurowissenschaften (insg.)

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