4.5 Article

Dendritic Computing: Branching Deeper into Machine Learning

期刊

NEUROSCIENCE
卷 489, 期 -, 页码 275-289

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neuroscience.2021.10.001

关键词

non-linear dendrites; plasticity; rewiring; expressivity; maxout networks; machine learning; deep neural networks

资金

  1. Ministry of education, Singapore [MOE2018-T2-2- 083]
  2. City University of Hong Kong [9380132]
  3. European Union [899265, 863245]
  4. National Natural Science Foundation of China [62076084]
  5. EINSTEIN Visiting Fellowship of the EINSTEIN Foundation, Berlin

向作者/读者索取更多资源

In this paper, the nonlinear computational power provided by dendrites in biological and artificial neurons is discussed. Biological evidence, plasticity rules, and their impact on biological learning assessed by computational models are briefly presented. The computational implications include improved expressivity, efficient resource utilization, utilization of internal learning signals, and enabling continual learning. Examples of using dendritic computations to solve real-world classification problems are discussed, categorized based on the methods of plasticity used. The convergence between concepts of deep learning and dendritic computations is highlighted, along with future research directions.
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.This article is part of a Special Issue entitled: SI: Dendritic contributions to biological and artificial computations. (c) 2022 IBRO. Published by Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据