Preprint Open Access

Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks

Spyridon Chavlis; Panayiota Poirazi

This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adapt them in artificial neurons in order to exploit the respective benefits in machine learning.

Files (519.6 kB)
Name Size
ChavlisPoirazi2021.pdf
md5:0fb4219b1e2330d28e0a4828d50b199e
519.6 kB Download
77
76
views
downloads
All versions This version
Views 7777
Downloads 7676
Data volume 39.5 MB39.5 MB
Unique views 6565
Unique downloads 6262

Share

Cite as