Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 22, 2025

Artificial neural networks (ANNs) are at the core of most Deep Learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. However, unlike biological brains who similar in a very efficient manner, DL require large number trainable parameters, making them energy-intensive prone to overfitting. Here, we show new ANN architecture incorporates structured connectivity restricted sampling properties dendrites counteracts these limitations. We find dendritic ANNs more robust overfitting match or outperform traditional on several classification tasks while using significantly fewer parameters. These advantages likely result different learning strategy, whereby nodes respond multiple classes, classical strive for class-specificity. Our findings suggest incorporation can make precise, resilient, parameter-efficient shed light how features impact strategies ANNs.

Language: Английский

Visually guided in vivo single-cell electroporation for monitoring and manipulating mammalian hippocampal neurons

Nature Protocols, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Language: Английский