Mapping Biological Neuron Dynamics into an Interpretable Two-layer Artificial Neural Network

TL;DR

This paper introduces a simple, interpretable two-layer neural network model that accurately captures the dendritic processing of biological neurons and can perform various computational tasks, bridging biology and artificial intelligence.

Contribution

The development of a biologically plausible, interpretable two-layer ANN called DBNN that models dendritic integration and neuron computation more accurately than previous deep models.

Findings

DBNN accurately predicts neuron sub-threshold voltage and spike timing.

DBNN captures dendritic integration, including bilinear rules.

DBNN performs tasks like direction selectivity and image classification.

Abstract

Dendrites are crucial structures for computation of an individual neuron. It has been shown that the dynamics of a biological neuron with dendrites can be approximated by artificial neural networks (ANN) with deep structure. However, it remains unclear whether a neuron can be further captured by a simple, biologically plausible ANN. In this work, we develop a two-layer ANN, named as dendritic bilinear neural network (DBNN), to accurately predict both the sub-threshold voltage and spike time at the soma of biological neuron models with dendritic structure. Our DBNN is found to be interpretable and well captures the dendritic integration process of biological neurons including a bilinear rule revealed in previous works. In addition, we show DBNN is capable of performing diverse tasks including direction selectivity, coincidence detection, and image classification. Our work proposes a biologically interpretable ANN that characterizes the computation of biological neurons, which can be potentially implemented in the deep learning framework to improve computational ability.