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Multi-compartment Neuron and Population Encoding improved Spiking Neural Network for Deep Distributional Reinforcement Learning

Computer Science

Multi-compartment Neuron and Population Encoding improved Spiking Neural Network for Deep Distributional Reinforcement Learning

Y. Sun, Y. Zeng, et al.

Discover a groundbreaking approach to deep distributional reinforcement learning through a brain-inspired spiking neural network that mimics biological neuron structures. This innovative research, conducted by Yinqian Sun, Yi Zeng, Feifei Zhao, and Zhuoya Zhao, reveals extraordinary results in Atari game experiments, outperforming traditional ANN methods.

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Playback language: English
Abstract
This paper proposes a brain-inspired spiking neural network (SNN)-based deep distributional reinforcement learning algorithm. It combines a bio-inspired multi-compartment neuron (MCN) model and a population coding method. The MCN model mimics the structure and function of biological neurons, while the population coding method implicitly represents quantile fractions in the spike information space. Experiments on Atari games show the model outperforms ANN-based FQF and ANN-SNN conversion methods.
Publisher
Not specified in provided text
Published On
Jan 18, 2023
Authors
Yinqian Sun, Yi Zeng, Feifei Zhao, Zhuoya Zhao
Tags
spiking neural networks
reinforcement learning
multi-compartment neuron
population coding
Atari games

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