Inspired by the information processing with binary spikes in the brain, the
spiking neural networks (SNNs) exhibit significant low energy consumption and
are more suitable for incorporating multi-scale biological characteristics.
Spiking Neurons, as the basic information processing unit of SNNs, are often
simplified in most SNNs which only consider LIF point neuron and do not take
into account the multi-compartmental structural properties of biological
neurons. This limits the computational and learning capabilities of SNNs. In
this paper, we proposed a brain-inspired SNN-based deep distributional
reinforcement learning algorithm with combination of bio-inspired
multi-compartment neuron (MCN) model and population coding method. The proposed
multi-compartment neuron built the structure and function of apical dendritic,
basal dendritic, and somatic computing compartments to achieve the
computational power close to that of biological neurons. Besides, we present an
implicit fractional embedding method based on spiking neuron population
encoding. We tested our model on Atari games, and the experiment results show
that the performance of our model surpasses the vanilla ANN-based FQF model and
ANN-SNN conversion method based Spiking-FQF models. The ablation experiments
show that the proposed multi-compartment neural model and quantile fraction
implicit population spike representation play an important role in realizing
SNN-based deep distributional reinforcement learning.

通过结合生物启发式多室神经元模型和群编码方法，提出基于突触前神经元的深度分布式强化学习算法，其在 Atari 游戏上的表现超过了 “vanilla” 的基于 ANN 的 FQF 模型和基于 ANN-SNN 转换方法的 Spiking-FQF 模型。