Adaptive spiking neuron with population coding for a residual spiking neural network

Spiking neural networks (SNNs) have attracted significant research attention due to their inherent sparsity and event-driven processing capabilities. Recent studies indicate that the incorporation of convolutional and residual structures into SNNs can substantially enhance performance. However, these converted spiking residual structures are associated with increased complexity and stacked parameterized spiking neurons. To address this challenge, this paper proposes a meticulously refined two-layer decision structure for residual-based SNNs, consisting solely of fully connected and spiking neuron layers. Specifically, the spiking neuron layers incorporate an innovative dynamic leaky integrate-and-fire (DLIF) neuron model with a nonlinear self-feedback mechanism, characterized by dynamic threshold adjustment and a self-regulating firing rate. Furthermore, diverging from traditional direct encoding, which focuses solely on individual neuronal frequency, we introduce a novel mixed coding mechanism that combines direct encoding with multineuronal population decoding. The proposed architecture improves the adaptability and responsiveness of spiking neurons in various computational contexts. Experimental results demonstrate the superior efficacy of our approach. Although it uses a highly simplified structure with only 6 timesteps, our proposal achieves enhanced performance in the experimental trials compared to multiple state-of-the-art methods. Specifically, it achieves accuracy improvements of 0.01-1.99% on three static datasets and of 0.14-7.50% on three N-datasets. The DLIF model excels in information processing, showing double mutual information compared to other neurons. In the sequential MNIST dataset, it balances biological realism and practicality, enhancing memory and the dynamic range. Our proposed method not only offers improved computational efficacy and simplified network structure but also enhances the biological plausibility of SNN models and can be easily adapted to other deep SNNs.