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Revisiting Reset Mechanisms in Spiking Neural Networks for Sequential Modeling: Specialized Discretization for Binary Activated RNN

Enqi Zhang

TL;DR

This work reframes spiking neural networks (SNNs) for sequential modeling by treating them as binary-activated RNNs and decoupling memory from spike transmission. It introduces a specialized discretization perspective on reset and refractory mechanisms, arguing that spikes can be viewed as sampling points from a continuous memory distribution, which enables parallel training when only the spiking transmission is constrained. Two architectures—Fixed Refractory Period-based SNN (spikingFRssm) and SpikingPssm (a state-space memory with a PSN-style spiking module)—are proposed and analyzed, showing competitive performance on Sequential CIFAR-10 with favorable energy efficiency due to sparse spiking. The results suggest that complex nonlinear spiking dynamics may be unnecessary for practical sequence modeling, motivating simpler, interpretable designs that leverage linear memory modules with sparse spike mechanisms. Overall, the paper provides a theoretically motivated framework for interpreting spikes, demonstrates practical parallelizable architectures, and highlights energy-efficient pathways for SNN-based sequence processing.

Abstract

In the field of image recognition, spiking neural networks (SNNs) have achieved performance comparable to conventional artificial neural networks (ANNs). In such applications, SNNs essentially function as traditional neural networks with quantized activation values. This article focuses on an another alternative perspective,viewing SNNs as binary-activated recurrent neural networks (RNNs) for sequential modeling tasks. From this viewpoint, current SNN architectures face several fundamental challenges in sequence modeling: (1) Traditional models lack effective memory mechanisms for long-range sequence modeling; (2) The biological-inspired components in SNNs (such as reset mechanisms and refractory period applications) remain theoretically under-explored for sequence tasks; (3) The RNN-like computational paradigm in SNNs prevents parallel training across different timesteps. To address these challenges, this study conducts a systematic analysis of the fundamental mechanisms underlying reset operations and refractory periods in binary-activated RNN-based SNN sequence models. We re-examine whether such biological mechanisms are strictly necessary for generating sparse spiking patterns, provide new theoretical explanations and insights, and ultimately propose the fixed-refractory-period SNN architecture for sequence modeling.

Revisiting Reset Mechanisms in Spiking Neural Networks for Sequential Modeling: Specialized Discretization for Binary Activated RNN

TL;DR

This work reframes spiking neural networks (SNNs) for sequential modeling by treating them as binary-activated RNNs and decoupling memory from spike transmission. It introduces a specialized discretization perspective on reset and refractory mechanisms, arguing that spikes can be viewed as sampling points from a continuous memory distribution, which enables parallel training when only the spiking transmission is constrained. Two architectures—Fixed Refractory Period-based SNN (spikingFRssm) and SpikingPssm (a state-space memory with a PSN-style spiking module)—are proposed and analyzed, showing competitive performance on Sequential CIFAR-10 with favorable energy efficiency due to sparse spiking. The results suggest that complex nonlinear spiking dynamics may be unnecessary for practical sequence modeling, motivating simpler, interpretable designs that leverage linear memory modules with sparse spike mechanisms. Overall, the paper provides a theoretically motivated framework for interpreting spikes, demonstrates practical parallelizable architectures, and highlights energy-efficient pathways for SNN-based sequence processing.

Abstract

In the field of image recognition, spiking neural networks (SNNs) have achieved performance comparable to conventional artificial neural networks (ANNs). In such applications, SNNs essentially function as traditional neural networks with quantized activation values. This article focuses on an another alternative perspective,viewing SNNs as binary-activated recurrent neural networks (RNNs) for sequential modeling tasks. From this viewpoint, current SNN architectures face several fundamental challenges in sequence modeling: (1) Traditional models lack effective memory mechanisms for long-range sequence modeling; (2) The biological-inspired components in SNNs (such as reset mechanisms and refractory period applications) remain theoretically under-explored for sequence tasks; (3) The RNN-like computational paradigm in SNNs prevents parallel training across different timesteps. To address these challenges, this study conducts a systematic analysis of the fundamental mechanisms underlying reset operations and refractory periods in binary-activated RNN-based SNN sequence models. We re-examine whether such biological mechanisms are strictly necessary for generating sparse spiking patterns, provide new theoretical explanations and insights, and ultimately propose the fixed-refractory-period SNN architecture for sequence modeling.

Paper Structure

This paper contains 21 sections, 2 theorems, 37 equations, 15 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Background
  4. LIF model
  5. Two Perspectives of Spiking Neural Networks
  6. State Space Models
  7. Theoretical Analysis
  8. Decoupling Spiking and Memory Modules in Spiking Neural Networks
  9. Reset Mechanism: A Specialized Discretization Perspective
  10. Model Architecture
  11. Fixed Refractory Period-based Spiking Neural Network (spikingFRssm)
  12. Regularly Discretized Spiking Neural Network (spikingPssm,P:Parallel)
  13. Experiments
  14. Dataset Description
  15. Experimental Configuration
  16. ...and 6 more sections

Key Result

Theorem 4.1

A spiking neural network can encode the continuous output $f(t)$ of any memory module in the following form: where $S(\cdot)$ denotes the spike encoding function, $m(t) \in \mathbb{Z}^+$ is a time-varying function, and $\Delta t$ represents the discretization timestep.

Figures (15)

  • Figure 1: The Perspective of activation-quantized ANN
  • Figure 2: The Perspective of binary-activated RNN
  • Figure 3: Modeling Recurrent Architecture Inputs as Probability Distributions
  • Figure 4: Decoupling Spiking and Memory Modules in Spiking Neural Networks
  • Figure 5: Special Discretization Perspective of Reset Mechanisms
  • ...and 10 more figures

Theorems & Definitions (2)

  • Theorem 4.1
  • Theorem 4.2