A Brain-Inspired Gating Mechanism Unlocks Robust Computation in Spiking Neural Networks

2025-09-03

AI Summary

Traditional leaky integrate-and-fire (LIF) neuron models neglect the dynamic conductance mechanisms inherent in biological neurons, limiting the robustness and computational capability of spiking neural networks (SNNs) under noise perturbations and temporal variations. To address this, we propose the Dynamic Gating Neuron (DGN) model, which— for the first time—formulates dynamic conductance as a biologically interpretable, adaptive gating mechanism to enable selective information flow regulation and perturbation suppression. The DGN model ensures theoretical stability while maintaining neuroscientific plausibility and supports end-to-end training via backpropagation through time. Evaluated on standard temporal benchmarks—including TIDIGITS and the Spiking Heidelberg Digits (SHD) dataset—DGN consistently outperforms LIF across multiple dimensions: noise robustness, temporal precision, and generalization performance. This work establishes a novel paradigm for designing SNNs that are both highly robust and biologically grounded.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

While spiking neural networks (SNNs) provide a biologically inspired and energy-efficient computational framework, their robustness and the dynamic advantages inherent to biological neurons remain significantly underutilized owing to oversimplified neuron models. In particular, conventional leaky integrate-and-fire (LIF) neurons often omit the dynamic conductance mechanisms inherent in biological neurons, thereby limiting their capacity to cope with noise and temporal variability. In this work, we revisit dynamic conductance from a functional perspective and uncover its intrinsic role as a biologically plausible gating mechanism that modulates information flow. Building on this insight, we introduce the Dynamic Gated Neuron~(DGN), a novel spiking unit in which membrane conductance evolves in response to neuronal activity, enabling selective input filtering and adaptive noise suppression. We provide a theoretical analysis showing that DGN possess enhanced stochastic stability compared to standard LIF models, with dynamic conductance intriguingly acting as a disturbance rejection mechanism. DGN-based SNNs demonstrate superior performance across extensive evaluations on anti-noise tasks and temporal-related benchmarks such as TIDIGITS and SHD, consistently exhibiting excellent robustness. Our results highlight, for the first time, a biologically plausible dynamic gating as a key mechanism for robust spike-based computation, providing not only theoretical guarantees but also strong empirical validations. This work thus paves the way for more resilient, efficient, and biologically inspired spiking neural networks.

Problem

• Enhancing noise robustness in spiking neural networks
• Addressing oversimplified neuron models in SNNs
• Implementing dynamic gating for adaptive input filtering

Innovation

• Dynamic Gated Neuron with adaptive membrane conductance
• Biologically plausible gating mechanism for noise suppression
• Enhanced stochastic stability in spiking neural networks