Noise-Driven Membrane Adaptation for Robust Context-Dependent Learning in Spiking Neural Networks

Spiking Neural Networks (SNNs), as the vanguard of the third generation of neural network models, encapsulate the nuanced temporal dynamics of biological neuronal processes, making them particularly adept at handling intricate spatiotemporal data streams. This unique capability opens new frontiers in computational neuroscience and artificial intelligence, as SNNs begin to outperform traditional networks in tasks requiring dynamic temporal processing. At the core of these advanced networks is the Leaky Integrate-and-Fire (LIF) neuron model, embodying the fundamental neural processes of charging, leakage, and firing. Despite their potential, SNNs face challenges, especially in noisy and variable environments, which can impede their context-dependent learning capabilities. Addressing this, our work introduces a novel noise-adaptive mechanism integrated within the LIF model, aimed at enhancing noise resilience and learning efficiency through dynamic modulation of the membrane time constant in response to environmental noise levels. Through comprehensive experimentation, this paper validates the effectiveness of our proposed model in improving SNN performance in varied conditions, marking a significant stride towards realizing the practical application of SNNs in complex, noise-inundated scenarios. The findings underscore the model’s potential in advancing both neuroscience research and machine learning applications, leveraging SNNs’ unique capacity for dynamic, temporal data processing.