Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling

TL;DR

This paper introduces AstroSNN, a biologically-inspired spiking neural network that incorporates astrocyte interactions, leading to improved performance in memory and language tasks while maintaining efficiency for resource-limited systems.

Contribution

The paper presents the Astrocyte-Modulated Spiking Unit (AM-SU) and AstroSNN, integrating astrocyte dynamics into neural models, advancing biological plausibility and computational capabilities.

Findings

Enhanced memory retention and language processing in AstroSNN.

Low latency, high throughput, and reduced memory usage.

Effective handling of long-term dependencies and complex linguistic structures.

Abstract

Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism. These cells regulate neural activity through tripartite synapses, directly impacting cognitive processes such as learning and memory. Despite the growing recognition of astrocytes' significance, traditional Spiking Neural Network (SNN) models remain predominantly neuron-centric, overlooking the profound influence of astrocytes on neural dynamics. Inspired by these biological insights, we have developed an Astrocyte-Modulated Spiking Unit (AM-SU), an innovative framework that integrates neuron-astrocyte interactions into the computational paradigm, demonstrating wide applicability across various hardware platforms. Our Astrocyte-Modulated Spiking Neural Network (AstroSNN) exhibits exceptional performance in tasks involving memory retention and natural language generation, particularly in handling long-term dependencies and complex linguistic structures. The design of AstroSNN not only enhances its biological authenticity but also introduces novel computational dynamics, enabling more effective processing of complex temporal dependencies. Furthermore, AstroSNN shows low latency, high throughput, and reduced memory usage in practical applications, making it highly suitable for resource-constrained environments. By successfully integrating astrocytic dynamics into intelligent neural networks, our work narrows the gap between biological plausibility and neural modeling, laying the groundwork for future biologically-inspired neural computing research that includes both neurons and astrocytes.