  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.
