AstroSNN: astrocyte-augmented spiking neural networks for unsupervised image classification
TL;DR
Simulation results show that incorporating astrocytic modulation consistently enhances classification performance in leaky integrate-and-fire (LIF) networks, including under noisy conditions, and suggest that augmenting simplified astrocytic dynamics can improve robustness and computational capability in SNNs, while also increasing their biological plausibility.
Abstract
Spiking neural networks (SNNs) have attracted growing interest due to their event-driven computation and close correspondence with biological neuronal signaling. Despite this, most SNN models focus exclusively on neuronal dynamics, overlooking the role of astrocytes—a major class of glial cells now recognized as active modulators of synaptic transmission, network excitability, and learning. In this work, an astrocyte-augmented SNN architecture is proposed by introducing a hidden layer of astrocytes that interact with excitatory neurons through gliotransmitter-mediated currents. The astrocytic dynamics are modeled using a simplified calcium-based framework derived from previous works, enabling efficient integration into spiking networks. Simulation results on an image classification task show that incorporating astrocytic modulation consistently enhances classification performance in leaky integrate-and-fire (LIF) networks, including under noisy conditions. These results suggest that augmenting simplified astrocytic dynamics can improve robustness and computational capability in SNNs, while also increasing their biological plausibility.