A Bio-Inspired Computational Astrocyte Model for Spiking Neural Networks

The mammalian brain is the most capable and complex computing entity known today, and for years there has been research focused on reproducing the brain's capabilities. An early example of this endeavor was the perceptron which has become a core building block of neural network models in the deep learning era. Despite many successes achieved through deep learning, these networks behave much differently than their biological counterparts. In a search for improvements to things like training time and dataset size, power consumption, noise, and adversarial input tolerance, research is looking towards the brain, and bio-inspired computing models. Spiking neural networks (SNNs) take a step closer to biology in their operation and are the focus of much research, geared towards reproducing some of these novel features of the brain. As of yet, SNNs have not reached their full potential. This work explores the advancement of SNNs though introduction of a novel astrocyte model. Astrocytes, initially thought to be passive support cells in the brain are now known to actively participate in neural processing. The proposed astrocyte model is geared towards synaptic plasticity and is shown to generalize and extend spike-timing dependent plasticity (STDP). In addition, the model supports plasticity-focused multi-synapse integration. Logical AND was used as a test function for multi-synapse astrocyte plasticity, and convergence for a single leaky integrate and fire (LIF) neuron with 2, 3, and 4 synapses was demonstrated. The plasticity model is general and many other functions could presumably be used in-place of AND, taking advantage of a multi-synapse view. This leaves a solid path forward for future work.