Hardware Implementation of PCM-Based Neurons with Self-Regulating Threshold for Homeostatic Scaling in Unsupervised Learning

Brain-inspired neuromorphic engineering aims at designing networks capable of learning from their own experience, in terms of both plasticity and stability. In biology, homeostatic scaling can regulate the frequency of neural processing in the brain and enable efficient synaptic learning activity. Implementing homeostatic regulation into hardware neural networks can thus enable stable, energy-efficient learning. Here, we present a novel artificial neuron based on phase change memory (PCM) devices capable of homeostatic regulation and power saving via self-adaptive threshold control. We experimentally show that this mechanism optimizes multi-pattern learning of the Fashion-MNIST dataset with asynchronous spike-timing-dependent plasticity (STDP). The PCM-based adaptive threshold is shown to act as a spike-frequency modulator of the whole neural network, giving robustness to the system against external perturbations. This work highlights the suitability of PCM devices for the optimization of synaptic dynamics and the implementation of brain-inspired neuromorphic circuits for cognitive agents and edge computing.