A Memristor-Based Spiking Neural Network with High Scalability and Learning Efficiency

Spike-timing dependent plasticity (STDP)-based spiking neural network (SNN) is a promising choice to realize unsupervised intelligent systems with a limited power budget. In addition to STDP, another two bio-inspired mechanisms of lateral inhibition and homeostasis are always implemented in the unsupervised training procedure of STDP-based SNNs. However, the existing methods to achieve lateral inhibition necessitate a great number of connections that are proportional to the square of the number of learning neurons, and the existing hardware solution of homeostasis demands complex circuits for each learning neuron, both of which challenge the hardware implementation of STDP-based SNNs. In this paper, we propose a novel SNN using memristor-based inhibitory synapses to realize the mechanisms of lateral inhibition and homeostasis with low hardware complexity. The proposed SNN can improve the network scalability by reducing the connection number for lateral inhibition from N2 to N and reduce the hardware overhead by leveraging the circuit of lateral inhibition to achieve homeostasis. Software simulations on the recognition task on MNIST dataset show that the proposed SNN achieves a ∼2 times higher learning efficiency with comparable accuracy. In addition, the challenging properties of realistic memristor devices, including limited number of resistive states, intrinsic parameter variation, and permanent open device, are added in the simulation to evaluate the robustness of our proposed approach.