A Memristive Spiking Neural Network Circuit for Bio-Inspired Navigation Based on Spatial Cognitive Mechanisms

Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking; the action cell module determines the direction of subsequent movement; and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application, the entire SNN is quantized and deployed on a real memristive hardware platform, achieving about a 21$\times$ reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios.