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

• Published in: IEEE transactions on biomedical circuits and systems Vol. PP; pp. 1 - 13
• Main Authors: Chen, Zhanfei, Wang, Xiaoping, Wang, Zilu, Yang, Chao, Huang, Tingwen, Lai, Jingang, Zeng, Zhigang
• Format: Journal Article
• Language: English
• Published: United States IEEE 15.10.2024
• Subjects: bio-inspired circuit; Biological information theory; Circuits; computing-in-memory; Decision making; Encoding; Hardware; Head; Hippocampus; Memristor; Memristors; Navigation; Organisms; spiking neural network
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2024.3480272

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× 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.