Neuroscience-inspired information-integration system based on stochastic magnetic tunnel junctions

Effective integration of multiple sensory information in the brain is essential to achieve accurate information perception and processing despite the noise and imperfections in biological neural systems, providing strong inspiration for improving computational accuracy in neuromorphic computing. In the future development of brainlike chips, e.g., biomimetic robots, multisensory-information integration is an essential capability for recognizing the external circumstances. Here, based on the computational model proposed by neuroscientists, we propose a hardware implementation scheme of the decentralized multisensory information-integration system using spintronic devices, in which magnetic tunnel junctions are employed as artificial neurons with stochastic dynamics. Using a one-dimensional continuous variable (orientation, head direction, etc.) as a typical example, we demonstrate that the input information from noisy cues is extracted with high accuracy after integration. This spintronic neuromorphic system exhibits remarkable tolerance for both nonuniform devices and the malfunction of different modules. The computational advantages and robustness of the spintronics-based information-integration system provide an important basis for the development of hardware neuromorphic platforms.