ABSTRACT
Adaptive processing allows sensory systems to autonomically adjust their sensitivity with exposure to a constant sensory stimulus and thus organisms to adapt to environmental variations. Bioinspired electronics with adaptive functions are highly desirable for the development of neuromorphic sensory systems (NSSs). Herein, the functions of desensitization and sensitivity changing with background intensity (i.e., Weber's law), as two fundamental cues of sensory adaptation, are biorealistically demonstrated in an Ag nanowire (NW)-embedded sodium alginate (SA) based complementary memristor. In particular, Weber's law is experimentally emulated in a single complementary memristor. Furthermore, three types of adaptive NSS unit are constructed to realize a multiple perceptual capability that processes the stimuli of illuminance, temperature, and pressure signals. Taking neuromorphic vision as an example, scotopic and photopic adaptation functions are well reproduced for image enhancement against dark and bright backgrounds. Importantly, an NSS system with multisensory integration function is demonstrated by combining light and pressure spikes, where the accuracy of pattern recognition is obviously enhanced relative to that of an individual sense. This work offers a new strategy for developing neuromorphic electronics with adaptive functions and paves the way toward developing a highly efficient NSS.
ABSTRACT
A neuromorphic computing chip that can imitate the human brain's ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system.