Bioinspired interactive neuromorphic devices br

The performance of conventional computer based on von Neumann architecture is limited due to thephysical separation of memory and processor. By synergistically integrating various sensors withsynaptic devices, recently emerging interactive neuromorphic devices can directly sense/store/processvarious stimuli information from external environments and implement functions of perception,learning, memory, and computation. In this review, we present the basic model of bioinspiredinteractive neuromorphic devices and discuss the performance metrics. Next, we summarize the recentprogress and development of bioinspired interactive neuromorphic devices, which are classified intoneuromorphic tactile systems, visual systems, auditory systems, and multisensory system. They arediscussed in detail from the aspects of materials, device architectures, operating mechanisms, synapticplasticity, and potential applications. Additionally, the bioinspired interactive neuromorphic devicesthat can fuse multiple/mixed sensing signals are proposed to address more realistic and sophisticatedproblems. Finally, we discuss the pros and cons regarding to the computing neurons and integratingsensory neurons and deliver the perspectives on interactive neuromorphic devices at the material,device, network, and system levels. It is believed the neuromorphic devices can provide promisingsolutions to next generation of interactive sensation/memory/computation toward the development ofmultimodal, low-power, and large-scale intelligent systems endowed with neuromorphic features

Automated summary

This review discusses the development and applications of bioinspired interactive neuromorphic devices. By integrating various sensors and synaptic devices, these devices can sense, store, and process information from the external environment and perform functions of perception, learning, memory, and computation. The article presents the basic model and performance metrics of these devices and discusses the recent progress in materials, device architectures, synaptic plasticity, and potential applications. It also proposes devices that can fuse multiple sensing signals to address more complex problems. Finally, the pros and cons of computing neurons and integrating sensory neurons are discussed, along with the perspectives on future development at the material, device, network, and system levels.