Spatiotemporal Information Processing Emulated by Multiterminal Neuro‐Transistor Networks. Issue 21 (8th April 2019)

• Record Type: Journal Article
• Authors: He, Yongli; Nie, Sha; Liu, Rui; Jiang, Shanshan; Shi, Yi; Wan, Qing
• Is Part Of: Advanced materials. Volume 31:Issue 21(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-08
• Subjects: capacitive coupling -- multiterminal neuromorphic devices -- spatiotemporal information processing; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.201900903
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: All external sensory stimuli produce a spatiotemporal pattern of action potentials, which is transmitted to the biological neural system to be processed. The relative timing of synaptic spikes from different presynaptic neurons represents the features of the stimuli. A fundamental prerequisite in cortical information processing is the discrimination of different spatiotemporal input sequences. Here, capacitively coupled multiterminal oxide‐based neuro‐transistors are proposed for spatiotemporal information processing, mimicking the dendritic discriminability of different spatiotemporal input sequences. The experimental results demonstrate that such multiterminal neuromorphic devices can act as spatiotemporal information processing compartments for fundamental cortical computation. Also, as an example of spatiotemporal information processing, sound location functionality of the human brain is also emulated by constructing a simple artificial neural network based on such oxide‐based multiterminal neuro‐transistors. Abstract : Spatiotemporal information processing is demonstrated based on capacitively coupled multiterminal neuro‐transistor networks. The dendritic discrimination of different spatiotemporal input patterns is emulated, indicating that it can act as a spatiotemporal information processing compartment for fundamental cortical computation. In addition, sound azimuth detection is achieved based on a simple neural network.