Bionic Olfactory Synaptic Transistors for Artificial Neuromotor Pathway Construction and Gas Recognition

Author:

Wu Xiaocheng12,Jiang Longlong12,Xu Honghuan3,Wang Banghu12,Yang Lu3,Wang Xiaohong12,Zheng Lei4,Xu Wentao3ORCID,Qiu Longzhen12

Affiliation:

1. National Engineering Lab of Special Display Technology State Key Lab of Advanced Display Technology Academy of Opto‐Electronic Technology Hefei University of Technology Hefei 230009 P. R. China

2. Intelligent Interconnected Systems Laboratory of Anhui Anhui Province Key Laboratory of Measuring Theory and Precision Instrument School of Instrument Science and Optoelectronic Engineering Hefei University of Technology Hefei 230009 P. R. China

3. Institute of Photoelectronic Thin Film Devices and Technology Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin College of Electronic Information and Optical Engineering Nankai University Tianjin 300350 P. R. China

4. School of Food and Biological Engineering Hefei University of Technology Hefei 230009 P. R. China

Abstract

AbstractThe superior recognition ability and excitatory–inhibitory balance of the olfactory system has important applications in the efficient recognition, analysis, and processing of data. In this study, transistor synaptic devices are prepared utilizing poly‐diketo‐pyrrolopyrrole‐selenophene polymer (PTDPPSe‐5Si) with excellent electrical properties as the active layer, and dual‐gas pulses are applied for the first time to simulate excitatory and inhibitory behaviors in the olfactory system. Basic synaptic properties are successfully simulated, such as excitatory/inhibitory postsynaptic currents (EPSC/IPSC), and long‐term potentiation/depression (LTP/LTD). The regulation of excitatory–inhibitory balance in biomimetic olfaction is successfully simulated. This working mechanism is attributed to the capture and release of carriers in the channel induced by the gas's electron‐donating and electron‐withdrawing characteristics. The neuromotor pathway is constructed using synaptic devices as the key processing unit, which enables the integration of information from neurons and the output of information from motor neurons. A convolutional neural network is constructed to achieve recognition of eight common laboratory gas types and concentrations with a recognition accuracy of over 97%. The simulated excitatory and inhibitory behaviors exhibited by this device hold significant importance for the development of artificial neural networks, intelligent frameworks, and neural robots.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Wiley

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