Mimicking Pavlovian conditioning by memristive synapse is significant to implement neuromorphic computing at the hardware level. In this work, we demonstrated the Pavlovian conditioning based on the artificial synapse architecture of one-transistor/one-resistor (1T1R), which included an AgInSbTe/α-C-based memristor as a variable resistance and an N-MOS transistor. Thanks to stable resistance switching behavior of memristor and outstanding controllability of device conductance by transistor gating of 1T1R, the experimental demonstration of the acquisition and extinction of Pavlovian conditioning were realized. Moreover, the temporal relation between the conditioned and unconditioned stimuli was also established in which the memory time of associative learning decreased with the increase in the interval of two stimuli. This work provided an idea to biorealistically mimic the Pavlovian conditioning, paving the way for memristive neuromorphic computing.

Pavlovian conditioning achieved via one-transistor/one-resistor memristive synapse

Wang, Zhongqiang;Ielmini, Daniele;
2022-01-01

Abstract

Mimicking Pavlovian conditioning by memristive synapse is significant to implement neuromorphic computing at the hardware level. In this work, we demonstrated the Pavlovian conditioning based on the artificial synapse architecture of one-transistor/one-resistor (1T1R), which included an AgInSbTe/α-C-based memristor as a variable resistance and an N-MOS transistor. Thanks to stable resistance switching behavior of memristor and outstanding controllability of device conductance by transistor gating of 1T1R, the experimental demonstration of the acquisition and extinction of Pavlovian conditioning were realized. Moreover, the temporal relation between the conditioned and unconditioned stimuli was also established in which the memory time of associative learning decreased with the increase in the interval of two stimuli. This work provided an idea to biorealistically mimic the Pavlovian conditioning, paving the way for memristive neuromorphic computing.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1210200
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