journal article·2024-03-01Open access

Correlation between PPF index and short-term memory in physical-reservoir computing with artificial synapse using simple RC-series circuit

Hiroaki Komatsu, Takumi Ogawa, Norika Hosoda, Takashi Ikuno

AIP Advances· Volume 14· Issue 3

Abstract

The correlation between the short-term plasticity (STP) of artificial synapses and the computational performance of physical-reservoir computing (PRC) using artificial synapses has not yet been clarified. We investigate the correlation between the paired-pulse facilitation (PPF) index, which is an evaluation indicator of STP, and the memory capacity (MC) of the short-term memory task, which is one of the computational performance indicators of PRC, using a simple artificial synapse based on a series circuit comprising a resistor and a capacitor. The results show that a higher PPF index of the artificial synapse does not necessarily indicate a higher MC of the PRC using that artificial synapse. The maximum MC is obtained when the time constant of the artificial synapse and the pulse width of the input pulse are comparable. Typically, the MC is found to be the maximum at a PPF index of 114%. This correlation provides valuable guidelines for the design of artificial synapses for PRC applications, as the performance of PRC can be predicted from the STP parameters of artificial synapses.
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Research context

OpenAlex topics: Neural Networks and Reservoir Computing · Advanced Memory and Neural Computing · Neural dynamics and brain function

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Applied Electronics, Graduate School of Advanced Engineering, Tokyo University of Science , Katsushika, Tokyo 125-8585, Japan

Funding

Japan Science and Technology Agency

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