[15a-M_123-1~13] FS.1 Focused Session "AI Electronics"

12:00 PM - 12:15 PM JST(3:00 AM - 3:15 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[15a-M_123-12] Synaptic Learning and Neuron-Like Leaky Integrate-and-Fire in Quasi-1D Halogen-Bridged Metal Complexes

〇(M1)Tetsuro Moriya¹, Shinya Takaishi², Taishi Takenobu¹, Yugo Oshima³ (1.Nagoya Univ., 2.Tohoku Univ., 3.RIKEN)

Keywords:

memristor,Organic crystal

Neuromorphic computing has been advanced through architecture-oriented approaches typified by physical reservoir computing. From the viewpoint of pushing energy efficiency to the limit, however, device-oriented elements that enable the close integration of computation and memory (in-memory/near-memory) are also essential. In this study, we investigated synapse-like learning rules and neuron-like leaky integrate-and-fire behavior in the quasi-one-dimensional halogen-bridged metal complex [Ni(chxn)₂Br]Br₂, in which memristor operation has previously been confirmed.