Modulating synaptic plasticity with metal−organic framework for information-filterable artificial retina

Seongchan Kim, Ohchan Kwon, Seonkwon Kim, Seonmin Jang, Seungho Yu, Choong Hoo Lee, Yoon Young Choi, Soo Young Cho, Ki Chul Kim, Cunjiang Yu (), Dae Woo Kim () and Jeong Ho Cho ()
Additional contact information
Seongchan Kim: University of Pennsylvania
Ohchan Kwon: Yonsei University
Seonkwon Kim: Yonsei University
Seonmin Jang: The Pennsylvania State University, University Park
Seungho Yu: Konkuk University
Choong Hoo Lee: Yonsei University
Yoon Young Choi: University of Illinois, Urbana-Champaign
Soo Young Cho: Yonsei University
Ki Chul Kim: Konkuk University
Cunjiang Yu: University of Illinois, Urbana-Champaign
Dae Woo Kim: Yonsei University
Jeong Ho Cho: Yonsei University

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract Neuroprosthetics equipped with artificial synapses hold promise to address some most intricate medical problems, such as human sensory disorders. Yet, it is necessitated and of paramount importance for neuroprosthetics to be able to differentiate significant and insignificant signals. Here, we present an information-filterable artificial retina system that integrates artificial synapses with a signal-integration device for signal perception and processing with attention. The synaptic weight modulation is rendered through metal–organic framework (MOF) layers, where distinct short-term and long-term properties are predominantly determined by MOF’s pore diameter and functionality. Specifically, four types of isoreticular Zr-based MOFs that share Zr6O4(OH)4 secondary building units have been systematically examined. It is demonstrated that small pore diameters enhance short-term properties, while large pores, which are characterized by increased ion affinity, sustain long-term properties. Moreover, we demonstrated a 6 × 6 pixel artificial retina by incorporating both short-term and long-term artificial synapses with a signal-integration device. Signal summation by the signal-integration device enables attention-based information processing. The information-filterable artificial retina system developed here emulates human perception processes and holds promise in the fields of neuroprosthetics and advanced artificial intelligence.

Date: 2025
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DOI: 10.1038/s41467-024-55173-2

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