Metal–Organic\nFramework Nanofluidic Synapse

Chemical\nsynapse completes the signaling through neurotransmitter-mediated\nion flux, the emulation of which has been a long-standing obstacle\nin neuromorphic exploration. Here, we report metal–organic\nframework (MOF) nanofluidic synapses in which conjugated MOFs with\nabundant ionic storage sites underlie the ionic hysteresis and simultaneously\nserve as catalase mimetics that sensitively respond to neurotransmitter\nglutamate (Glu). Various neurosynaptic patterns with adaptable weights\nare realized via Glu-mediated chemical/ionic coupling. In particular,\nnonlinear Hebbian and anti-Hebbian learning in millisecond time ranges\nare achieved, akin to those of chemical synapses. Reversible biochemical\nin-memory encoding via enzymatic Glu clearance is also accomplished.\nSuch results are prerequisites for highly bionic electrolytic computers.

Publication Details

Journal
Figshare
Published
2024-09-18
DOI
https://doi.org/10.1021/jacs.4c08833.s001
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

Metal–Organic\nFramework Nanofluidic Synapse

Figshare
Advanced Memory and Neural Computing
article

Metal–Organic\nFramework Nanofluidic Synapse

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Abstract

Chemical\nsynapse completes the signaling through neurotransmitter-mediated\nion flux, the emulation of which has been a long-standing obstacle\nin neuromorphic exploration. Here, we report metal–organic\nframework (MOF) nanofluidic synapses in which conjugated MOFs with\nabundant ionic storage sites underlie the ionic hysteresis and simultaneously\nserve as catalase mimetics that sensitively respond to neurotransmitter\nglutamate (Glu). Various neurosynaptic patterns with adaptable weights\nare realized via Glu-mediated chemical/ionic coupling. In particular,\nnonlinear Hebbian and anti-Hebbian learning in millisecond time ranges\nare achieved, akin to those of chemical synapses. Reversible biochemical\nin-memory encoding via enzymatic Glu clearance is also accomplished.\nSuch results are prerequisites for highly bionic electrolytic computers.

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Openalex Percentile: Top 67%
Advanced Memory and Neural Computing
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