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Multi-neuron connection using multi-terminal floating–gate memristor for unsupervised learning

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Title
Multi-neuron connection using multi-terminal floating–gate memristor for unsupervised learning
Author(s)
Won, Ui Yeon; Quoc An Vu; Park, Sung Bum; Park, Mi Hyang; Dam Do, Van; Park, Hyun Jun; Yang, Heejun; Young Hee Lee; Yu, Woo Jong
Publication Date
2023-05
Journal
Nature Communications, v.14, no.1
Publisher
Nature Research
Abstract
Multi-terminal memristor and memtransistor (MT-MEMs) has successfully performed complex functions of heterosynaptic plasticity in synapse. However, theses MT-MEMs lack the ability to emulate membrane potential of neuron in multiple neuronal connections. Here, we demonstrate multi-neuron connection using a multi-terminal floating-gate memristor (MT-FGMEM). The variable Fermi level (E F) in graphene allows charging and discharging of MT-FGMEM using horizontally distant multiple electrodes. Our MT-FGMEM demonstrates high on/off ratio over 105 at 1000 s retention about ~10,000 times higher than other MT-MEMs. The linear behavior between current (I D) and floating gate potential (V FG) in triode region of MT-FGMEM allows for accurate spike integration at the neuron membrane. The MT-FGMEM fully mimics the temporal and spatial summation of multi-neuron connections based on leaky-integrate-and-fire (LIF) functionality. Our artificial neuron (150 pJ) significantly reduces the energy consumption by 100,000 times compared to conventional neurons based on silicon integrated circuits (11.7 μJ). By integrating neurons and synapses using MT-FGMEMs, a spiking neurosynaptic training and classification of directional lines functioned in visual area one (V1) is successfully emulated based on neuron’s LIF and synapse’s spike-timing-dependent plasticity (STDP) functions. Simulation of unsupervised learning based on our artificial neuron and synapse achieves a learning accuracy of 83.08% on the unlabeled MNIST handwritten dataset. © 2023, The Author(s).
URI
https://pr.ibs.re.kr/handle/8788114/13553
DOI
10.1038/s41467-023-38667-3
ISSN
2041-1723
Appears in Collections:
Center for Integrated Nanostructure Physics(나노구조물리 연구단) > 1. Journal Papers (저널논문)
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