posted on 2024-03-05, 15:06authored byShanwu Ke, Yanqin Pan, Yaoyao Jin, Jiahao Meng, Yongyue Xiao, Siqi Chen, Zihao Zhang, Ruiqi Li, Fangjiu Tong, Bei Jiang, Zhitang Song, Min Zhu, Cong Ye
Benefiting
from the brain-inspired event-driven feature and asynchronous
sparse coding approach, spiking neural networks (SNNs) are becoming
a potentially energy-efficient replacement for conventional artificial
neural networks. However, neuromorphic devices used to construct SNNs
persistently result in considerable energy consumption owing to the
absence of sufficient biological parallels. Drawing inspiration from
the transport nature of Na+ and K+ in synapses,
here, a Li-based memristor (LixAlOy) was proposed to emulate the biological
synapse, leveraging the similarity of Li as a homologous main group
element to Na and K. The Li-based memristor exhibits ∼8 ns
ultrafast operating speed, 1.91 and 0.72 linearity conductance modulation,
and reproducible switching behavior, enabled by lithium vacancies
forming a conductive filament mechanism. Moreover, these memristors
are capable of simulating fundamental behaviors of a biological synapse,
including long-term potentiation and long-term depression behaviors.
Most importantly, a threshold-tunable leaky integrate-and-fire (TT-LIF)
neuron is built using LixAlOy memristors, successfully integrating synaptic signals
from both temporal and spatial levels and achieving an optimal threshold
of SNNs. A computationally efficient TT-LIF-based SNN algorithm is
also implemented for image recognition schemes, featuring a high recognition
rate of 90.1% and an ultralow firing rate of 0.335%, which is 4 times
lower than those of other memristor-based SNNs. Our studies reveal
the ion dynamics mechanism of the LixAlOy memristor and confirm its potential in rapid
switching and the construction of SNNs.