Memcapacitive Spiking Neurons and Associative Memory Application

被引:0
|
作者
Dat Tran, S. J. [1 ]
机构
[1] Santa Clara Univ, Dept Elect & Comp Engn, Santa Clara, CA 95053 USA
来源
IEEE ACCESS | 2025年 / 13卷
关键词
Neurons; Firing; Mathematical models; RLC circuits; Integrated circuit modeling; Biological system modeling; Membrane potentials; Computational modeling; Brain modeling; Energy efficiency; Memcapacitive spiking neuron; memcapacitor; Izhikevich spiking neuron; spiking neuron circuit; pulse-coupled network; associative memory; MODEL; RESONANCE;
D O I
10.1109/ACCESS.2025.3549357
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Hodgkin and Huxley neuron model describes the complex behavior of biological neurons. However, due to the complexity of these computations, the Hodgkin and Huxley models are impractical for use in large-scale networks. In contrast, Izhikevich introduced a simpler model capable of producing various firing patterns typical of cortical neurons. This study proposes a novel model of memcapacitive-based neurons that offers a potential implementation of spiking neurons with energy efficiency due to the inherent storage nature of memcapacitive devices. The findings demonstrate that memcapacitive neurons can produce 23 firing patterns similar to Izhikevich neurons but at significantly higher firing rates. Memcapacitive neurons exhibit firing patterns associated with excitatory, inhibitory, and thalamocortical neurons. Similar to Izhikevich neurons, pulse-coupled neural networks of memcapacitive neurons display collective behaviors, such as synchronous and asynchronous responses, which are common in the biological brain. Compared to Hopfield and Izhikevich networks in content-addressable memory applications, memcapacitive networks successfully retrieved correct memory patterns with high accuracy, even for distorted inputs of up to 40%. The simulation results illustrate that the novel model of the memcapacitive spiking neuron offers a potential advancement in implementing artificial spiking neurons with high energy efficiency, bringing a step closer to mimicking biological neurons.
引用
收藏
页码:43933 / 43946
页数:14
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