The Storage and Calculation of Biological-like Neural Networks for Locally Active Memristor Circuits

LI Fupeng; WANG Guangyi; LIU Jingbiao; YING Jiajie

doi:10.11999/JEIT250631

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LI Fupeng, WANG Guangyi, LIU Jingbiao, YING Jiajie. The Storage and Calculation of Biological-like Neural Networks for Locally Active Memristor Circuits[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250631

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LI Fupeng, WANG Guangyi, LIU Jingbiao, YING Jiajie. The Storage and Calculation of Biological-like Neural Networks for Locally Active Memristor Circuits[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250631

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The Storage and Calculation of Biological-like Neural Networks for Locally Active Memristor Circuits

doi: 10.11999/JEIT250631 cstr: 32379.14.JEIT250631

1.
School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
2.
School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing 210016, China

Funds: The National Natural Science Foundation of China(61771176, 61801154)

Received Date: 2025-07-04
Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-12

Abstract

Abstract

Objective At present, binary computing systems have encountered bottlenecks in terms of power consumption, operation speed and storage capacity. In contrast, the biological nervous system seems to have unlimited capacity. The biological nervous system has significant advantages in low-power computing and dynamic storage capability, which is closely related to the working mechanism of neurons transmitting neural signals through directional secretion of neurotransmitters. After analyzing the Hodgkin-Huxley model of squid giant axon, Professor Leon Chua proposed that synapses could be composed of locally passive memristors, and neurons could be made up of locally active memristors. The two types of memristors share similar electrical characteristics with nerve fibers. Since the memristors was claimed to be found, locally active memristive devices have been identified in the research of devices with layered structures. The circuits constructed from those devices exhibit different types of neuromorphic_dynamics under different excitations, However, a single two-terminal device capable of achieving multi-state storage has not yet been reported. Locally active memristors have advantages in generating biologically-inspired neural signals. Various forms of locally active memristor models can produce neural morphological signals based on spike pulses. The generation of neural signals involves the amplification and computation of stimulus signals, and its working mechanism can be realized using capacitance-controlled memristor oscillators. When a memristor operates in the locally active domian, the output voltage of its third-order circuit undergoes a period-doubling bifurcation as the capacitance in the circuit changes regularly, forming a multi-state mapping between capacitance values and oscillating voltages. In this paper, the local active memristor-based third-order circuitis used as a unit to generate neuromorphic signals, thereby forming a biologically-inspired neural operation unit, and an operation network can be formed based on the operation unit. Methods The mathematical model of the Chua Corsage Memristor proposed by Leon Chua was selected for analysis. The characteristics of the partial local active domain were examined, and an appropriate operating point and external components were chosen to establish a third-order memristor chaotic circuit. Circuit simulation and analysis were then conducted on this circuit. When the memristor operates in the locally active domain, the oscillator formed by its third-order circuit can simultaneously perform the functions of signal amplification, computation, and storage. In this way, the third-order circuit can be perform as the nerve cell and the variable capacitors as cynapses. Enables the electrical signal and the dielectric capacitor to work in succession, allowing the third-order oscillation circuit of the memristor to function like a neuron, with alternating electrical fields and neurotransmitters forming a brain-like computing and storage system. The secretion of biological neurotransmitters has a threshold characteristic, and the membrane threshold voltage controls the secretion of neurotransmitters to the postsynaptic membrane, thereby forming the transmission of neural signals. The step peak value of the oscillation circuit can serve as the trigger voltage for the transfer of the capacity electrolyte. Results and Discussions This study utilizes the third-order circuit of a local active memristor to generate stable period-doubling bifurcation voltage signal oscillations as the external capacitance changes. The variation of capacitance in the circuit causes different forms of electrical signals to be serially output at the terminals of the memristor, and the voltage amplitude of these signals changes stably in a periodic manner. This results in a stable multi-state mapping relationship between the changed capacitance and the output voltage signal, thereby forming a storage and computing unit, and subsequently a storage and computing network. Currently, a structure that enables the dielectric to transfer and change the capacitance value to the next stage under the control of the modulated voltage threshold needs to be realized. It is similar to the function of neurotransmitter secretion. The feasibility of using the third-order oscillation circuit of the memristor as a storage and computing unit is expounded, and a storage and computing structure based on the change of capacitance value is obtained. Conclusions When the Chua Corsage Memristor operates in its locally active domain, its third order circuit–powered solely by a voltage-stabilized source generates stable period-doubling bifurcation oscillations as external capacitance changes. The serially output oscillating signals exhibit stable voltage amplitudes/periods and has threshold characteristics. The change of the capacitance in the circuit causes different forms of electrical signals to be serially output at the terminals of the memristor, and the voltage amplitude of these signals changes stably in a periodic manner. This results in a stable multi-state mapping relationship between the changed capacitance and the output voltage signal, thereby forming a storage and computing unit, and subsequently a storage and computing network. Currently, a structure is need to realize the transfer of the dielectric to the subordinate under the control of the modulated voltage threshold, similar to the function of neurotransmitter secretion. The feasibility of using the third-order oscillation circuit of the memristor as a storage and computing unit is obtained, and a storage and computing structure based on the variation of capacitance value is described.
- Locally,
- active memristor,
- Chaos,
- Neural network

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References

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