The Storage and Calculation of Biological-like Neural Networks for Locally Active Memristor Circuits
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摘要: 生物神经系统在低功耗计算、动态存储方面具有显著的优势，这与神经元通过定向分泌递质来传递神经信号的工作机制密切相关。神经信号的产生涉及刺激信号的放大和运算，其工作机制可以利用忆阻器容控混沌振荡器实现。本文利用局部有源忆阻器随外接电容改变形成稳定的倍周期分叉的电压信号振荡，获得了电路中电容与忆阻器两端电压信号之间稳定的映射关系，电路中电容的改变使得忆阻器两端串行输出不同形态的电信号，其电压幅值稳定的周期改变。使得改变的电容与输出的电压信号之间形成稳定的多状态映射关系，从而构成存算单元。结合蔡氏结型忆阻器模型建立了三阶忆阻器电路，当忆阻器工作在局部有源区其三阶电路构成的振荡器能够同时完成信号放大、运算和存储。Abstract:
Objective At present, binaryBinary computing systems have encounteredreached 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 indemonstrates remarkable low-power computing and dynamic storage capability, which is closely relatedattributed to the working mechanism of neurons transmitting neural signals through the directional secretion of neurotransmitters. After analyzing the Hodgkin-–Huxley model of the 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 , both exhibiting electrical characteristics with similar to those of nerve fibers. Since the first experimental claim of memristors was claimed to be found, locally active memristive devices have been identified in the research of devices with layered structures. The, and circuits constructed from thosethese devices exhibit differentdisplay various types of neuromorphic _dynamics under different excitations,. However, ano single two-terminal device capable of achieving multi-state storage has not yet been reported. Locally active memristors have advantages inare advantageous for generating biologically -inspired neural signals. Various forms of locally active memristor, as their models can produce neural morphological signals based on spike pulses. The generation of neuralsuch signals involvesis achieved through the amplification and computation of stimulus signals, and its working mechanism can beexternal stimuli, a process realized using capacitance-controlled memristor oscillators. When a memristor operates in the locally active domiandomain, 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 localwork, a locally active memristor-based third-order circuitiscircuit is used as a unit to generate neuromorphic signals, thereby forming a biologically -inspired neural operation unit, and an. An operation network can be formed based on the operation unitconstructed from such units, providing a framework for storage and computation in biological-like neural networks. Methods The mathematical model of the Chua Corsage Memristor proposed by Leon Chua wasis selected for analysis. The characteristics of the partial locallocally active domain wereare examined, and an appropriate operating point and together with external components were chosenis determined to establish a third-order memristor chaotic circuit. Circuit simulation and analysis were then conductedare performed 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 ofperforms signal amplification, computation, and storage. In this wayconfiguration, the third-order circuit can be performis regarded as thea nerve cell, and the variable capacitors are treated as cynapses. Enables the synapses. The electrical signal and the dielectric capacitor to workoperate in succession, allowingenabling the third-order oscillation circuit of the memristor to functionbehave like a neuron, withwhere alternating electricalelectric fields and neurotransmitters formingcapacitive dynamics mimic neurotransmitter-mediated processes to form a brain-like computing and storage system. The secretion of biological neurotransmitters hasis characterized by a threshold characteristic, and , with the membrane threshold voltage controlsregulating the secretionrelease of neurotransmitters to the postsynaptic membrane, thereby forming the transmission oftransmitting neural signals. The Analogously, the step peak value of the oscillation circuit can servefunctions as the trigger voltage for the transfer of the capacity electrolytecapacitive charge. Results and Discussions This study utilizes the third-order circuit of a locallocally active memristor to generate stable voltage oscillations exhibiting period-doubling bifurcation voltage signal oscillations as the external capacitance changes. The variation of varies. Changes in capacitance in the circuit causes different forms of electrical signals lead to be seriallydifferent forms of electrical signals being sequentially output at the memristor terminals of the memristor, and, with the voltage amplitude of these signals changes stably in a exhibiting stable periodic mannervariation. This results inestablishes a stable multi-state mapping relationship between the changed capacitance values and the output voltage signalsignals, thereby forming the basis of a storage and computing unit, and, subsequently, a storage and computing network. Currently, At present, it remains necessary to develop a structure that enablesallows the dielectric to transfer and change theadjust capacitance value to the next stage under the control of thea modulated voltage threshold needs to be realized. It is similar, analogous to the function of neurotransmitter secretion. The in biological systems. These results demonstrate the feasibility of using the third-order oscillation circuit of thea memristor as a storage and computing unit is expounded, and and highlight the potential for constructing a storage and computing structurearchitecture based on the change of capacitance value is obtainedvariation. 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 the external capacitance changesvaries. The seriallysequentially output oscillating signals exhibitdisplay stable voltage amplitudes/and periods and haswith threshold characteristics. The change of the Variations in capacitance in the circuit causesinduce different forms of electrical signals to be serially output at the memristor terminals of the memristor, and the, with voltage amplitude of these signals changes stably in a periodic manner.amplitudes changing periodically and stably. This results inestablishes a stable multi-state mapping relationship between the changed capacitance values and the output voltage signalvoltages, thereby forming a storage and computing unit, and subsequently, by extension, a storage and computing network. Currently, aA structure is need to realize the transfer of thethat enables dielectric transfer and capacitance adjustment to the subordinatenext stage under the control of thea modulated voltage threshold, similar to the function of neurotransmitter secretion., still needs to be developed. The findings demonstrate the feasibility of using the third-order oscillation circuit of thea memristor as a storage and computing unit is obtained, and describe a potential storage and computing structurearchitecture based on the variation of capacitance value is describedvariation. -
Key words:
- Locally /
- Active memristor /
- Chaos /
- Neural network
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表 1 忆阻器三阶电路振荡输出与电容值之间的映射关系。
忆阻器振荡V-I图 C1=0.5 μF C1=2.0 μF C1=4.0 μF C2=0.5 μF 
C2=2.0 μF 
C2=4.0 μF 
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