A memristor-based neuronal circuit with homeostatic plasticity

Abstract

The invention discloses a memristor-based neuron circuit with homeostatic plasticity. The neuron circuit comprises an excitation module, a pulse generation module and a feedback module; the signal input end of the excitation module receives an input pulse generated by a front neuron and is used for outputting an excitation pulse according to the input pulse; the input end of the pulse generation module is connected to the output end of the excitation module and used for generating a corresponding exciting pulse according to triggering of the excitation pulse; the input end of the feedback module is connected to the second output end of the pulse generation module, the output end of the feedback module is connected to the feedback input end of the excitation module, and the feedback moduleis used for comparing the frequency of the exciting pulse with the inherent pulse frequency of the neuron and outputting a corresponding feedback voltage according to a comparison result. By means ofthe circuit, a negative feedback mechanism, namely homeostatic plasticity, in a biological neural system can be realized, the exciting frequency of the neuron can be adjusted in a self-adaptive mode,and the exciting frequency is kept at the inherent exciting frequency of the neuron.

Description

technical field [0001] The invention belongs to the emerging application field of circuit technology, and more specifically relates to a memristor-based neuron circuit with steady-state plasticity. Background technique [0002] In the biological nervous system, neurons and synapses are connected to each other and transmit information. Neural activity can enhance or weaken synaptic connections between neurons through long-term potentiation (LTP) and long-term depression (LTD), however, the continuous effect of this positive feedback regulation mechanism may lead to excessive neural networks. Excitement or overinhibition. In order to avoid this situation, there is another negative feedback regulation mechanism complementary to LTP and LTD in the nervous system, that is, homeostatic plasticity. Homeostatic plasticity has two manifestations, in synapses and neurons, respectively. The steady-state plasticity existing in neurons can adaptively adjust the excitatory frequency of...

AI Technical Summary

Problems solved by technology

[0003] In the field of neuromorphic computing research, the realization of biological neural activity through circuit devices or systems is an important research direction. However, most of the current hardware implementations for the plasticity of biological nervous systems are aimed at the study of steady-state plasticity in biological synapses. , there is little research on the homeostatic plasticity that exists in neurons

Most of the existing neurons with steady-state plasticity are integrated by traditional CMOS that are too large to be integrated on a large scale.

In order to achieve the steady-state plasticity of neurons, in related research, some scholars have implemented the steady-state plasticity rules of neurons in the form of software codes, and the pattern recognition accuracy of the neural network based on this rule has been significantly improved. However, The implementation of the software code is based on the serial working mode, the operation speed is slow, and the excitation frequency of the neurons cannot be adjusted in real time. Therefore, a small-sized, large-scale integrated, and parallel-adjustable neuron can be studied. Metacircuits are of great significance for neuromorphic computing and further research on brain-inspired intelligence

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