Resistance-adjustable-memristor-based time-related learning neuron circuit and implementation method thereof

Abstract

The invention discloses a resistance-adjustable-memristor-based time-related learning neuron circuit and an implementation method thereof. According to the invention, switching characteristics of a resistance-adjustable memristor are utilized, when two ends of the memristor are synchronously selected by two excitation signals, voltage drop capable of enabling the memristor to generate resistance adjustment is formed at the two ends of a device, the connection or disconnection of a synapse is realized, the correlation or noncorrelation of the two excitation signals is realized, a memory characteristic is realized, and the previous excitation signals can be repeated, so a learning goal is achieved. The resistance-adjustable memristor is simple in structure, and high in integration level, so that the connection of large-scale physical neuron synapses is realized so as to achieve a more complicated learning or even logic function. The neuron circuit and the implementation method thereof disclosed by the invention have a good application prospect in the neuron calculation.

Description

technical field [0001] The invention relates to a neuron cell circuit, in particular to a time-associated learning neuron circuit based on a resistive memristor and an implementation method thereof. Background technique [0002] Digital computer is an important product of the progress of human science and technology civilization in the 20th century, and its influence penetrates into every aspect of people's life. However, with the development of the computer industry and the progress of the microelectronics industry, people can no longer be satisfied with the functions of existing computers. High computing speed, large storage capacity, and intelligence have become an inevitable trend for the further development of computers. Due to its characteristics of large-scale parallel processing, strong recognition ability, ability to process analog information, and machine self-learning, neural computers will become a powerful substitute for digital computers in the future. The key ...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, the calculation function of the digital computer has been completed in the design stage, but after the computer design is completed, the computer only reproduces the logic that has been set, and does not have the ability to learn independently, and does not have a real learning function

Images