Unit, device and method for simulating biological neuronal synapsis

Abstract

Disclosed are a unit, device and method for simulating a neuronal synapse of a biont based on a chalcogenide compound. The unit comprises a first electrode layer, a functional material layer and a second electrode layer. The first electrode layer receives a first pulse signal, and the second electrode layer receives a second pulse signal. A device can change the conductance thereof according to an input signal, so as to simulate the change of synapse weighting. When the difference value between the frequency of the first pulse signal and that of the second pulse signal is positive or negative, the change of conductance realizes the simulation of the function of spike frequency-dependent synapse plasticity of the neuronal synapse of the biont; and when the peak value of the signal difference between the first pulse signal and the second pulse signal is positive or negative, the change of the conductance realizes the simulation of the function of spike timing-dependent synapse plasticity of the neuronal synapse of the biont. The present invention can achieve the basic function of the neuronal synapse of the biont on a single inorganic device, and provide basic components forming an artificial neural network, and can achieve the effects of increasing the integration degree and reducing the power consumption.

Description

technical field [0001] The invention belongs to the field of microelectronic devices, and more specifically relates to a unit, device and method for simulating biological synapses. Background technique [0002] In a traditional computer based on the von Neumann architecture, the processor and the memory are separated and connected by a bus. Such a structure has the so-called "von Neumann bottleneck", which makes it difficult to adapt to the current era of rapid development of information technology with explosive growth of information. [0003] Compared with von Neumann computers, the neural information activities of the human brain have the characteristics of large-scale parallelism, distributed storage and processing, self-organization, self-adaptation and self-learning. Researchers in the fields of traditional artificial neural networks and neuromorphic engineering have also been working on using nonlinear circuits, FPGA, VLSI and other means to simulate the basic bioele...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the defects of the prior art, the purpose of the present invention is to provide a unit for simulating biological synapses, aiming to solve the problem that only multiple components can realize the function of a synapse

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