Fast memory coding method and device based on multi-synaptic plasticity spiking neural network

Abstract

The invention provides a fast memory coding method based on a multi-synaptic plasticity pulse neural network. The fast memory coding method comprises the steps of 1, converting external stimulation into an input pulse sequence based on a hierarchical coding strategy; 2, after the pulse neural network receives an input pulse, updating a membrane potential of neurons of an output layer based on an improved SRM model; 3, updating a synaptic weight input to an output layer by using a supervisory group Tempotron, and activating neuron memory input of the output layer; step 4, after the neurons of the output layer are activated, using the unsupervised STDP to update synaptic weights among the activated neurons in the layer, and forming an enhanced cyclic sub-network storage memory; and step 5, while executing the step 4, using unsupervised inhibition synaptic plasticity, updating a synaptic weight between an inhibition layer and an output layer, and inhibiting separation of distribution time of neural populations with different inputs of feedback guarantee memories. The invention further provides a fast memory coding device based on the multi-synaptic plastic spiking neural network. According to the invention, the coding speed and stability of memory are effectively improved.

Description

technical field [0001] The invention belongs to the field of brain-like intelligence and artificial intelligence, and relates to a fast memory encoding method and device based on a multi-synaptic plastic impulse neural network. Background technique [0002] Spiking Neural Network (SNN) simulates the information processing mechanism of the biological nervous system. It drives calculations through discrete pulse events. Compared with traditional artificial neural networks, it has the advantages of low power consumption and stronger information expression capabilities. Powerful tools for analyzing and simulating the cognitive functions of the brain. As a cognitive ability to memorize, maintain and reproduce experienced things, memory is one of the core components of brain intelligence. [0003] Synaptic plasticity has long been considered the basis of learning and memory, and neuroscience research has shown that the biological brain relies on the synergy of multiple synaptic p...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of low memory encoding efficiency and instability in the current impulse memory model based on unsupervised plasticity existing in the prior art, the present invention proposes a fast memory encoding method and device based on multi-synaptic plasticity impulse neural network, and its specific technology The scheme is as follows:

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