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A method and system for neuron membrane potential transfer pulse sequence

A technology of pulse sequence and membrane potential, applied in the field of neural network control, can solve problems such as inaccurate results and achieve the effect of improving accuracy

Active Publication Date: 2022-07-05
中科南京智能技术研究院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

(3) Error measurement caused by conversion
Second, the results obtained by manual intervention are not accurate enough

Method used

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  • A method and system for neuron membrane potential transfer pulse sequence
  • A method and system for neuron membrane potential transfer pulse sequence
  • A method and system for neuron membrane potential transfer pulse sequence

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Embodiment Construction

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] The purpose of the present invention is to provide a method and system for converting the neuron membrane potential into a pulse sequence, which improves the accuracy of converting the membrane potential of a biological neuron model into a pulse sequence.

[0059] In order to make the above objects, features and advantages of the present invention more clearly understood, the present invention will be described in further detail...

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Abstract

The invention relates to a method and a system for converting neuron membrane potential into a pulse sequence, belonging to the field of neural network control. The pulse sequence and the second membrane potential data determine the hard threshold, and according to the hard threshold, the membrane potential data of the biological neuron model is converted into the second pulse sequence; the first pulse sequence is converted into the first pulse moment set represented by the time, and the The second pulse sequence is converted into a second pulse time set represented by time; the maximum error tolerance of the elements in the first pulse time set and the elements in the second pulse time set is determined according to the first pulse time set and the second pulse time set; Based on the first set of pulse times and the second set of pulse times, a pulse sequence of the biological neuron model with maximum error tolerance is determined. The invention improves the accuracy of converting the membrane potential of the biological neuron model into a pulse sequence.

Description

technical field [0001] The invention relates to the technical field of neural network control, in particular to a method and a system for converting the neuron membrane potential to a pulse sequence. Background technique [0002] In recent years, the development of brain-like computing, brain science, and deep learning has been in full swing, and neurons as the most basic unit have received close attention. People hope to study related topics such as intelligence and consciousness by studying the mechanism of biological neurons and simulating the network structure of the brain. It is a complex process for neurons to receive, process, and transmit external stimulus signals. Simulation and modeling of them can not only help understand the operating mechanism of biological nerve cells, but also help develop more biologically interpretable neural cells. Artificial Neural Network Algorithm. [0003] figure 2 Demonstrates the mechanism by which biological neurons receive extern...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/04G06N3/06A61B5/388
CPCG06N3/049G06N3/061A61B5/388
Inventor 曾一诺尚德龙周玉梅
Owner 中科南京智能技术研究院
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