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A Fast Prediction Method for Neuronal Action Potential Sequences in the Brain

A technology of action potential and prediction method, applied in the field of brain simulation, which can solve problems such as low accuracy and slow speed

Active Publication Date: 2018-12-25
COMMUNICATION UNIVERSITY OF CHINA
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  • Description
  • Claims
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is the problem of slow speed and low accuracy in simulating a large-scale neural network

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  • A Fast Prediction Method for Neuronal Action Potential Sequences in the Brain
  • A Fast Prediction Method for Neuronal Action Potential Sequences in the Brain
  • A Fast Prediction Method for Neuronal Action Potential Sequences in the Brain

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Embodiment Construction

[0027] The technical solutions of the present invention will be described in detail below in conjunction with the drawings and specific embodiments of the present invention.

[0028] V in the present invention max ,V min and Dt respectively represent the highest value, lowest value and width of a single spike (the difference between the highest value and the lowest value corresponding time, that is: Dt=t min -t max ), V 1 ,V 2 and V 3 Represents three consecutive voltage values ​​with an interval of 1ms. Similarly, VV 1 、VV 2 、VV 3 and V t1 , V t2 , V t3 Also represent three consecutive voltage values ​​with an interval of 1 ms, respectively.

[0029] figure 1 It is a simplified block diagram for a fast prediction method of brain neuron action potential sequence, in which 10 represents the sample collection of the action potential sequence of a given neuron; 20 represents building a prediction module network based on the collected sample signal, and training and T...

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Abstract

The invention discloses a quick prediction method of a cerebral neuron action potential sequence, relates to the field of cerebral simulation, and especially aims at the accurate and quick calculation of the cerebral neuron action potential sequence in large-scale cerebral simulation. Two modules are introduced, wherein one module is used for classification, and the other module is used for prediction. Then, a great quantity of sample data is collected to train two modules, and finally, characteristics corresponding to spike can be quickly and accurately predicted only on the basis of few non-spike membrane potential values which mutually have a large interval. The quick prediction method aims to solve the technical problem in the prior art that the calculation speed of the cerebral neuron action potential is low in a large-scale cerebral simulation process. By use of the method disclosed by the invention, the calculation speed of the action potential can be greatly improved, meanwhile, fairly high accuracy is kept, and the quick prediction method is very suitable for the simulation of the large-scale cerebral neuron network.

Description

technical field [0001] The invention relates to the field of brain simulation, in particular to the calculation problem of neuron action potential sequence in large-scale brain simulation. Background technique [0002] The brain is composed of hundreds of millions of neurons, which constitute the basic unit of neural information processing, and the discharge activity exhibited by neurons represents the way the neurons encode external stimuli. How to accurately simulate these electrical discharges is a key step in brain simulation. In addition, the number of neurons in the brain is very large, and neurons will form functional groups through complex synaptic connections, forming an organic part of the brain. In brain simulation, in order to approach the real brain area, it is usually necessary to ensure that the number of neurons in the network has a certain scale. The expansion of the number brings the need for simulation speed, and there is an urgent need for a fast and acc...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/06G06N3/08
CPCG06N3/061G06N3/084
Inventor 曹立宏汪雷沈佳敏
Owner COMMUNICATION UNIVERSITY OF CHINA
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