Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Human brain language cognition modeling method

A modeling method and cognitive model technology, applied in the field of human brain language cognition technology, can solve problems such as wrong cognitive results, unsatisfactory statistical cognitive modeling methods, and lack of statistical significance

Inactive Publication Date: 2014-01-22
DALIAN MARITIME UNIVERSITY
View PDF4 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In fact, the observed cognitive activation features are event-related, and the cognitive state has the characteristics of superposition and small data samples, which do not have strict statistical significance and do not meet the prerequisites of statistical cognitive modeling methods. It is easy to lead to erroneous cognitive results

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Human brain language cognition modeling method
  • Human brain language cognition modeling method
  • Human brain language cognition modeling method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] The present invention will be further described below in conjunction with the accompanying drawings. Such as Figure 1-2 As shown, the model method of the present invention includes triple time series, that is: upper-level stimulus time series Ξ, middle-level hidden state cognitive time series I, and observation time series S. There is a hidden state cognitive superposition in the observation sequence, that is, middle-level hidden state When the cognitive sequence I is mapped to the observation sequence S, there are multiple arrows pointing to the same mapping area, but this triple time series has a basic mapping relationship, and this relationship is determined by the probability parameter Φ, through steps D1-D5 according to the formula ( 6) Analyzing the probability mapping relationship determined by its probability parameters. Give a simple example: the input stimulus time series Ξ is {Δ 1 ,Δ 2 ,Δ 3}; Observation time series S is {s 1 ,s 2 ,s 3}; Implicit cogn...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a human brain language cognition modeling method. The human brain language cognition modeling method comprises the following steps of initialization of a cognitive state example, mapping of probability distribution between activation characteristics and observation data, definition of a brain tacit cognition model and parameter analysis of the tacit cognition model. In the cognition modeling process, input stimulation, the observation result and the tacit cognition state are defined as triple time sequences related to a dynamic event, namely a cognition stimulation task time sequence, an observation characteristic time sequence and a tacit cognition state time sequence, the triple time sequences are related to one another through a set of probability distribution, and not all collected brain data are treated as static information for statistics. Therefore, the human brain language cognition modeling method does not need to meet the basic assumption based on statistics, is established under a small sample data condition, and guarantees the correctness of the cognition analysis result, thereby achieving cognition modeling under the small sample data condition. The human brain language cognition modeling method improves the accuracy of cognition modeling, and provides an effective approach for complex cognition analysis.

Description

technical field [0001] The invention relates to a human brain language cognition technology, in particular to a human brain language cognition modeling method. Background technique [0002] The most direct reflection of cognition in the human brain is the degree of activation or inhibition of brain regions. From the perspective of neuroinformatics, the key to the study of language cognition is to study the state of brain regions in the process of language cognition in the human brain and the process of these state migrations. , one of the most important methods is to design experiments according to the requirements of neuroinformatics, stimulate the cognitive functions of the subjects (mostly volunteers from a specific group), and collect functional data, and then use certain modeling techniques Analyze these data. Brain imaging techniques developed in recent years (positron emission tomography, functional nuclear magnetic resonance) provide means for people to directly obs...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 刘洪波冯士刚鲁明羽
Owner DALIAN MARITIME UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com