Neural Network Growth Modeling for Simulating Infant Brain Development
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Solution Overview
Problem
Conventional electroencephalogram signal processing apparatuses are unable to simulate the development of an infant's brain.
Innovation Solution
An NN growth apparatus that includes an NN storage unit, start point storage unit, goal storage unit, information acceptance unit, state determination unit, feature acquisition unit, firing node determination unit, and growth unit, which collectively simulate infant brain growth by processing neural network information, sound information, and image information to generate and grow neural network structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional electroencephalogram signal processing apparatuses are used, then electroencephalogram signals can be acquired and processed, but the development of an infant's brain cannot be simulated
Solution Approach 1:
The patent creates a virtual copy of the infant brain's neural network structure using computational models. The system replicates the essential components (nodes representing neurons, edges representing connections) and their dynamic growth patterns without requiring actual biological tissue, thereby enabling simulation of brain development while avoiding the complexity of working with real infant brains.
Solution Approach 2:
The patent replaces the biological mechanical system of actual brain development with a computational information processing system. Neural network growth is simulated through algorithms that process information about acoustic environments and generate corresponding structural changes in the virtual neural network, substituting physical biological processes with information-based computational processes.
2Manufacturing precision
If a detailed neural network growth simulation is implemented, then infant brain development can be accurately simulated, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex neural network growth simulation into distinct functional modules: an information acceptance unit for acquiring acoustic environment data, a state determination unit for processing this information, and a growth unit for generating structural changes. This segmentation allows each component to handle specific aspects of the simulation, improving precision while managing overall system complexity through modular design.
Solution Approach 2:
The system performs preliminary processing of acoustic environment information before generating neural network growth patterns. The state determination unit pre-processes acoustic data into meaningful states that guide subsequent growth decisions, allowing the growth unit to operate with pre-prepared information rather than raw data, thereby improving precision without proportionally increasing complexity.
Data Source
AI summary
An NN growth apparatus includes: a start point storage in which pieces of firing start point information are stored, each piece of firing start point information containing an information identifier of feature information of image information and node identifiers; a goal storage in which pieces of goal information are stored, the pieces of goal information specifying goals respectively corresponding to states; a state determination unit that determines one state, using the sound information; a feature acquisition unit that acquires feature information from the image information; a firing node determination unit that determines node identifiers corresponding to the pieces of feature information from the start point storage unit, and determines a node identifier of a firing node connected to the nodes; and a growth unit that acquires the goal information paired with the one state, and performs processing to grow node information or edge information corresponding to node identifiers.