Neural Population Coding Data Processing for Training Efficiency
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Solution Overview
Problem
Conventional supervised learning models face challenges with low training efficiency and poor robustness, particularly in handling large datasets and requiring numerous training examples.
Innovation Solution
A data processing method based on neural population coding that employs common spatial pattern transformation and gradient descent update rules to optimize weight parameters in a neural population coding network model, enhancing learning efficiency and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional supervised learning methods (SVM, BP) are used, then the model can learn from labeled data, but training efficiency is low and robustness is poor
Solution Approach 1:
The patent transforms the raw data using a transformation matrix obtained through common spatial pattern analysis, changing the parameter representation of the data. This transformation optimizes the data structure to enhance both training efficiency and model robustness simultaneously, addressing the technical contradiction by modifying how data is represented rather than changing the learning algorithm itself.
2Measurement precision
If a large number of training examples are used, then learning accuracy improves, but training time and computational cost increase
Solution Approach 1:
The patent extracts the most discriminative features from the training data through common spatial pattern transformation, obtaining a transformation matrix that captures essential patterns. This extraction process reduces the effective dimensionality and information redundancy, allowing the model to achieve high learning accuracy with fewer training examples and reduced training time.
3Reliability
If raw data is processed directly without transformation, then computation is simpler, but noise interference reduces model performance
Solution Approach 1:
The patent performs preliminary data transformation using a pre-computed transformation matrix before the main learning process. This preliminary action of transforming raw data into optimally represented features removes noise and highlights discriminative patterns upfront, improving model performance without adding significant complexity during the actual training phase.
Data Source
AI summary
A data processing method and apparatus based on neural population coding, a storage medium, and a processor are provided. The method includes: obtaining raw data and performing a common spatial pattern transformation on the raw data to obtain transformed data; obtaining, based on the transformed data, a first target function including a first matrix, where the first target function is a target function of a neural population coding network model of the raw data, and the first matrix is a weight parameter of the target function of the neural population coding network model; updating the first matrix according to a preset gradient descent update rule, to obtain a second matrix; and updating the first target function based on the second matrix.