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Published on: February 15, 2017
Genetic design of biologically inspired receptive fields for neural pattern recognition
C A Perez1, C A Salinas, P A Estevez
1Dept. of Electr. Eng., Univ. de Chile, Santiago, Chile.
Summary
This study introduces a novel method using simulated evolution to design biologically inspired receptive fields in neural networks (NNs) for enhanced pattern recognition. This approach significantly improves classification accuracy in tasks like handwritten digit and face recognition compared to standard NNs.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Machine Learning
Background:
- Traditional feedforward neural networks (NNs) often lack specialized architectures for complex pattern recognition.
- Designing effective receptive fields is crucial for improving NN performance in tasks like image analysis.
Purpose of the Study:
- To propose and evaluate a novel method for designing biologically inspired receptive fields in feedforward NNs using simulated evolution.
- To enhance pattern recognition capabilities by creating neural architectures specifically tuned for given problems.
Main Methods:
- A combined neural architecture featuring a feature extraction network (FEN) followed by a classifier was proposed.
- Receptive fields in the FEN were constructed using additive superposition of excitatory and inhibitory fields.
- A genetic algorithm (GA) was employed to optimize receptive field parameters (size, orientation, bias, number) for improved classification.
Main Results:
- The GA-optimized receptive fields led to significant improvements in classification performance.
- Handwritten digit classification accuracy reached up to 90.8%, and face recognition accuracy reached up to 84.2%.
- Performance gains were notably higher than those achieved by standard feedforward multilayer perceptron (MLP) NNs.
Conclusions:
- The study demonstrates a strong dependency between NN classification performance and receptive field architecture.
- Simulated evolution provides an effective mechanism for designing specialized neural architectures.
- The proposed method offers a promising approach for enhancing pattern recognition in NNs.
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