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Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Information Theory

    Background:

    • Understanding the internal workings of trained neural networks (NNs) is crucial for advancing AI.
    • Information-theoretic quantities offer a quantitative lens to analyze NN behavior.

    Purpose of the Study:

    • To investigate the relationship between information-theoretic measures (entropy, mutual information, KL divergence-based selectivity) and classification performance in feedforward NNs.
    • To explore how these measures correlate with performance during neuron ablation experiments.

    Main Methods:

    • Trained fully connected feedforward neural networks on MNIST, FashionMNIST, and CIFAR-10 datasets.
    • Cumulatively ablated neurons and analyzed information-theoretic quantities (entropy, mutual information, class selectivity).
    • Examined correlations between these quantities and test set classification performance.

    Main Results:

    • Class selectivity did not consistently predict overall classification performance across networks.
    • Mutual information and class selectivity showed positive correlations with performance within individual layers for ReLU networks.
    • Comparing information-theoretic quantities across different layers is not advisable.

    Conclusions:

    • Neuron ablation combined with information-theoretic analysis can reveal insights into neuron redundancy and synergy.
    • Findings provide a nuanced understanding of information processing within neural networks, connecting to information bottleneck theory.