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Found 4 papers, 2 papers with code
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer
As convolutional neural networks (CNNs) become more accurate at object recognition, their representations become more similar to the primate visual system.
Different Spectral Representations in Optimized Artificial Neural Networks and Brains
For convolutional networks, the best $\alpha$ values depend on the task complexity and evaluation metric: lower $\alpha$ values optimized validation accuracy and robustness to adversarial attack for networks performing a simple object recognition task (categorizing MNIST images of handwritten digits); for a more complex task (categorizing CIFAR-10 natural images), we found that lower $\alpha$ values optimized validation accuracy whereas higher $\alpha$ values optimized adversarial robustness.
Integration of Leaky-Integrate-and-Fire-Neurons in Deep Learning Architectures
We apply our method to the IRIS blossoms image data set and show that the training technique can be used to train LIF neurons on image classification tasks.
Sparsity through evolutionary pruning prevents neuronal networks from overfitting
We conclude that sparsity is a central property of neural networks and should be considered for modern Machine learning approaches.
