<ul data-eligibleForWebStory="true"><li>Exploring complex-valued neural networks reveals hidden periodic alter-egos of activation functions.</li><li>Transitioning neural networks to complex numbers introduces challenges with activation function differentiability.</li><li>Complex-Valued Neural Networks (CVNNs) show promise in wave-related fields but face activation function limitations.</li><li>Traditional activation functions like tanh become singular and non-differentiable in the complex plane.</li><li>There is a trade-off between boundedness and differentiability leading to need for specialized functions.</li></ul>

What happens if we have complex-valued neural networks? A Thought Experiment

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