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General Theory of Neural Networks
From gene regulatory networks to artificial neural networks
My findings anticipated later developments in machine learning, such as Hinton et al’s discovery that pruning artificial neural networks enhances performance, leading to so-called dropout strategies. A computational null hypothesis helps identify biases and uncover new areas for inquiry, demystifying complex systems and driving profound insights into both biological and artificial networks. Similarly, biological systems such as gene regulatory networks and cellular signaling pathways favor sparse, functionally critical interactions due to evolutionary pressures, ensuring efficiency and robustness.
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