General Theory of Neural Networks

Rob Leclerc explores the concept of Universal Activation Networks (UANs), highlighting how diverse systems from gene regulatory networks to artificial neural networks exhibit common principles of evolvability and open-endedness. He argues that these systems, sharing a familial computational structure, demonstrate robustness and adaptability, forming a basis for a unified theory of neural networks. Key themes include the critical importance of network topology over implementation details and the potential for pruning techniques to reveal fundamental, efficient network architectures.

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