Description: The neural field theory is a mathematical framework that seeks to understand the dynamics of neural networks by representing their activations as continuous fields in space. This approach allows modeling the interaction between neurons and how they communicate and process information more efficiently. Instead of treating neurons in isolation, neural field theory considers the network as a whole, where the activations of neurons can be seen as functions that vary in space, facilitating the analysis of patterns and the generalization of results. This framework is based on concepts from physics and complex systems theory, providing a solid foundation for understanding phenomena such as network convergence and solution stability. Additionally, neural field theory offers tools for optimizing network training, allowing for a better understanding of how parameter modifications affect the overall performance of the model. In summary, this approach not only enriches the theory behind neural networks but also opens new avenues for research and the development of more robust and efficient algorithms in the field of machine learning.
