The fields of astronomical signal processing, neuromorphic computing, and artificial intelligence are undergoing significant transformations, driven by innovations in implicit neural representations, biological plausible models, and efficient architectures. Researchers are exploring new ways to improve the classification and representation of astronomical objects, leveraging tools such as LSTM neural networks and wavelet decomposition. In neuromorphic computing, there is a growing interest in developing event-based systems and integrating homeostatic mechanisms into spiking neural networks. The field of artificial intelligence is moving towards more efficient and robust transformer architectures, diffusion models, and adaptive computation methods. Notable advancements include the development of compact neural networks for astronomy, neural spectral transport representation for space-varying frequency fields, and scaling implicit fields via hypernetwork-driven multiscale coordinate transformations. Additionally, researchers are making progress in similarity systems and associative memory, with a focus on compositional and adaptive approaches. The use of witness-overlap structures, modular construction of similarity systems, and adaptive similarity measures are enabling the development of more robust and efficient similarity systems. Overall, these advancements have the potential to revolutionize various applications in astronomy, computer vision, natural language processing, and beyond.