**Archaeology of AI and Cognitive Science: A Deep Dive into Neural Architecture**
Explore the fascinating excavation of artificial intelligence's theoretical foundations and cognitive modeling approaches. This collection spans from classic neural architectures like Hopfield networks and self-organizing maps to cutting-edge developments in spiking neural networks and neural cellular automata. Discover how researchers are bridging biological and artificial intelligence through studies of Drosophila brains, EEG data analysis, and investigations into whether LLMs can predict human neural activation. The selection includes critical examinations of modern deep learning—from tokenization bottlenecks to quantization techniques—alongside theoretical frameworks treating language models as Markov chains and exploring polynomial theories of complex systems. Practical applications range from medical leaflet simplification to linguistic comparisons between Romanian dialects, revealing the interdisciplinary "archaeological layers" of contemporary AI research.