Scientists develop a method to map brain activity into a frequency domain representation, where thoughts, memories, and consciousness itself are encoded as specific harmonic patterns. This is achieved through advanced Fourier-like transforms or wavelet analyses of neural data.
Researchers discover that consciousness operates on multiple simultaneous "frequencies" of neural oscillation (we already know about alpha, beta, gamma waves etc). The breakthrough comes from finding that these frequencies follow precise mathematical relationships similar to harmonics in music. Furthermore, we find that consciousness can be represented as a set of eigenmodes (fundamental vibrational modes) of a neural network. By identifying and isolating these eigenmodes, they develop a method to compress consciousness into a compact, efficient form.
Researchers discover a way to encode neural network architectures into quantum crystalline structures, where information is stored and processed in the topological properties of the lattice (e.g., edge states or topological invariants). These structures are highly ordered, efficient, and capable of parallel processing at unprecedented scales.
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