Quantum Information Science: The Human Brain and Neural Signaling

Mathematical advance in quantum information science is proceeding quickly and applies to many fields, particularly the complexities of neuroscience (here focusing on image-readable physical behaviors such as neural signaling, as opposed to higher-order operations of cognition, memory, and attention). Quantum mathematical models are extensible to neuroscience problem classes treating dynamical time series, diffusion, and renormalization in multiscalar systems. Approaches first reconstruct wavefunctions observed in EEG and fMRI scans. Second, single-neuron models (Hodgkin-Huxley, integrate-and-fire, theta neurons) and collective neuron models (neural field theories, Kuramoto oscillators) are employed to model empirical data. Third, genome physics is used to study time series sequence prediction in DNA, RNA, and proteins based on 3d+ complex geometry involving fields, curvature, knotting, and information compaction. Finally, quantum neuroscience physics is applied in AdS/Brain modeling, Chern-Simons biology (topological invariance), neuronal gauge theories, network neuroscience, and the chaotic dynamics of bifurcation and bistability (to explain epileptic and resting states). The potential benefit of this work is an improved understanding of disease and pathology resolution in humans.