Neural Engineering and Computational Neuroscience
Quantitative measures and computational descriptions of neural systems is the focus of neuroengineering and computational neuroscience. The cross-disciplinary integration of information and methods from multiple scientific fields (neuroscience, engineering, materials science, chemistry, biophysics, mathematics, computer science, and psychology) provides greater possibilities to improve life than any one discipline alone. From subcellular computational domains to single cells and large-scale networks, the nervous system is the premier example of how functions emerge from complex systems. Medical technological advances arise from engineering based studies of the nervous system function and its disorders. Modeling neural systems allows for solving problems of other disciplines (e.g., information processing, pattern recognition, artificial intelligence, and machine learning).
Developing sensory and prosthetic systems to monitor physiological function and assist human behavior enables personalized therapeutics to improve quality of life in patient care. Computational models are essential for bridging the theoretical and experimental approaches to neuroscience, permitting comparisons across multiple spatial-temporal scales from genes and proteins to behaviors, to electrochemical potentials and network oscillations.