Computational Neurology

Computational neuroscience is an approach to understanding the information content of neural signals by modeling the nervous system at many different structural scales, including the biophysical, the circuit, and the systems levels. Computer simulations of neurons and neural networks are complementary to traditional techniques in neuroscience. Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm. Brain science seeks to understand the myriad functions of the brain in terms of principles that lead from molecular interactions to behavior. Although the complexity of the brain is daunting and the ?eld seems brazenly ambitious, painstaking experimental e?orts have made impressive progress. While investigations, being dependent on methods of measurement, have frequently been driven by clever use of the newest technologies, many diverse phenomena have been rendered comprehensible through interpretive analysis, which has often leaned heavily on mathematical and statistical ideas. These ideas are varied, but a central framing of the problem has been to “elucidate the representation and transmission of information in the nervous system” (Perkel and Bullock 1968). In addition, new and improved measurement and storage devices have enabled increasingly detailed recordings, as well as methods of perturbing neural circuits, with many scientists feeling at once ex-cited and overwhelmed by opportunities of learning from the ever-larger and more complex data sets they are collecting. Thus, in Neuro Forum 2023 computational neurology has come to encompass not only a program of modeling neural activity and brain function at all levels of detail and abstraction, from sub-cellular biophysics to human behavior, but also advanced methods for analysis of neural data.