Investigating the biophysical basis of neural computation and the computational basis of pain

Our ultimate goal is to uncover how the nervous system processes pain-related sensory information and how chronic pain arises from aberrations in that processing. To that end, we investigate the biophysical basis for neural information processing at the cellular and network levels... most of those have broad applications. Beyond that, we consider the specific consequences of neural information processing for pain. Thus, through a two-step process - linking biophysics with information processing, and information processing with pain - we hope to gain detailed, robust understanding of the biophysical (molecular) basis of chronic pain.

Our approach is a multidisciplinary one involving electrophysiology, calcium imaging, and optogenetics together with computational simulations and rigorous mathematical analysis.

Our view is that the pain system, like other parts of the nervous system, is complex because it is nonlinear. Nonlinearities occur when different parts of a system (e.g. different ions channels within a neuron, or different neurons within a network) cooperate, compete, or interfere with one another. Nonlinearities cause the principle of superposition to fail, which means the system is not the sum of its parts. To grapple with the nonlinear features of pain processing, we exploit the tools and concepts of nonlinear dynamics. Our hope is that this sort of computational approach will deepen our theoretical understanding of pain processing under normal and pathological conditions.