UC San Diego

Traditionally, characterizing many properties of biological or silicon neural systems has been expensive, laborious, or impossible. Conductance models describing how properties of these systems change with time can be used with accessible data, such as measured voltage traces, to help characterize inaccessible properties such as ionic currents or transistor mismatch. This is accomplished using variational methods which formulate an inference problem about these properties as nonlinear optimization. Because measurement noise and model error are inevitable in the study of complex systems, the method is designed to cope with unknown processes. Conductance models are overparameterized, causing the inference problem to remain underdetermined, which can result in a proliferation of widely separated sets of estimated model parameters producing accurate predictions. Additionally, real world data will be approximated by a model in a number of ways, leading to an additional contribution to this model identifiability problem. This dissertation probes and overcomes some of the difficulties encountered in the analysis of real world data in individual biological and silicon neurons. One key result is the characterization of a neuromorphic silicon neuron followed by emulation of a biological neuron on the silicon substrate. Another key result is a data mining approach which discovers statistical differences in estimated model parameters, despite underdeterminacy, in an Alzheimer's strain of neurons in mice compared to healthy controls.