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Constraining Ill-Posed Inverse Problems in Neural Electrophysiology via Biophysically Detailed Forward Simulation
- Baratham, Vyassa
- Advisor(s): Bouchard, Kristofer;
- DeWeese, Michael
Abstract
Biophysically detailed simulation is an invaluable tool for understanding experimental data when those data do not uniquely determine the underlying state of the system, a situation we refer to as an ill-posed inverse problem. Such problems arise frequently in the study of biological systems with many degrees of freedom. This dissertation presents simulation- based approaches to two ill-posed inverse problems in neural electrophysiology. First, using a large volume of simulated data, we demonstrate that a Convolutional Neural Network can be trained to determine the conductances of various ion channels in a neuron from its somatic membrane potential in response to a current injection. Next, we use a simulation to study the cellular origin of electrical signals recorded at the surface of the brain, and find that they are produced primarily in layers V and VI of the cortex, contrary to the intuition that neurons closer to the electrode should contribute more of the signal. In both cases, simulation is a natural way to incorporate biological constraints to rule out certain a priori plausible solutions. Our results show how the massive throughput, fine-grained control over model parameters, and access to underlying ground-truth details within a simulation can be utilized to overcome the ill-posedness that many biological problems exhibit when stated in physical terms.
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