Model-Driven Collection of Neural Modulation Data
- Resource Type
- Conference
- Authors
- Cole, Eric R.; Grogan, Dayton P.; Eggers, Thomas E.; Connolly, Mark J.; Laxpati, Nealen G.; Gross, Robert E.
- Source
- 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Neural Engineering (NER), 2021 10th International IEEE/EMBS Conference on. :281-284 May, 2021
- Subject
- Bioengineering
Signal Processing and Analysis
Neurological diseases
Uncertainty
Modulation
Neural engineering
Gaussian processes
Data collection
Tools
- Language
- ISSN
- 1948-3554
Neural modulation has become a fundamental tool for treating neurological diseases and understanding their mechanisms. A significant challenge in neural modulation is understanding and characterizing the brain's response to stimulation parameters - a difficult problem when parameter spaces are very large, their evaluation is time-consuming, and their effects feature complex inter- and intra- subject variability. In this study, we first fit a model to optogenetic stimulation data to construct a simulation of a neural modulation experiment. We then show two ways that Gaussian process modeling can supplement neural modulation data collection procedures - by quantifying the uncertainty of standard sampling approaches and by selecting samples via active learning. We found that the active learning approach required 33% fewer samples than the other strategies to accurately learn the mean of the simulation function. We also show that the uncertainty estimate of the active learning model converged most similarly to the ground-truth error throughout the simulation. These results suggest that Gaussian process modeling can quantitatively improve data collection procedures in neural modulation.