A mixture-model approach for burst detection in basal ganglia spike trains
The paper introduces LognormISI, a data-driven mixture-modeling method that overcomes the limitations of fixed-threshold detectors by identifying statistically separable burst phenotypes in basal ganglia spike trains with high specificity, offering a reproducible framework for analyzing burst-symptom associations in Parkinson's disease.