Optimizing Neural Source Extraction Algorithms: A Performance Measure  Based on Neuronal Network Properties

Abstract: Extracting neural activity from electrophysiological and calcium All existing automated algorithms for this purpose, however, rely heavily on manual intervention and parameter tuning. In this thesis, we introduce a novel performance measure based on well-founded notions of neuronal network organization. This enables us to systematically tune parameters, using techniques from statistical design of experiments and response surface methods. We implement this framework on an algorithm used to extract neural activity from microendoscopic calcium imaging datasets, and demonstrate that this greatly reduces manual intervention.