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

Tid: To 2017-06-29 kl 13.00

Plats: Room 3418, Lindstedtsvägen 25, Dept of Mathematics, KTH

Abstract
Extracting neural activity from electrophysiological and calcium imaging recordings is an important problem in neuroscience. 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.

Respondent: Pranav Mamidanna

Opponent: Marcus Lundberg

Handledare: Benjamin Dunn (NTNU), Metta Langaas (NTNU), Michael Hanke

2017-06-29T13:00 2017-06-29T13:00 Pranav Mamidanna: Optimizing Neural Source Extraction Algorithms: A Performance Measure Based on Neuronal Network Properties Pranav Mamidanna: Optimizing Neural Source Extraction Algorithms: A Performance Measure Based on Neuronal Network Properties
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