CellMincer: Self-Supervised Denoising of Functional Imaging

All-optical electrophysiology offers accessibility and scalability in observing neuronal activity beyond what can feasibly be achieved with patch clamp techniques. However, imaging platforms like Optopatch suffer from excessive detection noise, photobleaching, and an inability to organically segment and isolate neurons of interest. These drawbacks preclude its use as a true substitute for direct electrophysiological measurement, but recent advances in deep neural network inference may enable computation to recover the difference in data quality. To date, few robust denoising algorithms have been designed and implemented for voltage imaging data, in part because the lack of ground truth imaging complicates the task of training such a model. This thesis introduces CellMincer, a self-supervised deep neural network for denoising functional imaging. By exploiting a combination of spatiotemporally local contexts and precomputed global features, CellMincer outperforms comparable algorithms at denoising several modes of optical electrophysiology on a range of metrics, including measures of biologically relevant features.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Wang, Brice

Advisors dc:contributor.advisor

• Babadi, Mehrtash
• Uhler, Caroline

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/145032

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/145032