realSEUDO for real-time calcium imaging analysis

Iuliia Dmitrieva, Sergey Babkin, Adam Shabti Charles

Keywords: calcium imaging, real-time, LASSO, convex optimization, fast algorithms, neuroscience

TL;DR: In this work we present an algorithm that leverages algorithmic, modeling, and implementation advances to perform real-time inference of cell profiles and time-courses in calcium imaging data, facilitating future closed-loop experimentation.

Abstract: Closed-loop neuroscience experimentation, where recorded neural activity is used to modify the experiment on-the-fly, is critical for deducing causal connections and optimizing experimental time. A critical step in creating a closed-loop experiment is real-time inference of neural activity from streaming recordings. One challenging modality for real-time processing is multi-photon calcium imaging (CI). CI enables the recording of activity in large populations of neurons however, often requires batch processing of the video data to extract single-neuron activity from the fluorescence videos. We use the recently proposed robust time-trace estimator—Sparse Emulation of Unused Dictionary Objects (SEUDO) algorithm—as a basis for a new on-line processing algorithm that simultaneously identifies neurons in the fluorescence video and infers their time traces in a way that is robust to as-yet unidentified neurons. To achieve real-time SEUDO (real-SEUDO), we optimize the core estimator via both algorithmic improvements and fast C-based implementation, as well as by creating a new cell finding loop to enable real-SEUDO to also identify new cells. We demonstrate comparable performance to offline algorithms (e.g., CNMF), and improved performance over the current on-line approach (OnACID) at speeds up to 120Hz on average.

Primary Area: applications to neuroscience & cognitive science

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Submission Number: 6111