realSEUDO for real-time calcium imaging analysis

TL;DR

This paper introduces realSEUDO, a real-time calcium imaging analysis algorithm that efficiently identifies neurons and infers their activity, enabling closed-loop neuroscience experiments with high speed and robustness.

Contribution

The paper presents realSEUDO, a novel real-time processing algorithm for calcium imaging that improves speed and robustness over existing online methods.

Findings

Achieves 120 Hz processing speed.

Performs comparably to offline algorithms like CNMF.

Outperforms current online methods such as OnACID.

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 (realSEUDO), we optimize the core estimator via both algorithmic improvements and an fast C-based implementation, and create a new cell finding loop to enable realSEUDO 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 of 120 Hz on average.