  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.
