The miniaturized calcium imaging is an emerging neural recording technique
that can monitor neural activity at large scale at a specific brain region of a
rat or mice. It has been widely used in the study of brain functions in
experimental neuroscientific research. Most calcium-image analysis pipelines
operate offline, which incurs long processing latency thus are hard to be used
for closed-loop feedback stimulation targeting certain neural circuits. In this
paper, we propose our FPGA-based design that enables real-time calcium image
processing and position decoding for closed-loop feedback applications. Our
design can perform real-time calcium image motion correction, enhancement, and
fast trace extraction based on predefined cell contours and tiles. With that,
we evaluated a variety of machine learning methods to decode positions from the
extracted traces. Our proposed design and implementation can achieve position
decoding with less than 1 ms latency under 300 MHz on FPGA for a variety of
mainstream 1-photon miniscope sensors. We benchmarked the position decoding
accuracy on open-sourced datasets collected from six different rats, and we
show that by taking advantage of the ordinal encoding in the decoding task, we
can consistently improve decoding accuracy without any overhead on hardware
implementation and runtime across the subjects.Comment: 11 pages, 15 figure