  Miniaturized calcium imaging is an emerging neural recording technique that
has been widely used for monitoring neural activity on a large scale at a
specific brain region of rats or mice. Most existing calcium-image analysis
pipelines operate offline. This results in long processing latency, making it
difficult to realize closed-loop feedback stimulation for brain research. In
recent work, we have proposed an FPGA-based real-time calcium image processing
pipeline for closed-loop feedback applications. It can perform real-time
calcium image motion correction, enhancement, fast trace extraction, and
real-time decoding from extracted traces. Here, we extend this work by
proposing a variety of neural network based methods for real-time decoding and
evaluate the tradeoff among these decoding methods and accelerator designs. We
introduced the implementation of the neural network based decoders on the FPGA,
and showed their speedup against the implementation on the ARM processor. Our
FPGA implementation enables the real-time calcium image decoding with sub-ms
processing latency for closed-loop feedback applications.
