2 research outputs found
Sequential Bayesian Detection of Spike Activities from Fluorescence Observations
Extracting and detecting spike activities from the fluorescence observations
is an important step in understanding how neuron systems work. The main
challenge lies in that the combination of the ambient noise with dynamic
baseline fluctuation, often contaminates the observations, thereby
deteriorating the reliability of spike detection. This may be even worse in the
face of the nonlinear biological process, the coupling interactions between
spikes and baseline, and the unknown critical parameters of an underlying
physiological model, in which erroneous estimations of parameters will affect
the detection of spikes causing further error propagation. In this paper, we
propose a random finite set (RFS) based Bayesian approach. The dynamic
behaviors of spike sequence, fluctuated baseline and unknown parameters are
formulated as one RFS. This RFS state is capable of distinguishing the hidden
active/silent states induced by spike and non-spike activities respectively,
thereby \emph{negating the interaction role} played by spikes and other
factors. Then, premised on the RFS states, a Bayesian inference scheme is
designed to simultaneously estimate the model parameters, baseline, and crucial
spike activities. Our results demonstrate that the proposed scheme can gain an
extra detection accuracy in comparison with the state-of-the-art MLSpike
method