  Calcium is a ubiquitous messenger in neural signaling events. An increasing
number of techniques are enabling visualization of neurological activity in
animal models via luminescent proteins that bind to calcium ions. These
techniques generate large volumes of spatially correlated time series. A
model-based functional data analysis methodology via Gaussian mixtures is
suggested for the clustering of data from such visualizations is proposed. The
methodology is theoretically justified and a computationally efficient approach
to estimation is suggested. An example analysis of a zebrafish imaging
experiment is presented.
