Toward reconstructing spike trains from large-scale calcium imaging data.

Neural activity can be captured by state-of-the-art optical imaging methods although the analysis of the resulting data sets is often manual and not standardized. Therefore, laboratories using large-scale calcium imaging eagerly await software toolboxes that can automate the process of identifying cells and inferring spikes. An algorithm proposed and implemented in a recent paper by Mukamel et al. [Neuron 63, 747-760 (2009)] used independent component analysis and offers significant improvements over conventional methods. The approach should be widely applicable, as tested with data obtained from the mouse cerebellum, neocortex, and spinal cord. The emergence of analysis tools in parallel with the rapid advances in optical imaging is an exciting development that will stimulate new discoveries and further elucidate the functions of neural circuits.