A structured matrix factorization framework for large scale calcium imaging data analysis

Understand

We present a structured matrix factorization approach to analyzing calcium imaging recordings of large neuronal ensembles.

• Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity of each neuron from the slow dynamics of the calcium indicator.
• The matrix factorization approach relies on the observation that the spatiotemporal fluorescence activity can be expressed as a product of two matrices: a spatial matrix that encodes the location of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time.
• We present a simple approach for estimating the dynamics of the calcium indicator as well as the observation noise statistics from the observed data.