A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data. Author summary Many studies in neuroscience revolve around understanding the patterns of activity of neurons and their relation to behavior. To be able to address such questions one must first record the activity of neurons. Broadly speaking, two different approaches are commonly used, each with its own advantages and disadvantages. Imaging can sample neural activity of hundreds of neurons in a local area and can be targeted to specific cell-types. But it does not record activity directly, reporting it rather through a transformation from intracellular calcium. Electrophysiological recordings report neural activity directly with high temporal precision but have limitations of their own such as being less likely to accurately pickup neurons with low activity. We compared neuronal spikes and fluorescence recorded in matched neural populations in behaving mice performing the same task. We report multiple discrepancies between analyses performed on the two types of data at the single neuron and population level. We developed a model transforming spike trains to synthetic-imaging data which recapitulated many of the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to predict and understand differences between them.