Grosmark, A. | Search bioRxiv

bioRxiv2021

Distinct ground state and activated state modes of spiking in forebrain neurons

Neuronal firing patterns have significant spatiotemporal variability with no agreed upon theoretical framework. Using a combined experimental and modeling approach, we found that spike interval statistics can be described by discrete modes of activity. Of these, a "ground state" (GS) mode of low-rate spiking is universal among forebrain excitatory neurons and characterized by irregular spiking at neuron-specific rates. In contrast, "activated state" (AS) modes consist of spiking at characteristic timescales and regularity that are specific to neuron populations in a given region and brain state. The majority of spiking is contributed by GS mode, while neurons can transiently switch to AS spiking in response to stimuli or in coordination with population activity patterns. We hypothesize that GS spiking serves to maintain a persistent backbone of neuronal activity while AS modes support communication functions.

neuroscience↗

bioRxiv2019

A zero-inflated gamma model for post-deconvolved calcium imaging traces

Calcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing \"pre-processing\" tools applied to calcium video data, addressing the important issues of e.g., motion correction, denoising, compression, demixing, and deconvolution. However, computational modeling of deconvolved calcium signals (i.e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements. Surprisingly, these issues have to date received significantly less attention. To fill this gap, we examine the statistical properties of the deconvolved activity estimates, and propose several density models for these random signals. These models include a zero-inflated gamma (ZIG) model, which characterizes the calcium responses as a mixture of a gamma distribution and a point mass which serves to model zero responses. We apply the resulting models to neural encoding and decoding problems. We find that the ZIG model out-performs simpler models (e.g., Poisson or Bernoulli models) in the context of both simulated and real neural data, and can therefore play a useful role in bridging calcium imaging analysis methods with tools for analyzing activity in large neural populations.

neuroscience↗