Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data

Abstract

The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.

Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity

“…Concurrent with this spatial organization, brain activity exhibits correlated firing among large groups of neurons, often referred to as neural assemblies (Harris, 2005). This assembly organization of brain dynamics has been observed in, e.g., auditory cortex (Bathellier et al, 2012), motor cortex (Narayanan et al, 2005), prefrontal cortex (Tavoni et al, 2017), hippocampus (Lin et al, 2005), retina (Shlens et al, 2009), and zebrafish optic tectum (Romano et al, 2015;Mölter et al, 2018;Diana et al, 2019;Triplett et al, 2020). These neural assemblies are thought to form elementary computational units and subserve essential cognitive functions such as short-term memory, sensorimotor computation or decision-making (Hebb, 1949;Gerstein 10 et al, 1989;Harris, 2005;Buzsáki, 2010;Harris, 2012;Palm et al, 2014;Eichenbaum, 2018).…”

“…This neural recording technique opens up new avenues for constructing near-complete models of neural activity, and in particular its assembly organization. Recent attempts have been made to identify assemblies using either clustering (Panier et al, 2013; Triplett et al, 2018; Chen et al, 2018; Mölter et al, 2018; Bartoszek et al, 2021), dimensionality reduction approaches (Romano et al, 2015; Mu et al, 2019) or latent variable models (Diana et al, 2019; Triplett et al, 2020), albeit often limited to single brain regions. However, these methods do not explicitly assess to what extent the inferred assemblies could give rise to the observed neural data statistics, which is a crucial property of physiologically meaningful assemblies (Harris, 2005).…”

Dimensionality reduction of calcium-imaged neuronal population activity

“…Previous studies have proposed methods for analyzing calcium imaging recordings that include latent variables and deconvolution in the same statistical model. Triplett et al 46 developed a method to study the interaction between evoked and spontaneous activity using calcium imaging recordings in sensory systems. In their model, the latent variables represent activity fluctuations shared amongst neurons that are not explained by the sensory stimulus, where these activity fluctuations are defined to be spontaneous activity.…”

A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with sub-frame temporal resolution

“…2p preprocessing pipelines 5,26 normally include methods that correct for motion, localize and demix neurons' fluorescence signals, and infer event rates from fluorescence traces. Several studies have applied deep learning in attempts to improve signal quality [37][38][39] , while a few others have focused on uncovering population-level structure [40][41][42][43][44][45] or locally linear dynamics underlying population activity, in particular via switching linear dynamical systems-based methods 46,47 . Here we build RADICaL on the AutoLFADS architecture, which leverages deep learning and large-scale distributed training.…”