Revealing unobserved factors underlying cortical activity using a rectified latent variable model applied to neural population recordings

Abstract

The activity of sensory cortical neurons is not only driven by external stimuli, but is also shaped by other sources of input to the cortex. Unlike external stimuli these other sources of input are challenging to experimentally control or even observe, and as a result contribute to variability of neuronal responses to sensory stimuli. However, such sources of input are likely not \"noise\", and likely play an integral role in sensory cortex function. Here, we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs non-negative (rectified) latent variables, and is able to be much less restrictive in the mathematical constraints on solutions due to the use an autoencoder neural network to initialize model parameters. We show the RLVM outperforms principal component analysis, factor analysis and independent component analysis across a variety of measures using simulated data. We then apply this model to the 2-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as non-stimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function, and demonstrates novel methods for leveraging large population recordings to this end.