Cortical state contributions to response variability in the early visual cortex: A system identification approach

Abstract

Neurons in the early visual cortex respond selectively to multiple features of visual stimuli, but they respond inconsistently to repeated presentation of the same visual stimulus. Such trial-to-trial response variabilities are often treated as noise and addressed by simple trial-averaging to obtain the stimulus-driven response, though this approach is insufficient to fully remove the response variability. More importantly, response variability may primarily be caused by non-sensory factors, particularly by variations in cortical state. Here we recorded and analyzed neuronal spiking activity in response to natural images from areas 17 and 18 of cats, along with local population neuronal signals, i.e. local field potentials (LFPs) and multi-unit activity (MUA). Single neurons showed highly varying degrees of trial-to-trial response variability, even when recorded simultaneously. We used a variability ratio (VR) measure to quantify the trial-wise differences in neural responses, and two cortical state indicative measures, a global fluctuation index (GFI) calculated using MUA, and a synchrony index (SI) calculated from LFP signals. We propose a compact convolutional neural network model with parallel pathways, to capture the stimulus-driven activity and the cortical state-driven response variabilities. The stimulus-driven pathway is comprised of a spatiotemporal filter, a parametric rectifier and a Gaussian map, and the cortical state-driven pathway contains temporal filters for MUA and LFPs. The model parameters are fit to best predict the spiking activity of each neuron. The fitted model performed with a significantly higher accuracy in predicting neural responses compared to a basic model with a stimulus-driven pathway alone. The neurons with higher response variability benefited more from the cortical state-driven pathway compared to less variable neurons. These results show that different neurons may differ greatly in their variability and in the degree of their relationship to indicators of cortical state fluctuations. Author SummaryNeuronal responses in the early visual cortex to repeated presentation of an identical stimulus can be highly variable across trials. The variable portion of these neuronal responses can in some cases be as large as the stimulus-driven response. The cortical state fluctuations that may underlie the response variabilities can vary continuously during a data recording session, and these dynamics are associated with population response signals such as local field potentials and multi-unit activity. Here we demonstrate that a model combining these cortical signals along with a visual stimulus processing pathway can predict single neurons responses significantly better than a model containing a stimulus-driven pathway alone. This improvement in predictive performance is heterogeneous across cortical neurons, and is much greater in neurons that exhibit greater trial-wise response variabilities. Overall, this work provides insights to understanding how visual cortex neurons not only respond to visual stimuli, but also interact with non-sensory events such as cortical state fluctuations.