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

## TL;DR

This study shows that cortical state fluctuations, not just visual stimuli, influence how neurons in the early visual cortex respond, and a new model can better predict these responses by incorporating both factors.

## Contribution

A novel system identification model combining stimulus-driven and cortical state-driven pathways improves prediction of neuronal responses and receptive field estimation.

## Key findings

- A model incorporating cortical state signals improves prediction of neural responses compared to stimulus-only models.
- Neurons with higher response variability benefit more from cortical state information in the model.
- Cortical state fluctuations are continuously linked to population signals like LFPs and MUA during recordings.

## 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 random noise and addressed by simple trial-averaging to obtain the stimulus-driven response. However, 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 each neuron’s spiking activity. We further evaluated the improvements in estimated receptive fields of neurons when incorporating cortical state-related information in our system identification model. The fitted model performed with a significantly higher accuracy in predicting neural responses as well as qualitative improvements in the estimated receptive fields 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.

Neuronal 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 and estimate qualitative receptive fields 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.