Biology subjects

Sooriyaarachchi, J.

Publications and source records attributed to Sooriyaarachchi, J..

2 recordsLinked to original sources

bioRxiv2024

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

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.

neuroscience↗

bioRxiv2023

Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification

Neurons in the primary visual cortex respond selectively to simple features of visual stimuli, such as orientation and spatial frequency. Simple cells, which have phase-sensitive responses, can be modeled by a single receptive field filter in a linear-nonlinear model. However, it is challenging to analyze phase-invariant complex cells, which require more elaborate models having a combination of nonlinear subunits. Estimating parameters of these models is made additionally more difficult by cortical neurons trial-to-trial response variability. We develop a simple convolutional neural network method to estimate receptive field models for both simple and complex visual cortex cells from their responses to natural images. The model consists of a spatiotemporal filter, a parameterized rectifier unit (PReLU), and a two-dimensional Gaussian "map" of the receptive field envelope. A single model parameter determines the simple vs. complex nature of the receptive field, capturing complex cell responses as a summation of homogeneous subunits, and collapsing to a linear-nonlinear model for simple type cells. The convolutional method predicts simple and complex cell responses to natural image stimuli as well as grating tuning curves. The fitted models yield a continuum of values for the PReLU parameter across the sampled neurons, showing that the simple/complex nature of cells can vary in a continuous manner. We demonstrate that complex-like cells respond less reliably than simple-like cells - compensation for this unreliability reveals good predictive performance on novel sets of natural images, with predictive performance for complex cells proportionately closer to that for simple cells. Most spatial receptive field structures are well fit by Gabor functions, whose parameters confirm well-known properties of cat A17/18 receptive fields. Author summaryMethods for recording increasingly many visual cortex neurons are advancing rapidly, demanding new approaches to characterize diverse receptive fields. We present a compact convolutional neural network model of early cortical neurons, which is uniformly applicable to simple and complex cells, and whose parameters are straightforwardly interpretable and readily estimated from responses to natural image stimuli. This novel approach introduces a single estimated model parameter to capture the simple/complex nature of a neurons receptive field, revealing a continuum of simple vs complex-like behaviour. We show that almost all complex-like cells exhibit a lower response reliability to repeated presentations of the same stimuli, compared to more reliable responses from simple-like cells. Accounting for this "noise ceiling" brings predictive performance for complex cells proportionately closer to that for simple cells. Using this model estimation approach with natural images, we evaluate findings from previous approaches that were restricted to simple-type cells and the use of grating or white noise stimuli, revealing a diversity of Gabor-like spatial receptive field shapes, which lie along a continuum of spatial bandwidths.

neuroscience↗