Background: All neurons of the visual system exhibit response to differences in luminance. This neural response to visual contrast, also known as the contrast response function (CRF), follows a characteristic sigmoid shape that can be fitted with the Naka-Rushton equation. Four parameters define the CRF, and they are often used in different visual research disciplines, since they describe selective variations of neural responses. As novel technologies have grown, the capacity to record thousands of neurons simultaneously brings new challenges: processing and robustly analyzing larger amounts of data to maximize the outcomes of our experimental measurements. Nevertheless, current guidelines to fit neural activity based on the Naka-Rushton equation have been poorly discussed in depth. In this study, we explore several methods of boundary-setting and least-square curve-fitting for the CRF in order to avoid the pitfalls of blind curve-fitting. Furthermore, we intend to provide recommendations for experimenters to better prepare a solid quantification of CRF parameters that also minimize the time of the data acquisition. For this purpose, we have created a simplified theoretical model of spike-response dynamics, in which the firing rate of neurons is generated by a Poisson process. The spike trains generated by the theoretical model depending on visual contrast intensities were then fitted with the Naka-Rushton equation. This allowed us to identify combinations of parameters that were more important to adjust before performing experiments, to optimize the precision and efficiency of curve fitting (e.g., boundaries of CRF parameters, number of trials, number of contrast tested, metric of contrast used and the effect of including multi-unit spikes into a single CRF, among others). Several goodness-of-fit methods were also examined in order to achieve ideal fits. With this approach, it is possible to anticipate the minimal requirements to gather and analyze data in a more efficient way in order to build stronger functional models.

Methods: Spike-trains were randomly generated following a Poisson distribution in order to draw both an underlying theoretical curve and an empirical one. Random noise was added to the fit to simulate empirical conditions. The correlation function was recreated on the simulated data and re-fit using the Naka-Rushton equation. The two curves were compared: the idea being to determine the most advantageous boundaries and conditions for the curve-fit to be optimal. Statistical analysis was performed on the data to determine those conditions for experiments. Experiments were then conducted to acquire data from mice and cats to verify the model.

Results: Results were obtained successfully and a model was proposed to assess the goodness of the fit of the contrast response function. Various parametres and their influence of the model were tested. Other similar models were proposed and their performance was assessed and compared to the previous ones. The fit was optimized to give semi-strict guidelines for scientists to follow in order to maximize their efficiency while obtaining the contrast tuning of a neuron.

Conclusions: The aim of the study was to assess the optimal testing parametres of the neuronal response to visual gratings with various luminance, also called the CRF. As technology gets more powerful and potent, one must make choices when experimenting. With a strong model, robust boundaries, and strong experimental conditioning, the best fit to a function can lead to more efficient analysis and stronger cognitive models.

Background: It is well known that the pulvinar establishes reciprocal connections with areas of the visual cortex, allowing the transfer of cortico-cortical signals through transthalamic pathways. However, the exact function of these signals in coordinating activity across the visual cortical hierarchy remains largely unknown. In anesthetized cats, we have explored whether pulvinar inactivation affects the dynamic of interactions between the primary visual cortex (a17) and area 21a, a higher visual cortical area, as well as between layers within each cortical area. We found that pulvinar inactivation modifies the local field potentials (LFPs) coherence between a17 and 21a during a visual stimulation. In addition, the Granger causality analysis showed that the functional connectivity changed across visual areas and between cortical layers during pulvinar inactivation, the effects being stronger in layers of the same area. We observed that the effects of pulvinar inactivation arise at two different epochs of the visual response, i.e., at the early and late components. The proportion of feedback and feedforward functional events was higher during the early and the late phases of the responses, respectively. We also found that pulvinar inactivation facilitates the feedback propagation of gamma oscillations from 21a to a17. This feedback transmission was predominant during the late response. At the temporal level, pulvinar inactivation also delayed the signals from a17 and 21a, depending on the source and the target of the cortical layer. Thus, the pulvinar can not only modify the functional connectivity between intra and inter cortical layers but may also control the temporal dynamics of neuronal activity across the visual cortical hierarchy.

Methods: In vivo electrophysiological recordings of visual cortical areas, area 17 and 21a, in anesthetized cats, were then explored with temporal serial analysis (i.e., Fourier analysis, Coherence, Cross-correlation and Granger causality) of the local field potential.

Results: Inactivation of the thalamic nucleus modifies the dynamics of areas 17 and 21a. The changes observed depends on the source and the target of the cortical layer. The pulvinar inactivation arise at two different epochs of visual response.

Conclusions: The pulvinar modifies the functional connectivity between intra and inter cortical layers and may also control the temporal dynamics of neuronal activity across the visual cortical hierarchy.

Background: For years, studies using several animal models have highlighted the predominant role of the primary visual area in visual information processing. Its six cortical layers have morphological, hodological and physiological differences, although their roles regarding the integration of visual contrast and the messages sent by the layers to other brain regions have been poorly explored. Given that cortical layers have distinct properties, this study aims to understand these differences and how they are affected by a changing visual contrast.

Methods: A linear multi-channel electrode was placed in the primary visual cortex (V1) of the anesthetized mouse to record neuronal activity across the different cortical layers. The laminar position of the electrode was verified in real time by measuring the current source density (CSD) and the multi-unit activity (MUA), and confirmed post-mortem by histological analysis. Drifting gratings varying in contrast enabled the measurement of the firing rate of neurons throughout layers. We fitted this data to the Naka-Rushton equations, which generated the contrast response function (CRF) of neurons.

Results: The analysis revealed that the baseline activity as well as the rate of change of neural discharges (the slope of the CRF) had a positive correlation across the cortical layers. In addition, we found a trend between the cortical position and the contrast evoking the semi-saturation of the activity. A significant difference in the maximum discharge rate was also found between layers II/III and IV, as well as between layers II/III and V.

Conclusions: Since layers II/III and V process visual contrast differently, our results suggest that higher cortical visual areas, as well subcortical regions, receive different information regarding a change in visual contrast. Thus, a contrast may be processed differently throughout the different areas of the visual cortex.