Background: Visual cortex neurons often respond to stimuli very differently on repeated trials. This trial-by-trial variability is known to be correlated among nearby neurons. Our long-term goal is to quantitatively estimate neuronal response variability, using multi-channel local field potential (LFP) data from single trials.
Methods: Acute experiments were performed with anesthetized (Remifentanil, Propofol, nitrous oxide) and paralyzed (Gallamine Triethiodide) cats. Computer-controlled visual stimuli were displayed on a gamma-corrected CRT monitor. For the principal experiment, two kinds of visual stimuli were used: drifting sine-wave gratings, and a uniform mean-luminance gray screen. These two stimuli were each delivered monocularly for 100 sec in a random order, for 10 trials. Multi-unit activity (MUA) and LFP signals were extracted from broadband raw data acquired from Area 17 and 18 using A1X32 linear arrays (NeuroNexus) and the OpenEphys recording system. LFP signal processing was performed using Chronux, an open-source MATLAB toolbox. Current source density (CSD) analysis was performed on responses to briefly flashed full-field stimuli using the MATLAB toolbox, CSDplotter. The common response variability (global noise) of MUA was estimated using the model proposed by Scholvinck et al. [2015].
Results: On different trials, a given neuron responded with different firing to the same visual stimuli. Within one trial, a neuron’s firing rate also fluctuated across successive cycles of a drifting grating. When the animal was given extra anesthesia, neurons fired in a desynchronized pattern; with lighter levels of anesthesia, neuronal firing because more synchronized. By examining the cross-correlations of LFP signals recorded from different cortical layers, we found LFP signals could be divided to two groups: those recorded in layer IV and above, and those from layers V and VI. Within each group, LFP signals recorded by different channels are highly correlated. These two groups were observed in lighter and deeper anesthetized animals, also in sine-wave and uniform gray stimulus conditions. We also investigated correlations between LFP signals and global noise. Power in the LFP beta band was highly correlated with global noise, when animals were in deeper anesthesia.
Conclusions: Brain states contribute to variations in neuronal responses. Raw LFP correlation results suggest that we should analyze LFP data according to their laminar organization. Correlation of low-frequency LFP under deeper anesthesia with global noise gives us some insight to predict noise from single-trial data, and we hope to extend this analysis to lighter anesthesia in the future.
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.
