Abstract
Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.
Highlights
Certain anatomical motifs are repeated across disparate brain areas with wide-ranging functions
Before investigating each stimulus feature in isolation, we evaluated whether the grand average spiking response to our stimuli matched predictions from the cortical microcircuit (CCM) (Figure 2A)
The resulting laminar profile of activation was consistent with both the expectations set by the CCM and previous studies of laminar visual activation in that layer 4C activity preceded that of the other layers (Mitzdorf, 1985; Schroeder et al, 1998; Maier et al, 2010; Spaak et al, 2012; Van Kerkoerle et al, 2014)
Summary
Certain anatomical motifs are repeated across disparate brain areas with wide-ranging functions. The CCM gives rise to a series of distinct, yet overlapping, activation steps that are spatially segregated between the superficial (supragranular), deep (infragranular), and middle (granular) layers of cortex (Rockland and Pandya, 1979; Rockland and Virga, 1989; Callaway, 1998; Binzegger et al, 2004; Douglas and Martin, 2004) According to this model, ascending (feedforward) signals from parts of the brain that are closer to the sensory periphery terminate in the middle layers of cortical areas while descending (feedback) signals from downstream areas target the layers above and below (Rockland and Pandya, 1979; Rockland and Virga, 1989; Felleman and Van Essen, 1991, but see Self et al, 2013). We still know little about how one and the same feedforward sweep of neural activation across cortical layers entails multiple streams of stimulus-specific information that manifest differently across space and time
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