The cortical column is one of the fundamental computational circuits in the brain. In order to understand the role neurons in different layers of this circuit play in cortical function it is necessary to identify the boundaries that separate the laminar compartments. While histological approaches can reveal ground truth they are not a practical means of identifying cortical layers in vivo. The gold standard for identifying laminar compartments in electrophysiological recordings is current-source density (CSD) analysis. However, laminar CSD analysis requires averaging across reliably evoked responses that target the input layer in cortex, which may be difficult to generate in less well studied cortical regions. Further the analysis can be susceptible to noise on individual channels resulting in errors in assigning laminar boundaries. Here, we have analyzed linear array recordings in multiple cortical areas in both the common marmoset and the rhesus macaque. We describe a pattern of laminar spike-field phase relationships that reliably identifies the transition between input and deep layers in cortical recordings from multiple cortical areas in two different non-human primate species. This measure corresponds well to estimates of the location of the input layer using CSDs, but does not require averaging or specific evoked activity. Laminar identity can be estimated rapidly with as little as a minute of ongoing data and is invariant to many experimental parameters. This method may serve to validate CSD measurements that might otherwise be unreliable or to estimate laminar boundaries when other methods are not practical.

Understanding cortical microcircuitry requires theoretical models that can tease apart their computational logic from biological details. Although Bayesian inference serves as an abstract framework of cortical computation, precisely mapping concrete instantiations of computational models to biology under real-world tasks is necessary to produce falsifiable neural models. On the basis of a recent generative model, recursive cortical networks, that demonstrated excellent performance on vision benchmarks, we derive a theoretical cortical microcircuit by placing the requirements of the computational model within biological constraints. The derived model suggests precise algorithmic roles for the columnar and laminar feed-forward, feedback, and lateral connections, the thalamic pathway, blobs and interblobs, and the innate lineage-specific interlaminar connectivity within cortical columns. The model also explains several visual phenomena, including the subjective contour effect and neon-color spreading effect, with circuit-level precision. Our model and methodology provides a path forward in understanding cortical and thalamic computations.

Neocortex, the neuronal structure at the base of the remarkable cognitive skills of mammals, is a layered sheet of neuronal tissue composed of juxtaposed and interconnected columns. A cortical column is considered the basic module of cortical processing present in all cortical areas. It is believed to contain a characteristic microcircuit composed of a few thousand neurons. The high degree of cortical segmentation into vertical columns and horizontal layers is a boon for scientific investigation because it eases the systematic dissection and functional analysis of intrinsic as well as extrinsic connections of the column. In this review we will argue that in order to understand neocortical function one needs to combine a microscopic view, elucidating the workings of the local columnar microcircuits, with a macroscopic view, which keeps track of the linkage of distant cortical modules in different behavioral contexts. We will exemplify this strategy using the model system of vibrissal touch in mice and rats. On the macroscopic level vibrissal touch is an important sense for the subterranean rodents and has been honed by evolution to serve an array of distinct behaviors. Importantly, the vibrissae are moved actively to touch - requiring intricate sensorimotor interactions. Vibrissal touch, therefore, offers ample opportunities to relate different behavioral contexts to specific interactions of distant columns. On the microscopic level, the cortical modules in primary somatosensory cortex process touch inputs at highest magnification and discreteness - each whisker is represented by its own so-called barrel column. The cellular composition, intrinsic connectivity and functional aspects of the barrel column have been studied in great detail. Building on the versatility of genetic tools available in rodents, new, highly selective and flexible cellular and molecular tools to monitor and manipulate neuronal activity have been devised. Researchers have started to combine these with advanced and highly precise behavioral methods, on par with the precision known from monkey preparations. Therefore, the vibrissal touch model system is exquisitely positioned to combine the microscopic with the macroscopic view and promises to be instrumental in our understanding of neocortical function.