Embedding of Cortical Representations by the Superficial Patch System

Abstract

Pyramidal cells in layers 2 and 3 of the neocortex of many species collectively form a clustered system of lateral axonal projections (the superficial patch system—Lund JS, Angelucci A, Bressloff PC. 2003. Anatomical substrates for functional columns in macaque monkey primary visual cortex. Cereb Cortex. 13:15–24. or daisy architecture—Douglas RJ, Martin KAC. 2004. Neuronal circuits of the neocortex. Annu Rev Neurosci. 27:419–451.), but the function performed by this general feature of the cortical architecture remains obscure. By comparing the spatial configuration of labeled patches with the configuration of responses to drifting grating stimuli, we found the spatial organizations both of the patch system and of the cortical response to be highly conserved between cat and monkey primary visual cortex. More importantly, the configuration of the superficial patch system is directly reflected in the arrangement of function across monkey primary visual cortex. Our results indicate a close relationship between the structure of the superficial patch system and cortical responses encoding a single value across the surface of visual cortex (self-consistent states). This relationship is consistent with the spontaneous emergence of orientation response–like activity patterns during ongoing cortical activity (Kenet T, Bibitchkov D, Tsodyks M, Grinvald A, Arieli A. 2003. Spontaneously emerging cortical representations of visual attributes. Nature. 425:954–956.). We conclude that the superficial patch system is the physical encoding of self-consistent cortical states, and that a set of concurrently labeled patches participate in a network of mutually consistent representations of cortical input.

Selective interhemispheric circuits account for a cardinal bias in spontaneous activity within early visual areas

“…A close relationship between the anatomical configuration of that patch system as determined by tracer injections, and the structure of maps acquired through visual stimulation has been suggested (Muir et al, 2011). Along this line, also spontaneous maps like the ones found by Kenet et al (2003) have been associated with the architecture of long-range intra-and corticocortical intrinsic and/or feedback connections.…”

“…A previous study (Peiker et al, 2013) pointed towards an anisotropy of selective networks interconnecting HN and VN in the vicinity of the 17/18 border (for review see Schmidt, 2016 According to a recent modeling study the patchy intrinsic network would provide "a physical encoding for statistical properties of the modality represented in an area of cortex" (Muir et al, 2011). Indeed, statistical properties of natural scenes as viewed by cats exhibit a bias for cardinal with prevalence for horizontal contours (Betsch et al, 2004).…”

“…orientation preference (Gilbert and Wiesel, 1989;Malach et al, 1993;Bosking et al, 1997;Schmidt et al, 1997b;Stettler et al, 2002). Accordingly, ongoing population activity in cortex would be biased toward states that reflect the spatial configuration of that patterned connections (Kenet et al, 2003;Muir et al, 2011).…”

Eigenspectrum bounds for semirandom matrices with modular and spatial structure for neural networks

“…For example, in the primary visual cortex of many mammals the response of a neuron to a set of drifting grating stimuli can be highly tuned to the orientation or direction of grating drift [34]. In many mammalian species, neurons (or small regions of the cortex) that have similar functional metrics are more likely to be connected (mouse, [21]; cat, [15,22]; tree shrew, [19]; monkey, [17,18,22]; human, [16]). For simplicity, we consider that these functional dimensions are treated similarly to spatial dimensions, so that a node is assigned a "location" in a high-dimensional space that defines its spatial and functional properties.…”

“…However, connections in physical systems such as the mammalian neocortex are not made randomly [15][16][17][18][19][20][21][22] and include spatial constraints, and so these theorems are insufficient for models with more cortically realistic architecture. Here we propose two alternative rule-based methods for generating matrices corresponding to systems with parameterized nonrandom interactions, and determine bounds on the eigenvalue spectra of these matrices.…”