Hierarchical organization of macaque and cat cortical sensory systems explored with a novel network processor

Abstract

Neuroanatomists have described a large number of connections between the various structures of monkey and cat cortical sensory systems. Because of the complexity of the connection data, analysis is required to unravel what principles of organization they imply. To date, analysis of laminar origin and termination connection data to reveal hierarchical relationships between the cortical areas has been the most widely acknowledged approach. We programmed a network processor that searches for optimal hierarchical orderings of cortical areas given known hierarchical constraints and rules for their interpretation. For all cortical systems and all cost functions, the processor found a multitude of equally low-cost hierarchies. Laminar hierarchical constraints that are presently available in the anatomical literature were therefore insufficient to constrain a unique ordering for any of the sensory systems we analysed. Hierarchical orderings of the monkey visual system that have been widely reported, but which were derived by hand, were not among the optimal orderings. All the cortical systems we studied displayed a significant degree of hierarchical organization, and the anatomical constraints from the monkey visual and somato-motor systems were satisfied with very few constraint violations in the optimal hierarchies. The visual and somato-motor systems in that animal were therefore surprisingly strictly hierarchical. Most inconsistencies between the constraints and the hierarchical relationships in the optimal structures for the visual system were related to connections of area FST (fundus of superior temporal sulcus). We found that the hierarchical solutions could be further improved by assuming that FST consists of two areas, which differ in the nature of their projections. Indeed, we found that perfect hierarchical arrangements of the primate visual system, without any violation of anatomical constraints, could be obtained under two reasonable conditions, namely the subdivision of FST into two distinct areas, whose connectivity we predict, and the abolition of at least one of the less reliable rule constraints. Our analyses showed that the future collection of the same type of laminar constraints, or the inclusion of new hierarchical constraints from thalamocortical connections, will not resolve the problem of multiple optimal hierarchical representations for the primate visual system. Further data, however, may help to specify the relative ordering of some more areas. This indeterminacy of the visual hierarchy is in part due to the reported absence of some connections between cortical areas. These absences are consistent with limited cross-talk between differentiated processing streams in the system. Hence, hierarchical representation of the visual system is affected by, and must take into account, other organizational features, such as processing streams.

Learning and inference in the brain

“…On the basis of computational neuroanatomic studies, Hilgetag, O'Neill, and Young (2000) conclude laminar hierarchical constraints that are presently available in the anatomical literature are "insufficient to constrain a unique ordering" for any of the sensory systems analysed. However, basic hierarchical principles were clearly evident.…”

Organization, development and function of complex brain networks

“…Topological sequences of areas might provide the layout for signaling pathways across cortical networks [39]. Alternatively, hierarchies of cortices can be constructed, based on the laminar origin and termination patterns of interconnections [23,40].…”

Mapping the human connectome at multiple scales with diffusion spectrum MRI

“…More recently it became clear that beyond the aim of characterizing individual fiber bundles, the connectivity profile of the entire brain is of highest importance in neuroscience. Following the pioneering work based on chemical tracing of (Felleman and Van Essen, 1991) and others (Hilgetag and Kaiser, 2004;Hilgetag et al, 2000;Sporns and Zwi, 2004), similar connection matrices have been built from MRI tractography, either by constructing large-scale networks of 1000 nodes (Hagmann et al, 2007) or more anatomically based connection matrices (Gong et al, 2009(Gong et al, , 2008Iturria-Medina et al, 2007Li et al, 2009;Thottakara et al, 2006). Diffusionbased connectivity has also been used in some studies (Behrens and Johansen-Berg, 2005;Klein et al, 2007;Tomassini et al, 2007) to parcellate gray matter.…”