My collaborator Jorge Mejias is presenting a poster at the 2015 Society for Neuroscience meeting, on Monday, October 18, in the afternoon session. Our poster is titled: Building a large-scale cortical network model incorporating laminar structure: frequency-specific feedforward and feedback interactions. Our abstract is below:
A major challenge in the development of large-scale cortical network models is to implement area-to-area interactions allowing elucidation of dynamical operations in a biologically constrained manner. These interactions are anatomically organized in a laminar specific manner: feedforward projections preferentially stem from superficial layers, whereas feedback projections originate chiefly from deep layers. Feedforward interactions transmit sensory information to higher brain areas, while feedback interactions may mediate a prediction/expectation signal or top-down attentional modulation of early sensory areas. Quantitative data on laminar-dependent inter-areal connectivity of the macaque cortex have become available only recently. We have incorporated these data in a large-scale dynamical model of the primate cortex endowed with weighted and directed connectivity. Each cortical area is modeled with a superficial and a deep layer. Based on recent physiological evidence, we modeled excitatory and inhibitory neural populations in each layer, with local properties that generate noisy gamma oscillations in the superficial layer and alpha oscillations in the deep layer. Furthermore, the interactions between the two layers are guided by anatomical and physiological data, with specific superficial-to-deep and deep-to-superficial projections that allow to explain experimental observations of phase-amplitude coupling. We calibrated the model by simulating physiological observations that feedforward interactions are associated with oscillations in the gamma band (40-80Hz), while feedback interactions relate to lower frequencies, in the alpha or low beta frequency range (8-20 Hz). Using Granger causality to establish the directionality of information flow, the model reproduces the observed functional hierarchical order of visual areas (Bastos et al. Neuron 2015). The model identifies several properties of feedback projections as a key factor to explain these hierarchical dynamics, in particular the specific pattern of feedback projections to a target area. We further discuss the usefulness of functional hierarchies to reconstruct structural properties of cortical connectivity. Our results represent a step forward in the advance of a quantitative model of the primate large-scale cortical system.