A statistical analysis of information-processing properties of lamina-specific cortical microcircuit models

A major challenge for computational neuroscience is to understand the computational function of lamina-specific synaptic connection patterns in stereotypical cortical microcircuits. Previous work on this problem had focused on hypothesized specific computational roles of individual layers and connections between layers and had tested these hypotheses through simulations of abstract neural network models. We approach this problem by studying instead the dynamical system defined by more realistic cortical microcircuit models as a whole and by investigating the influence that its laminar structure has on the transmission and fusion of information within this dynamical system. The circuit models that we examine consist of Hodgkin-Huxley neurons with dynamic synapses, based on detailed data from Thomson and others (2002), Markram and others (1998), and Gupta and others (2000). We investigate to what extent this cortical microcircuit template supports the accumulation and fusion of information contained in generic spike inputs into layer 4 and layers 2/3 and how well it makes this information accessible to projection neurons in layers 2/3 and layer 5. We exhibit specific computational advantages of such data-based lamina-specific cortical microcircuit model by comparing its performance with various types of control models that have the same components and the same global statistics of neurons and synaptic connections but are missing the lamina-specific structure of real cortical microcircuits. We conclude that computer simulations of detailed lamina-specific cortical microcircuit models provide new insight into computational consequences of anatomical and physiological data.