Motif distribution and computational performance of two data-based cortical microcircuit templates

S. Haeusler, K. Schuch, and W. Maass


The neocortex is a continuous sheet composed of rather stereotypical local microcircuits that consist of neurons on several laminae with characteristic synaptic connectivity patterns. An understanding of the structure and computational function of these cortical microcircuits may hold the key for understanding the enormous computational power of the neocortex. Two templates for the structure of laminar cortical microcircuits have recently been published by Thomson et al. (2002) and Binzegger et al. (2004), both resulting from long-lasting experimental studies (but based on different methods). We analyze and compare in this study the structure and computational properties of these two microcircuit templates. In particular, we examine the distribution of network motifs, i.e. of sub-circuits consisting of a small number of neurons. The distribution of these building blocks of complex networks has recently emerged as a method for characterizing similarities and differences among complex networks. We show that the two microcircuit templates have quite different distributions of network motifs, although they both share characteristic global structural properties, like degree distributions (distribution of the number of synapses per neuron) and small-world properties. In order to understand the computational properties of the two microcircuit templates, we have generated computer models of them, consisting of Hodgkin-Huxley point neurons with conductance based synapses that have a biologically realistic short-term plasticity. The information processing capabilities of the two cortical microcircuit models were studied for 7 generic computational tasks that require accumulation and merging of information contained in two afferent spike inputs. Although the two models exhibit a different performance for some of these tasks, their average computational performance is very similar. When we changed the connectivity structure of these two microcircuit models in order to see which aspects of it are essential for computational performance, we found that the distribution of degrees of nodes is a key factor for their computational performance. References Thomson et al. (2002), Cerebral Cortex, 12(9):936 Binzegger et al. (2004), J. Neurosci., 24(39):8441

Reference: S. Haeusler, K. Schuch, and W. Maass. Motif distribution and computational performance of two data-based cortical microcircuit templates. 38th Annual Conference of the Society for Neuroscience, Program 220.9, 2008.