부산대학교 도서관

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jphysparis.2009.05.006 Byline: Stefan Haeusler, Klaus Schuch, Wolfgang Maass Keywords: Cortical layers; Lamination; Microcircuit Models; Real-Time Computations; Network Motifs; Circuit dynamics Abstract: 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. and Binzegger et al., both resulting from long-lasting experimental studies (but based on different methods). We analyze and compare in this article the structure of these two microcircuit templates. In particular, we examine the distribution of network motifs, i.e. of subcircuits consisting of a small number of neurons. The distribution of these building blocks 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 have a characteristic small-world property. In order to understand the dynamical and computational properties of these 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 performance of these two cortical microcircuit models was studied for seven 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 common key factor for their computational performance. We also show that their computational performance is correlated with specific statistical properties of the circuit dynamics that is induced by a particular distribution of degrees of nodes. Author Affiliation: Institute for Theoretical Computer Science, Graz University of Technology, A-8010 Graz, Austria Article Note: (footnote) [star] Written under partial support by the Austrian Science Fund FWF # S9102-N13 and project # FP6-015879(FACETS), project # FP7-216593 (SECO), project # FP7-506778 (PASCAL2) as well as project # FP7-231267 (ORGANIC) of the European Union.