Motif distribution, dynamical properties, 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. 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.
Reference: S. Haeusler, K. Schuch, and W. Maass.
Motif distribution, dynamical properties, and computational performance of two
data-based cortical microcircuit templates.
J. of Physiology (Paris), 103(1-2):73-87, 2009.