Functional structure of cortical neuronal networks grown in vitro

• Published in: Physical review. E, Statistical, nonlinear, and soft matter physics Vol. 75; no. 2 Pt 1; p. 021915
• Main Authors: Bettencourt, Luís M A, Stephens, Greg J, Ham, Michael I, Gross, Guenter W
• Format: Journal Article
• Language: English
• Published: United States 01.02.2007
• Subjects: Action Potentials - physiology; Animals; Biological Clocks - physiology; Cells, Cultured; Cerebral Cortex - cytology; Cerebral Cortex - physiology; Computer Simulation; Feedback - physiology; Mice; Models, Neurological; Nerve Net - physiology; Neurons - cytology; Neurons - physiology; Synaptic Transmission - physiology
• ISSN: 1539-3755
• DOI: 10.1103/PhysRevE.75.021915

We apply an information-theoretic treatment of action potential time series measured with microelectrode arrays to estimate the connectivity of mammalian neuronal cell assemblies grown in vitro. We infer connectivity between two neurons via the measurement of the mutual information between their spike trains. In addition we measure higher-point multi-information between any two spike trains, conditional on the activity of a third cell, as a means to identify and distinguish classes of functional connectivity among three neurons. The use of a conditional three-cell measure removes some interpretational shortcomings of the pairwise mutual information and sheds light on the functional connectivity arrangements of any three cells. We analyze the resultant connectivity graphs in light of other complex networks and demonstrate that, despite their ex vivo development, the connectivity maps derived from cultured neural assemblies are similar to other biological networks and display nontrivial structure in clustering coefficient, network diameter, and assortative mixing. Specifically we show that these networks are weakly disassortative small-world graphs, which differ significantly in their structure from randomized graphs with the same degree. We expect our analysis to be useful in identifying the computational motifs of a wide variety of complex networks, derived from time series data.