title: Structure, Function, and Propagation of Information across Living Two, Four, and Eight Node Degree Topologies.
abstract: In this study, we created four network topologies composed of living cortical neurons and compared resultant structural-functional dynamics including the nature and quality of information transmission. Each living network was composed of living cortical neurons and were created using microstamping of adhesion promoting molecules and each was "designed" with different levels of convergence embedded within each structure. Networks were cultured over a grid of electrodes that permitted detailed measurements of neural activity at each node in the network. Of the topologies we tested, the "Random" networks in which neurons connect based on their own intrinsic properties transmitted information embedded within their spike trains with higher fidelity relative to any other topology we tested. Within our patterned topologies in which we explicitly manipulated structure, the effect of convergence on fidelity was dependent on both topology and time-scale (rate vs. temporal coding). A more detailed examination using tools from network analysis revealed that these changes in fidelity were also associated with a number of other structural properties including a node's degree, degree-degree correlations, path length, and clustering coefficients. Whereas information transmission was apparent among nodes with few connections, the greatest transmission fidelity was achieved among the few nodes possessing the highest number of connections (high degree nodes or putative hubs). These results provide a unique view into the relationship between structure and its affect on transmission fidelity, at least within these small neural populations with defined network topology. They also highlight the potential role of tools such as microstamp printing and microelectrode array recordings to construct and record from arbitrary network topologies to provide a new direction in which to advance the study of structure-function relationships.
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida , Gainesville, FL , USA.
authors.forename: Sankaraleengam
authors.initials: S
authors.lastname: Alagapan
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida , Gainesville, FL , USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Franca
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida , Gainesville, FL , USA.
authors.forename: Liangbin
authors.initials: L
authors.lastname: Pan
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida , Gainesville, FL , USA.
authors.forename: Stathis
authors.initials: S
authors.lastname: Leondopulos
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Department of Biomedical Engineering, University of California San Diego, San Diego, CA, USA.
authors.forename: Bruce C
authors.initials: BC
authors.lastname: Wheeler
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Department of Pediatric Neurology, University of Florida, Gainesville, FL, USA.
authors.forename: Thomas B
authors.initials: TB
authors.lastname: DeMarse
keywords: convergence–divergence; cortical networks; graph theory; information transmission; structure–function