| Summary: | 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 that 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 versus temporal coding). At long time scales associated with rate based coding the simple linear 2D or line networks produced the highest fidelity during transmission among neurons (nodes) followed by 4D (4 connections per node) and 8D (8 connections per node) grid networks. By contrast, at more precise temporal scales it was the increased convergence within the 8D topology that now resulted in the highest fidelity followed by 4D and 2D networks. 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.
|
|---|
