Identifying Spatio-temporal Dynamics in Interacting Brain Networks.

[摘要] Complex activity patterns in the brain arise from underlying interactions between neurons and astrocytes in the brain.Recent data acquisition has made it possible to measure the activity from many cells at one time. Extracting information from these data sources requires systems analysis techniques capable of describing network dynamics. Here I examine two broad approaches to understand interactions in brain networks.Astrocyte have recently been implicated in modulating neuron networks, but astrocyte signaling remains largely unknown. Astrocyte dynamics are examined by investigating the impact of distinct signaling pathways.We find that the interplay between the pathways is highly dependent upon the network topology, with cooperation being promoted by local networks and competition observed in random networks.Local networks are good approximations of biological astrocytes and are capable of reproducing the firing rate, shape, and spatial distribution of astrocyte network transients. This helps us to understand the way these networks develop and how they can be manipulated.The second way we look at activity patterns is by addressing some challenges provided by high dimensional data. Starting from modern data sets, specifically time series data, we identify functional relationships in neurons by quantifying consistent firing behaviors.We find that using just the spike times of neurons we can create a reliable functional connectivity measure without the need to invoke slow Monte Carlo sampling methods. We also look to measure changes in functional relationships which are believed to store memories within brain networks. Here we find that changes in functional network structure reflect learned behavior in mice.

[发布机构] University of Michigan

[效力级别] Physics

[关键词] Systems neuroscience approach to understanding interactions in brain networks;Physics;Science;Physics