Understanding Multimodal Interactions in Neuronal Networks

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The goal of this work is to elucidate evolving multimodal interactions between structural, dynamical, and functional network properties based on the interdependencies observed between neuronal and astrocytic networks acting on diverse spatial and temporal scales. Specifically we will investigate how dynamical and structural network characteristics interact on these different time scales to form evolving, functional neural ensembles. To achieve this goal we plan to combine computational modeling with experimental approaches that include calcium optical imaging, multi-electrode recordings, and structural labeling studies in primary neuronal cultures. This will allow for monitoring of multi-scale, simultaneous dynamical and structural changes in networks under different conditions. In particular we want to address the following questions: What properties of spatio-temporal patterning are mediated through fast and/or slow network interactions? How does network connectivity influence multimodal network activity? Whether and how do local network changes modify local patterns of fast and slow dynamics? Finally, we want to understand how the functions of these dynamical modes evolve during network development.Networks of interacting elements are ubiquitous in nature and man-made systems. The interactions in these networks happen on different spatial and temporal scales. Neural networks are a prime example of such a complex system. While interconnected neurons establish fast modes of communication, the slower astrocytes can modulate overall activity in the network that in turn will affect its connectivity structure and function. It becomes then pertinent to understand the interactions between these two modes of transmission and what their differential roles in network function are. This interdisciplinary project connects neurobiology with dynamical systems approaches to tackle these problems and can potentially have important impact on diverse fields. Identifying dynamical mechanisms underlying these interactions will allow for a better understanding of the network processes that underlie both cognitive tasks and brain pathologies: for example, on one hand structural network reconfiguration is crucial for memory formation but at the same time may drive formation of epileptic seizures. At the same time, the use of these dynamical modes for controlled network development may also lead to formulation of new strategies for network repair after injury. Finally, the insight obtained from this work could be generalized and applied to man-made networks that employ multi-scale temporal dynamics and could undergo functional reorganization online.This project will also provide opportunity for graduate and undergraduate students to experience interdisciplinary research connecting biology, physics and dynamical systems theory.