Development and use of an enhanced neuronal/silicon interface for the study of neuronal system dynamics.

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Brain function results from the propagation of information through networks of interconnected neurons. Glial cells provide physiological support for neurons but also directly modulate neuronal activity and thus contribute to the network structure. Moreover, glial cells are connected together in their own communication network that runs in parallel to, but is distinct from, the neuronal network. While neuronal networks have been intensely studied, the role of glial networks in modulating the flow of information through the neurons has not. In vitro neuronal systems have allowed us to investigate the underlying mechanisms of neural circuits, and from this has come the study of emergent complex behaviors in the firing patterns of ensembles of interconnected neurons. My research program aims at further development of the technology to acquire high spatial and temporal resolution recordings of both neuronal and glial activity simultaneously, and to then to use novel analysis techniques to determine the relationship between the two. My research goals are: Aim 1) To enhance the neuronal/silicon device developed in my first DG with activity detection features which will allow complex activity patterns in both neurons and glial cells to be recorded. This will involve incorporation of a high resolution CCD chip with a 200 fps frame rate, coupled to a built in light source, detecting light emitted by fluorescent sensors when they are triggered by neuronal or glial events. This will allow the extended acquisition of neuronal and glial activity data of sufficient spatial and temporal resolution to perform multi-layer analysis of network structure. Aim 2) To perform dynamical systems analysis to determine how glial activity patterns change the flow of information through the neuronal network. To date, no technique has existed to estimate how these populations communicate at a large scale. To address the relationship between the neuronal and glial networks we will identify causal interactions between the two by extending techniques from Information Theory, in particular Transfer Entropy. Ultimately the goal will be to apply Causal Deep Learning, a method that combines Information Theory and Deep Learning, to infer neuronal and glial connectivity. Aim 3) Incorporate surface plasmon resonance (SPR) nanoparticle (NP) amplification of photonic signals in our detection methodology. The use of SPR in the design of biosensors has been under intense development for the past decade. My lab has developed a lipid encapsulation technique for delivering silver NPs to living cultured neurons, and preliminary results suggest they can be used for detection of changes in membrane potential. This project also provides a potential strategy to detect biophotonic emission from neural tissue, a long pursued hypothesis in the realm of quantum neuroscience. Development of these technologies will advance our understanding of complex neuronal systems, and ultimately, the brain.