Higher-level functions, such as visual object recognition or memory, emerge from interactions within the brain's neural networks. The ability to determine the patterns of connections among neurons would facilitate discovering how such computations are performed. Because many connectivity patterns could theoretically produce neural output with only subtle differences, characterizing even basic circuitry elements within the brain remains a formidable challenge. The goal of this project is to develop and analyze mathematical models of neuronal networks in order to design tools that can distinguish among network configurations. Preliminary studies have demonstrated a new approach that shows promise of leading to tools to infer connectivity patterns in spiking neuronal networks. The key idea motivating this approach is to exploit models of neural response to an experimentally controlled stimulus in order to analyze neuron output. The explicit structure imposed by mathematical models helps overcome a major difficulty in estimating network connectivity from experiments: the fact that only a small subset of neurons can be measured simultaneously. Hence, the central challenge addressed by this project is accounting for the effects of unmeasured neurons. Mathematical models lead to predictions of the effects of connections from unmeasured neurons. The aim of the work is to use these predictions to factor out the effects of unmeasured neurons and better estimate the connectivity among the measured neurons. The resulting tools help neuroscientists understand local circuits that underlie neural processing and function within the brain.
The goal of this work is to develop mathematical tools that neuroscientists can use to determine how neurons in the brain are connected to each other. Understanding how neurons are connected is an important first step toward discovering how neurons communicate with each other to process information. Such knowledge could help scientists better understand the effects of the degradation of such connections, such as in neurodegenerative diseases, and devise strategies to mitigate or reverse these effects.
