Topology and Network Structure in Sparsely Sampled Neural Systems

We apply a method of topological analysis on spiking correlation networks in neurological systems with data measured using micro-electrode arrays (MEAs) and one- and two-photon microscopy. From these real-world, sparsely sampled biological networks, we extract connectivity using pairwise spike-timing correlations, which are optimized for time delays introduced by synaptic communication. In addition to performing a complete suite of standard network measures such as strength, clustering, and path length distributions, we analyze network topology to identify emergent structures and compare the results to two randomized control models. Through this approach, we aim to determine which network and topological descriptors are robust to sampling limitations and can capture biologically meaningful organization. We will further assess whether these network and topological features are significant enough to differentiate between datasets collected under distinct experimental conditions, such as similar neural systems with unique mutations. By applying topological analysis to sparsely sampled neural networks, we demonstrate both the potential and the limitations of this framework for interpreting experimentally-detected neural activity across complex systems.