Information theoretic visualization of spiking neural networks

Visualization is a flexible way to analyze simulated data and serves as a means for scientific discovery. Large scale neural simulations using high performance and distributed computing techniques produce huge amount of data for which visual analysis is generally difficult to perform. In this paper, a spiking neuron simulation environment was created to model and simulate networks of neurons of the cerebellum. Traditional visualization techniques were used to highlight relevant findings from small scale cerebellar networks. Time varying volume visualization using traditional techniques was found infeasible as network size increased. New data abstractions were required to depict the data that changes over time. With large scale cerebellar networks, Information theoretic methods were used to reduce dimensionality and to extract valuable information from data. We suggested that, information theory can be used as an efficient scientific data analysis and visualization tool to evaluate and validate computational models of cerebellar like structures.