UC Irvine

In this dissertation, I set out to develop novel methodologies for the evolution and evaluation of spiking neural networks. This series of studies involved the use of GPU- accelerated, parallelized evolutionary algorithms. The project was intended to aid collaboration efforts between theoretical and experimental neuroscientists, who often spend tremendous time and money developing experiments that may not provide useful results. It was also intended to develop a veridical way of modeling neural systems by matching experimentally observed neurophysiological data. The networks evolve such that higher-order features of the region, such as functional behavior and population coding, emerge by virtue of replicated firing patterns. In the first study, I developed the automated tuning framework and applied it to a case study using a dataset recorded from rat retrosplenial cortex. The framework successfully takes as input the recorded behavioral metrics associated with neuronal firing patterns which are encoded by idealized input neurons and evolves a generic spiking neural network to match the data by correlating the synthetic neuronal response patterns with the experimentally observed ones. In the second study, I developed a virtual testbed to evaluate evolved models of cognitive function. Within the framework, novel experimental designs can be simulated and model response patterns can be recorded. By simulating experiments such as lesioning of the network and manipulation of behavioral inputs, new predictions can be made about the function of the brain region, and new experiments to probe that function can be designed without expending unnecessary time and effort on the part of experimentalists. In the final study, I link spike-timing plasticity to dimensionality reduction in the brain by applying a statistical algorithm known as nonnegative matrix factorization (NMF) to the same dataset. I demonstrate that similar results, and a similar model of RSC functionality, can be achieved simply through nonnegative and parts-based dimensionality reduction, and propose that nonnegative sparse coding may be a canonical computation performed by plasticity rules in the brain to handle high-dimensional input spaces.