Hierarchical Bayesian cortical models: Analysis and acceleration on multicore architectures

There is a significant interest in the research community to develop large scale, high performance implementations of neuromorphic models. These have the potential to provide significantly stronger information processing capabilities than current computing algorithms. This thesis examines the parallelization of two recent biologically inspired hierarchical Bayesian cortical models onto recent multicore architectures. These models have been developed recently based on new insights from neuroscience and have several advantages over traditional neural networks. In particular, they need far fewer network nodes to simulate a large scale cortical model than traditional neural networks, making them computationally more efficient. This is the first study of the parallelization of this class of models onto multicore processors. Results indicate that the models can take advantage of parallelism present in the processors to provide significant speedups on multicore architectures. These models are further shown to scale well on a cluster of 336 PS3s available at the Air Force Research Lab which is shown to emulate between 108 to 1010 neurons. In particular, the results indicate that a cluster of Playstation 3s can provide an economical, yet powerful, platform for simulating large scale neuromorphic models.