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Hierarchical Bayesian Cortical Models: Analysis And Acceleration On Multicore Architectures, Pavan Yalamanchili

All Theses

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 …