BibTeX
@MISC{Hilk12gpgpu-basedcortical,
author = {Theodore Hilk},
title = {GPGPU-BASED CORTICAL MODELING},
year = {2012}
}
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Abstract
Cortical modeling is an area of research seeking to model and simulate the cerebral cortex of the brain, which is of fundamental importance to conscious thought and action. Computational power is a major challenge in this eld and the problem is inherently well-suited to SIMD architectures. This suggests the implementation of a general-purpose GPU framework for the development and execution of cortical models, and indeed, several such frameworks do exist. However, they suffer from hardware and software vendor lock-in and unnecessary assumptions that limit the generality of the models they can execute. In order to overcome these obstacles in preparation for anticipated future work by the author and others, we have implemented a new cortical modeling framework in OpenCL using PyOpenCL. The new framework has several notable advantages: it is open-source, it does not suffer from hardware or software vendor lock-in, it is cross-platform compatible, and in principle it can simulate any model expressed as a message-passing architecture
Keyphrases
gpgpu-based cortical modeling fundamental importance several notable advantage computational power simd architecture software vendor lock-in several framework cortical modeling anticipated future work conscious thought cortical model new cortical modeling framework software vendor unnecessary assumption general-purpose gpu framework new framework cerebral cortex message-passing architecture major challenge
