Blake Lemoine

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—This paper demonstrates that feature acquisition systems composed of spiking neurons trained by spike–timing– dependent plasticity (STDP) can effectively be scaled using General-Purpose computing on Graphics Processing Units (GPGPU). While previous studies have demonstrated this for classes with low intra–class variability, parallelization substantially(More)
This paper examines the effects of parallelizing an unsu-pervised feature acquisition algorithm using General-Purpose computing on Graphics Processing Units (GPGPU). Previous work in feature learning has established the effectiveness of hierarchical learning methods that refine large quantities of elementary features into small numbers of intermediate-level(More)
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