Anthony Maida

Blake Lemoine

BMC Neuroscience

English

PubMed

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GPU facilitated unsupervised visual feature acquisition

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POSTER PRESENTATION Open Access
GPU facilitated unsupervised visual feature
acquisition
Blake Lemoine*, Anthony Maida
From Twenty First Annual Computational Neuroscience Meeting: CNS*2012
Decatur, GA, USA. 21-26 July 2012
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 estab-
lished the effectiveness of hierarchical learning methods
that refine large quantities of elementary features into
small numbers of intermediate-level features. Some of
the most effective of these methods alternate between
template matching layers that combine features and sub-
sampling layers that perform feature scale and/or posi-
tion invariance. This approach is inspired by the mam-
malian ventral visual pathway. One such approach that
produces highly informative features is a spiking convo-
lutional network trained with STDP-based learning [1].
When using a serial implementation, however, this
approach is costly in time and consequently is difficult to
scale to a large library of internally complex features. The
present paper demonstrates that GPGPU parallelism can
be leveraged to overcome the scaling limitations of the
serial version. Highly informative features can then be
generated in large quantities. By scaling the number of
features learned the maximum complexity of learnable
classes is significantly increased.
Published: 16 July 2012
Reference
1. Masquelier T, Thorpe S: Unsupervised learning of visual features through
spike timing dependent plasticity. PLoS Computational Biology 2007, 3.
doi:10.1186/1471-2202-13-S1-P87
Cite this article as: Lemoine and Maida: GPU facilitated unsupervised
visual feature acquisition. BMC Neuroscience 2012 13(Suppl 1):P87.
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* Correspondence: bal2277@louisiana.edu
Center for Advanced Computer Studies, University of Louisiana, Lafayette, LA
70503, USA
Lemoine and Maida BMC Neuroscience 2012, 13(Suppl 1):P87
http://www.biomedcentral.com/1471-2202/13/S1/P87
© 2012 Lemoine and Maida; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.


Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Computational Biology

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GPU facilitated unsupervised visual feature acquisition

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