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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol.5(5): 426-430 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.546

Response Properties of Single Neurons Predicted by Sparse Representation

Jiqian Liu, Chengbin Zeng, and Liping Xiao
Abstract—Sparse representation by neuronal populations in sensory cortex has been heavily investigated and validated. Recently, it was reported that sparse representation can predict response properties of single neurons, which gives insight into the unified understanding of the responses of both neuronal populations and single neurons. The current work takes a step forward in this regard. We simulate the response properties of simple cells with a neurally plausible sparse representation model. The model turns out to explain well the response properties of single cortical neurons, such as the sharp and contrast invariant orientation tuning as well as cross orientation suppression within the classical receptive field (CRF) and surround suppression from beyond the CRF.

Index Terms—Sparse representation, orientation selectivity, cross orientation suppression, surround suppression.

Jiqian Liu and Chengbin Zeng are with the School of Information Engineering, Guizhou Institute of Technology, Guiyang, 550003, P.R. China (e-mail: liujiqian@git.edu.cn).
Liping Xiao is with School of Mining Engineering, Guizhou Institute of Technology.


Cite: Jiqian Liu, Chengbin Zeng, and Liping Xiao, "Response Properties of Single Neurons Predicted by Sparse Representation," International Journal of Machine Learning and Computing vol.5, no. 5, pp. 426-430, 2015.

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