Slowness and Sparseness Have Diverging Effects on Complex Cell Learning

8Citations
Citations of this article
47Readers
Mendeley users who have this article in their library.

Abstract

Following earlier studies which showed that a sparse coding principle may explain the receptive field properties of complex cells in primary visual cortex, it has been concluded that the same properties may be equally derived from a slowness principle. In contrast to this claim, we here show that slowness and sparsity drive the representations towards substantially different receptive field properties. To do so, we present complete sets of basis functions learned with slow subspace analysis (SSA) in case of natural movies as well as translations, rotations, and scalings of natural images. SSA directly parallels independent subspace analysis (ISA) with the only difference that SSA maximizes slowness instead of sparsity. We find a large discrepancy between the filter shapes learned with SSA and ISA. We argue that SSA can be understood as a generalization of the Fourier transform where the power spectrum corresponds to the maximally slow subspace energies in SSA. Finally, we investigate the trade-off between slowness and sparseness when combined in one objective function. © 2014 Lies et al.

Cite

CITATION STYLE

APA

Lies, J. P., Häfner, R. M., & Bethge, M. (2014). Slowness and Sparseness Have Diverging Effects on Complex Cell Learning. PLoS Computational Biology, 10(3). https://doi.org/10.1371/journal.pcbi.1003468

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free