Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 10, Issue 3, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1003468
Keywords
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Funding
- Max Planck Society
- German Ministry of Education, Science, Research and Technology through the Bernstein award (BMBF) [FKZ: 01GQ0601]
- Bernstein Center for Computational Neuroscience, Tuebingen (BMBF) [FKZ: 01GQ1002]
- German Excellency Initiative through the Centre for Integrative Neuroscience Tubingen [EXC307]
- Swartz Foundation
- Deutsche Forschungsgemeinschaft
- Open Access Publishing Fund of Tuebingen University
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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.
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