The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Sparse Coding via Thresholding and Local Competition in Neural Circuits
Abstract
While evidence indicates that neural systems may be employing sparse approximations to represent sensed stimuli, the mechanisms underlying this ability are not understood. We describe a locally competitive algorithm (LCA) that solves a collection of sparse coding principles minimizing a weighted combination of mean-squared error and a coefficient cost function. LCAs are designed to be implemented in a dynamical system composed of many neuron-like elements operating in parallel. These algorithms use thresholding functions to induce local (usually one-way) inhibitory competitions between nodes to produce sparse representations. LCAs produce coefficients with sparsity levels comparable to the most popular centralized sparse coding algorithms while being readily suited for neural implementation. Additionally, LCA coefficients for video sequences demonstrate inertial properties that are both qualitatively and quantitatively more regular (i.e., smoother and more predictable) than the coefficients produced by greedy algorithms.
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Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
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“In order for a sparse sensory input to perturb a network, there must be sufficient network interconnectivity such that alterations in the activity of a few neurons can spread to other neurons in the network (Laurent, 2002; Rozell et al., 2008).”
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Metadata
- Authors
- Rozell, C. J., Johnson, D. H., Baraniuk, R. G., & Olshausen, B. A.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 2008-10
- Volume
- 20
- Issue
- 10
- Pages
- 2526-2563
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2100543212
- Cited by
- 456
- References
- 2
- FWCI
- 17.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- Rozell, C. J., Johnson, D. H., Baraniuk, R. G., & Olshausen, B. A. (2008). Sparse Coding via Thresholding and Local Competition in Neural Circuits. Neural Computation. https://doi.org/10.1162/neco.2008.03-07-486
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