Relating sparse and predictive coding to divisive normalization

Sparse coding, predictive coding and divisive normalization have each been found to be principles that underlie the function of neural circuits in many parts of the brain, supported by substantial experimental evidence. However, the connections between these related principles are still poorly under...

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Veröffentlicht in:	PLoS computational biology Jg. 21; H. 5; S. e1013059
Hauptverfasser:	Lian, Yanbo, Burkitt, Anthony N.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 01.05.2025 Public Library of Science (PLoS)
Schlagworte:	Analysis Animals Biology and Life Sciences Brain Brain - physiology Coding Computational Biology Computer and Information Sciences Computer Simulation Feedback Homeostasis Humans Learning Learning - physiology Mathematical models Medicine and Health Sciences Models, Neurological Nerve Net - physiology Neural circuitry Neural networks Neurological research Neurons - physiology Nonlinear systems Nonlinearity Primary Visual Cortex - physiology Social Sciences Somatosensory cortex Sparsity Visual cortex Visual Cortex - physiology Visual pathways Visual perception Australia
ISSN:	1553-7358, 1553-734X, 1553-7358
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Zusammenfassung:	Sparse coding, predictive coding and divisive normalization have each been found to be principles that underlie the function of neural circuits in many parts of the brain, supported by substantial experimental evidence. However, the connections between these related principles are still poorly understood. Sparse coding and predictive coding can be reconciled into a learning framework with predictive structure and sparse responses, termed as sparse/predictive coding. However, how sparse/predictive coding (a learning model) is connected with divisive normalization (not a learning model) is still not well investigated. In this paper, we show how sparse coding, predictive coding, and divisive normalization can be described within a unified framework, and illustrate this explicitly within the context of a two-layer neural learning model of sparse/predictive coding. This two-layer model is constructed in a way that implements sparse coding with a network structure that is constructed by implementing predictive coding. We demonstrate how a homeostatic function that regulates neural responses in the model can shape the nonlinearity of neural responses in a way that replicates different forms of divisive normalization. Simulations show that the model can learn simple cells in the primary visual cortex with the property of contrast saturation, which has previously been explained by divisive normalization. In summary, the study demonstrates that the three principles of sparse coding, predictive coding, and divisive normalization can be connected to provide a learning framework based on biophysical properties, such as Hebbian learning and homeostasis, and this framework incorporates both learning and more diverse response nonlinearities observed experimentally. This framework has the potential to also be used to explain how the brain learns to integrate input from different sensory modalities.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1013059