Proposed mechanisms exist for normalization in probabilistic population codes
Multiple studies demonstrate and model the theoretical and circuit-level mechanisms for normalization within probabilistic neural population codes.
The claim is specific, empirical, and directly addressed by several retrieved papers investigating computational and circuit-level mechanisms of normalization within neural population coding frameworks.
Siwei Lyu. Dependency Reduction with Divisive Normalization: Justification and Effectiveness. 2011. https://doi.org/10.1162/neco_a_00197
Paper 0 establishes theoretical mechanisms for divisive normalization as an efficient coding transform in probabilistic models.
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Lian Y, Burkitt AN. Relating sparse and predictive coding to divisive normalization.. 2025. https://doi.org/10.1371/journal.pcbi.1013059
Paper 3 connects divisive normalization with sparse and predictive coding within a unified neural framework.
Huang X, Ghimire B, Chakrala AS, Wiesner S. Neural coding of multiple motion speeds in visual cortical area MT.. 2026. https://doi.org/10.7554/elife.94835
Paper 5 employs a modified divisive normalization model to explain neural coding mechanisms of multiple speeds in visual populations.
Yanbo Lian, A. Burkitt. Relating sparse/predictive coding to divisive normalization. 2025. https://doi.org/10.1101/2023.06.08.544285
Paper 10 integrates divisive normalization into a learning framework demonstrating how neural response nonlinearities emerge.
Song D, Ruff D, Cohen M, Huang C. Neuronal heterogeneity of normalization strength in a circuit model.. 2026. https://doi.org/10.1126/sciadv.adv9396
Paper 11 investigates circuit mechanisms underlying normalization strength in a spiking neural network model.
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