The human brain utilizes data compression algorithms in sensory processing.
Multiple neuroscience and neural network studies demonstrate that the brain utilizes efficient coding, redundancy reduction, and data compression principles during sensory information processing.
The retrieved literature consistently supports the efficient coding hypothesis and neural compression models in sensory processing. Papers [0], [1], [10], and [11] explicitly address how sensory information is optimized and compressed by neural architectures. Papers [7] and [11] further detail how data redundancy and dimensionality reduction are managed by the brain for efficient perception. There are no refuting papers.
Shervin Safavi, M. Chalk, N. Logothetis, A. Levina. Signatures of criticality in efficient coding networks. 2019. https://doi.org/10.1101/2023.02.14.528465
Paper [0] discusses efficient coding networks that optimize sensory stimulus processing.
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M. Chalk, Iain Murray, P. Seriès. Attention as Reward-Driven Optimization of Sensory Processing. 2013. https://doi.org/10.1162/NECO_a_00494
Paper [1] notes that sensory processing is optimized via the efficient coding hypothesis based on input statistics.
Zhou S, Gao C, Delbruck T, Verhelst M, Liu SC. Exploiting neuro-inspired dynamic sparsity for energy-efficient intelligent perception.. 2025. https://doi.org/10.1038/s41467-025-65387-7
Paper [7] describes neuro-inspired dynamic sparsity in perception and data redundancy exploitation.
Arish Alreja, Ilya Nemenman, Christopher Rozell. Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices. 2020. https://doi.org/10.1101/2020.09.17.299040
Paper [10] applies efficient coding models to vision and explains neural constraints and representations.
Kim MS, Kim HF. Brain-inspired strategies for efficient artificial intelligence.. 2026. https://doi.org/10.1016/j.mocell.2026.100365
Paper [11] explains how the brain uses convergent processing to compress sensory inputs and extract core information.
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