The brain stores data through distributed neural network structures
Contemporary neuroscience research overwhelmingly supports the view that the brain stores data and maintains memories through distributed neural network structures involving coordinated interactions across multiple cortical regions.
The retrieved papers consistently demonstrate that working memory and data storage rely on distributed network dynamics spanning prefrontal, parietal, and sensory cortices, rather than localized single-area silos. Multiple neuroimaging and computational modeling studies substantiate this architecture. Consequently, the claim is strongly supported.
Mengli Feng, Abhirup Bandyopadhyay, Jorge F. Mejias. Emergence of distributed working memory in a human brain network model. 2023. https://doi.org/10.1101/2023.01.26.525779
Demonstrates that working memory emerges from distributed brain activity reliant on long-range synaptic projections.
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Eren Günseli, Joshua J. Foster, David W. Sutterer, Lara Todorova, Edward K. Vogel, Edward Awh. Overlapping Neural Representations for Dynamic Visual Imagery and Stationary Storage in Spatial Working Memory. 2022. https://doi.org/10.1101/2022.09.24.509255
Shows that alpha-band activity tracks and preserves spatial working memory representations dynamically across regions.
Dake M, Dandekar S, Curtis CE. Neural synchrony between prefrontal and visual cortex supports visual working memory. 2026. https://doi.org/10.64898/2026.06.05.730488
Provides evidence that working memory depends on neural mechanisms distributed across prefrontal and visual cortices.
Yizhar O, Bauer F, Pont Sanchis I, Bröhl F, Spitzer BJ. Abstract and Concrete Working Memory Information in Human Visual Cortex. 2026. https://doi.org/10.64898/2026.05.23.727368
Finds robust encoding of memory information across both parietal and visual areas, supporting distributed storage.
Angiolelli M, De Candia A, Sorrentino P, Filippi S, Chiodo L, Cherubini C, Scarpetta S. Modularity-dependent storage of dynamic spiking patterns: Bridging micro- and mesoscopic representations.. 2026. https://doi.org/10.1103/rklz-gkqn
Indicates that network models utilizing modular, distributed spatiotemporal patterns support large-scale memory storage.
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