Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning
書誌事項
- 公開日
- 2023-01-12
- 権利情報
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- https://www.springernature.com/gp/researchers/text-and-data-mining
- https://www.springernature.com/gp/researchers/text-and-data-mining
- DOI
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- 10.1038/s41593-022-01225-z
- 公開者
- Springer Science and Business Media LLC
この論文をさがす
説明
Recent experiments have revealed that neural population codes in many brain areas continuously change even when animals have fully learned and stably perform their tasks. This representational 'drift' naturally leads to questions about its causes, dynamics and functions. Here we explore the hypothesis that neural representations optimize a representational objective with a degenerate solution space, and noisy synaptic updates drive the network to explore this (near-)optimal space causing representational drift. We illustrate this idea and explore its consequences in simple, biologically plausible Hebbian/anti-Hebbian network models of representation learning. We find that the drifting receptive fields of individual neurons can be characterized by a coordinated random walk, with effective diffusion constants depending on various parameters such as learning rate, noise amplitude and input statistics. Despite such drift, the representational similarity of population codes is stable over time. Our model recapitulates experimental observations in the hippocampus and posterior parietal cortex and makes testable predictions that can be probed in future experiments.
収録刊行物
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- Nature Neuroscience
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Nature Neuroscience 26 (2), 339-349, 2023-01-12
Springer Science and Business Media LLC
