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一个通用的幂律优化了视觉适应中的能量和表征保真度。

A universal power law optimizes energy and representation fidelity in visual adaptation.

作者信息

Mariani Matteo, Moosavi Amin S, Ringach Dario, Dipoppa Mario

出版信息

bioRxiv. 2025 Apr 8:2025.03.20.643406. doi: 10.1101/2025.03.20.643406.

DOI:10.1101/2025.03.20.643406
PMID:40196572
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11974702/
Abstract

Sensory systems continuously adapt their responses based on the probability of encountering a given stimulus. In the mouse primary visual cortex (V1), the average population response is a power law of the prior probability of stimuli in the environment. For a given stimulus type (e.g., oriented gratings), the power law's exponent is invariant to changes in statistical environments, enabling predictions of average population responses to new environments. Here, we aim to provide a normative explanation for the power law behavior. We develop an efficient coding model where neurons adjust their firing rates through multi-objective optimization, hypothesizing that the neural population adapts to enhance stimulus detection and discrimination while reducing overall neural activity. We show that a power law that matches the one observed experimentally can emerge from our model. We interpret the exponent as reflecting a balance between energy efficiency and representational fidelity in adaptation. Furthermore, we account for the invariance of the power law's exponent across environmental changes by linking it to the dependence of tuning curve modulation on stimulus probability. Finally, we explain that variations in the exponent with different stimulus types (e.g., natural movies) result from changes in the minimal distances between neural representations, in agreement with experimental findings. We conclude that a universal power law of adaptation can be explained as a trade-off between representation fidelity and energy cost.

摘要

感觉系统会根据遇到特定刺激的概率不断调整其反应。在小鼠初级视觉皮层(V1)中,平均群体反应是环境中刺激先验概率的幂律函数。对于给定的刺激类型(例如,定向光栅),幂律的指数对于统计环境的变化是不变的,这使得能够预测对新环境的平均群体反应。在这里,我们旨在为幂律行为提供一种规范的解释。我们开发了一个高效编码模型,其中神经元通过多目标优化来调整其放电率,假设神经群体通过适应来增强刺激检测和辨别能力,同时降低整体神经活动。我们表明,我们的模型能够产生与实验观察到的幂律相匹配的幂律。我们将该指数解释为反映了适应过程中能量效率和表征保真度之间的平衡。此外,我们通过将幂律指数的不变性与调谐曲线调制对刺激概率的依赖性联系起来,解释了幂律指数在不同环境变化下的不变性。最后,我们解释了不同刺激类型(例如,自然电影)下指数的变化是由于神经表征之间最小距离的变化,这与实验结果一致。我们得出结论,一种通用的适应幂律可以被解释为表征保真度和能量成本之间的权衡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/6f38bc2b74a7/nihpp-2025.03.20.643406v2-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/14c4f0d90b1f/nihpp-2025.03.20.643406v2-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/d6307066c433/nihpp-2025.03.20.643406v2-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/a4a738ff6b61/nihpp-2025.03.20.643406v2-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/1e9180fb04ed/nihpp-2025.03.20.643406v2-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/6f38bc2b74a7/nihpp-2025.03.20.643406v2-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/14c4f0d90b1f/nihpp-2025.03.20.643406v2-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/d6307066c433/nihpp-2025.03.20.643406v2-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/a4a738ff6b61/nihpp-2025.03.20.643406v2-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/1e9180fb04ed/nihpp-2025.03.20.643406v2-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b261/11995950/6f38bc2b74a7/nihpp-2025.03.20.643406v2-f0005.jpg

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