Adaptive coding efficiency through joint gain control in neural populationsL Duong*, D Lipshutz*, D J Heeger, D Chklovskii and E P SimoncelliPublished in Computational and Systems Neuroscience (CoSyNe), Mar 2023.DOI: 10.57736/c514-ad88 This paper has been superseded by:
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We derive a normative adaptive whitening algorithm which regulates joint second-order statistics of a neural population by adjusting the marginal statistics of an overcomplete auxiliary population. The algorithm operates online, and can be mapped onto a recurrent neural network comprising principal cells and gain-modulating interneurons. Remarkably, the interneurons adjust gains using only local signals, and feed back onto principal cells to achieve a globally statistically white output. Our framework can be generalized to handle biophysical constraints, and we demonstrate its use in statistically whitening local image patches using convolutional weights. Finally, we compare our model to experimental observations of adaptation in early sensory systems.