ALD hyperparameters capture frequency components in kernels that reflect trial-to-trial variability.

<p>(<b>a</b>) Schematic of ALD inputs and outputs. Left: input data: outer product of body movement and saccade event vectors (X) and dF/F residuals (Y) for every trial. Center: The ALD procedure finds time domain prior (left) and frequency domain prior (right, absolute value is sh...

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Bibliographic Details
Main Author:	Dmitry R. Lyamzin (19852549) (author)
Other Authors:	Andrea Alamia (513332) (author), Mohammad Abdolrahmani (19852552) (author), Ryo Aoki (2812603) (author), Andrea Benucci (5032244) (author)
Published:	2024
Subjects:	Biochemistry Cell Biology Neuroscience Pharmacology Biotechnology Hematology Mathematical Sciences not elsewhere classified visually evoked responses underlying biological phenomena pupillometry &# 8212 model &# 8217 interacting neural signals interacting neural activations common experimental designs regularization significantly improved optimal model hyperparameters arousal </ p regularization method xlink "> unclear whether seemingly unpredictably ridge regression regularizing hyperparameters reduce overfitting observations could natural behaviors modeling framework help interpret findings demonstrate discovery tools animals measured
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ALD hyperparameters capture frequency components in kernels that reflect trial-to-trial variability.

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