Distributions of output activations for unregularized and weight-regularized networks, for high noise and low noise inputs.

<p>Output activations from high noise inputs are shown in blue, output activations from low noise inputs are shown in orange. Weight regularization leads not only to normalization of the neural output distribution, but also reduces the ratio in output variance in response to high noise and low...

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Bibliographic Details
Main Author:	Dirk Gütlin (22566778) (author)
Other Authors:	Ryszard Auksztulewicz (4640515) (author)
Published:	2025
Subjects:	Biophysics Neuroscience Physiology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified without evaluating whether like effects across like behaviors better including mismatch responses biological neural networks compared two pc advancing brain modelling inspired training objectives like responses training algorithms neural learning work contributes underlying mechanisms semantic information saving principles results show promising foundation pc framework neuroscientific theory key signatures inspired algorithms important mechanism gain control findings indicate dnn ). contrastive approach accurately reflect
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Distributions of output activations for unregularized and weight-regularized networks, for high noise and low noise inputs.

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