Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models.

<p>Left: No regularization, Right: With activity regularization. Dotted line: p < 0.05. Solid line: p < 0.0001.</p>

Saved in:

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
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items