Machine Learning as Tool and Theory in Computational Neuroscience
Intro
Introduction
Neurophysiology practice and the complexity of sensory cortex
Learning and its consequences
Structure of this dissertation
Chapters
Hue tuning curves in V4 change with visual context
Modern machine learning as benchmark for encoding models
The Four Roles of Supervised Machine Learning in Systems Neuroscience
A role for cortical interneurons as adversarial discriminators
Measuring and regularizing networks in function space
Efficient neural codes naturally emerge through gradient descent learning
Conclusions
Conclusions
Acknowledgements
repository
open issue
Index
