Abstract
We introduce Reset networks, which are compositions of several neural networks - typically several levels of CNNs - where outputs at one level are gathered and reshaped into a spatial input for the next level. We demonstrate that Reset networks exhibit emergent topographic organization for numbers, as well as for visual categories taken from CIFAR-100. We outline the implications of this model for theories of the cortex and developmental neuroscience.
Competing Interest Statement
The authors have declared no competing interest.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Posted December 01, 2021.
Reset Networks: Emergent Topography by Composition of Convolutional Neural Networks
