PT  - JOURNAL ARTICLE
AU  - Hannagan, T.
TI  - Reset Networks: Emergent Topography by Composition of Convolutional Neural Networks
AID  - 10.1101/2021.11.19.469308
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.11.19.469308
4099  - http://biorxiv.org/content/early/2021/12/01/2021.11.19.469308.short
4100  - http://biorxiv.org/content/early/2021/12/01/2021.11.19.469308.full
AB  - 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 StatementThe authors have declared no competing interest.