# A Vision Architecture

arXiv: https://arxiv.org/abs/1407.1642 · HTML: https://mlsanity.com/paper/1407.1642/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2014.

## Built on by (most cited first, 2 of 2)

- [Representation Learning in a Decomposed Encoder Design for Bio-inspired Hebbian Learning](https://mlsanity.com/paper/2401.08603/) (2023)
- [Uncertainty-Aware Decomposed Hybrid Networks](https://mlsanity.com/paper/2503.19096/) (2025)

## Abstract (arXiv)

We are offering a particular interpretation (well within the range of experimentally and theoretically accepted notions) of neural connectivity and dynamics and discuss it as the data-and-process architecture of the visual system. In this interpretation the permanent connectivity of cortex is an overlay of well-structured networks, nets, which are formed on the slow time-scale of learning by self-interaction of the network under the influence of sensory input, and which are selectively activated on the fast perceptual time-scale. Nets serve as an explicit, hierarchically structured representation of visual structure in the various sub-modalities, as constraint networks favouring mutually consistent sets of latent variables and as projection mappings to deal with invariance.