# Brain-inspired algorithms for processing of visual data

arXiv: https://arxiv.org/abs/2103.01634 · HTML: https://mlsanity.com/paper/2103.01634/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Non-linear Convolution Filters for CNN-based Learning](https://mlsanity.com/paper/1708.07038/) (2017)
- [Detection of curved lines with B-COSFIRE filters: A case study on crack delineation](https://mlsanity.com/paper/1707.07747/) (2017)
- [Learning sound representations using trainable COPE feature extractors](https://mlsanity.com/paper/1901.06904/) (2019)
- [Delineation of line patterns in images using B-COSFIRE filters](https://mlsanity.com/paper/1707.07438/) (2017)
- [Brain-inspired robust delineation operator](https://mlsanity.com/paper/1811.10240/) (2018)

## Built on by (most cited first, 1 of 1)

- [NeuGen: Amplifying the 'Neural' in Neural Radiance Fields for Domain Generalization](https://mlsanity.com/paper/2505.06894/) (2025)

## Abstract (arXiv)

The study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing. In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.