# Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

arXiv: https://arxiv.org/abs/2001.07092 · HTML: https://mlsanity.com/paper/2001.07092/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2020.

## Built on by (most cited first, 30 of 83)

- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
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- [Brain Captioning: Decoding human brain activity into images and text](https://mlsanity.com/paper/2305.11560/) (2023)
- [A Spiking Neural Network for Image Segmentation](https://mlsanity.com/paper/2106.08921/) (2021)
- [Dimensions underlying the representational alignment of deep neural networks with humans](https://mlsanity.com/paper/2406.19087/) (2024)
- [A Spectral Theory of Neural Prediction and Alignment](https://mlsanity.com/paper/2309.12821/) (2023)
- [Towards Biologically Plausible Convolutional Networks](https://mlsanity.com/paper/2106.13031/) (2021)
- [Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals](https://mlsanity.com/paper/2203.11560/) (2022)
- [Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli](https://mlsanity.com/paper/2212.06726/) (2022)
- [Poisson Variational Autoencoder](https://mlsanity.com/paper/2405.14473/) (2024)
- [Revealing Vision-Language Integration in the Brain with Multimodal Networks](https://mlsanity.com/paper/2406.14481/) (2024)
- [Discriminating image representations with principal distortions](https://mlsanity.com/paper/2410.15433/) (2024)
- [Testing the limits of natural language models for predicting human language judgments](https://mlsanity.com/paper/2204.03592/) (2022)
- [Human-Aligned Image Models Improve Visual Decoding from the Brain](https://mlsanity.com/paper/2502.03081/) (2025)
- [Deep Spiking Neural Networks with Resonate-and-Fire Neurons](https://mlsanity.com/paper/2109.08234/) (2021)
- [A model of semantic completion in generative episodic memory](https://mlsanity.com/paper/2111.13537/) (2021)
- [Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.04048/) (2021)
- [Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework](https://mlsanity.com/paper/2110.00214/) (2021)
- [High-performance Evolutionary Algorithms for Online Neuron Control](https://mlsanity.com/paper/2204.06765/) (2022)
- [Stochastic Adaptive Activation Function](https://mlsanity.com/paper/2210.11672/) (2022)
- [A Study on the Generality of Neural Network Structures for Monocular Depth Estimation](https://mlsanity.com/paper/2301.03169/) (2023)
- [DLSIA: Deep Learning for Scientific Image Analysis](https://mlsanity.com/paper/2308.02559/) (2023)
- [Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning](https://mlsanity.com/paper/2403.07078/) (2024)
- [Neural Circuit Architectural Priors for Embodied Control](https://mlsanity.com/paper/2201.05242/) (2022)
- [Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks](https://mlsanity.com/paper/2205.13816/) (2022)
- [Adaptive patch foraging in deep reinforcement learning agents](https://mlsanity.com/paper/2210.08085/) (2022)
- [Temporal-Difference Learning Using Distributed Error Signals](https://mlsanity.com/paper/2411.03604/) (2024)
- [Attention module improves both performance and interpretability of 4D fMRI decoding neural network](https://mlsanity.com/paper/2110.00920/) (2021)
- [CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing](https://mlsanity.com/paper/2112.00913/) (2021)

## Abstract (arXiv)

Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neuroscience and the various ways models can provide insight. Specifically, it covers the origins of CNNs and the methods by which we validate them as models of biological vision. It then goes on to elaborate on what we can learn about biological vision by understanding and experimenting on CNNs and discusses emerging opportunities for the use of CNNS in vision research beyond basic object recognition.