Data to 7 Oct 2026

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

arXiv 2001.07092 ↗First posted 20 Jan 2020q-bio.NC cs.CV cs.NEcited by 83

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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.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 11; 2023: 14; 2024: 20; 2025: 11; 2026: 10.

83 papers.

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2021Do Vision Transformers See Like Convolutional Neural Networks?cited by 671
2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 99
2023Neither hype nor gloom do DNNs justicecited by 40
2023Brain Captioning: Decoding human brain activity into images and textcited by 33
2021A Spiking Neural Network for Image Segmentationcited by 27
2024Dimensions underlying the representational alignment of deep neural networks with humanscited by 22
2023A Spectral Theory of Neural Prediction and Alignmentcited by 14
2021Towards Biologically Plausible Convolutional Networkscited by 13
2022Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signalscited by 12
2022Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimulicited by 12
2024Poisson Variational Autoencodercited by 11
2024Revealing Vision-Language Integration in the Brain with Multimodal Networkscited by 11

38 more

2022Testing the limits of natural language models for predicting human language judgmentscited by 7
2024Discriminating image representations with principal distortionscited by 7
2025Human-Aligned Image Models Improve Visual Decoding from the Braincited by 7
2021Deep Spiking Neural Networks with Resonate-and-Fire Neuronscited by 5
2021A model of semantic completion in generative episodic memorycited by 5
2021Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networkscited by 4
2021Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Frameworkcited by 4
2022High-performance Evolutionary Algorithms for Online Neuron Controlcited by 4
2022Stochastic Adaptive Activation Functioncited by 4
2023A Study on the Generality of Neural Network Structures for Monocular Depth Estimationcited by 4
2023DLSIA: Deep Learning for Scientific Image Analysiscited by 4
2024Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learningcited by 4
2022Neural Circuit Architectural Priors for Embodied Controlcited by 3
2022Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networkscited by 3
2022Adaptive patch foraging in deep reinforcement learning agentscited by 3
2024Temporal-Difference Learning Using Distributed Error Signalscited by 3
2021Attention module improves both performance and interpretability of 4D fMRI decoding neural networkcited by 2
2021CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicingcited by 2
2023Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignmentcited by 2
2024Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AIcited by 2
2024PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesiscited by 2
2024Learning in Wilson-Cowan model for metapopulationcited by 2
2024Neural Circuit Architectural Priors for Quadruped Locomotioncited by 2
2025Spectral Analysis of Representational Similarity with Limited Neuronscited by 2
2025Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentationscited by 2
2025Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problemscited by 2
2025Computer Vision Models Show Human-Like Sensitivity to Geometric and Topological Conceptscited by 2
2026Similarity of Processing Steps in Vision Model Representationscited by 2
2021Immuno-mimetic Deep Neural Networks (Immuno-Net)cited by 1
2021Symmetry Perception by Deep Networks: Inadequacy of Feed-Forward Architectures and Improvements with Recurrent Connectionscited by 1
2022Learning an Adaptation Function to Assess Image Visual Similaritiescited by 1
2022Representation learning for a generalized, quantitative comparison of complex model outputscited by 1
2023Language Cognition and Language Computation -- Human and Machine Language Understandingcited by 1
2023Beyond the limitations of any imaginable mechanism: large language models and psycholinguisticscited by 1
2023Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecturecited by 1
2024Playing With Neuroscience: Past, Present and Future of Neuroimaging and Gamescited by 1
2024SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencodercited by 1
2024Artificial Human Intelligence: The role of Humans in the Development of Next Generation AIcited by 1

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