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Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations

https://doi.org/10.1101/2020.06.16.154542

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What this work says

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Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception?

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Human Eyes–Inspired Recurrent Neural Networks Are More Robust Against Adversarial Noises

inferred← citeshttps://doi.org/10.1162/neco_a_01691openalex-snapshot

Spontaneous Emergence of Robustness to Light Variation in CNNs With a Precortically Inspired Module

inferred← citeshttps://doi.org/10.1167/jov.21.10.14openalex-snapshot

Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision

inferred← citeshttps://doi.org/10.1167/jov.22.2.19openalex-snapshot

Inference via sparse coding in a hierarchical vision model

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1009739openalex-snapshot

Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1010484openalex-snapshot

Structured random receptive fields enable informative sensory encodings

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1011145openalex-snapshot

How well do models of visual cortex generalize to out of distribution samples?

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1011506openalex-snapshot

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1012056openalex-snapshot

Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks

inferred← citeshttps://doi.org/10.1523/jneurosci.0602-21.2021openalex-snapshot

Rapid Extraction of the Spatial Distribution of Physical Saliency and Semantic Informativeness from Natural Scenes in the Human Brain

inferred← citeshttps://doi.org/10.1609/aaai.v37i1.25073openalex-snapshot

Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse

inferred← citeshttps://doi.org/10.1609/aaai.v38i1.27804openalex-snapshot

Enhancing the Robustness of Spiking Neural Networks with Stochastic Gating Mechanisms

inferred← citeshttps://doi.org/10.1609/aaai.v39i26.34988openalex-snapshot

Measuring Error Alignment for Decision-Making Systems

inferred← citeshttps://doi.org/10.20944/preprints202407.2012.v1openalex-snapshot

Human-Inspired Decision-Making in AI: Leveraging Insights from Visual Perception for Real-Time Sensor Data Processing

inferred← citeshttps://doi.org/10.21203/rs.3.rs-3131001/v1openalex-snapshot

Computational Analysis Reveals Sensitivity to Contrast in Simple Cells of Primary Visual Cortex

inferred← citeshttps://doi.org/10.2352/j.percept.imaging.2023.6.000402openalex-snapshot

Efficient Coding in Human Vision as a Useful Bias in Computer Vision and Machine Learning

inferred← citeshttps://doi.org/10.31234/osf.io/esvuwopenalex-snapshot

Examining Infant Relation Categorization Through Deep Neural Networks

inferred← citeshttps://doi.org/10.31234/osf.io/z95t7openalex-snapshot

A comparison between humans and AI at recognizing objects in unusual poses

inferred← citeshttps://doi.org/10.3389/fncom.2021.609721openalex-snapshot

Quantifying the Brain Predictivity of Artificial Neural Networks With Nonlinear Response Mapping

inferred← citeshttps://doi.org/10.3389/fpsyg.2023.1047694openalex-snapshot

Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations?

inferred← citeshttps://doi.org/10.3390/ai2010007openalex-snapshot

The Ouroboros Model, Proposal for Self-Organizing General Cognition Substantiated

inferred← citeshttps://doi.org/10.3390/app13074422openalex-snapshot

Robustness of Deep Learning Models for Vision Tasks

inferred← citeshttps://doi.org/10.3390/app132112068openalex-snapshot

Trustworthy Artificial Intelligence Methods for Users’ Physical and Environmental Security: A Comprehensive Review

inferred← citeshttps://doi.org/10.3390/bdcc10090308openalex-snapshot

Directional Inhibition Network (DI-Net): An Inspectable Retina-Inspired Code for Controlled GT-Isolated One-Pixel Eight-Way Direction Classification

inferred← citeshttps://doi.org/10.3390/biomimetics10010038openalex-snapshot

Artificial Visual System for Stereo-Orientation Recognition Based on Hubel-Wiesel Model

inferred← citeshttps://doi.org/10.3390/e26030258openalex-snapshot

Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation

inferred← citeshttps://doi.org/10.3390/electronics13132566openalex-snapshot

Advances in Brain-Inspired Deep Neural Networks for Adversarial Defense

inferred← citeshttps://doi.org/10.3390/math13010142openalex-snapshot

Enhancing Convolutional Neural Network Robustness Against Image Noise via an Artificial Visual System

inferred← citeshttps://doi.org/10.48550/arxiv.2006.07991openalex-snapshot

Emergent Properties of Foveated Perceptual Systems

inferred← citeshttps://doi.org/10.48550/arxiv.2008.13336openalex-snapshot

Shape Defense Against Adversarial Attacks

inferred← citeshttps://doi.org/10.48550/arxiv.2012.02976openalex-snapshot

Evaluating adversarial robustness in simulated cerebellum

inferred← citeshttps://doi.org/10.48550/arxiv.2106.07411openalex-snapshot

Partial success in closing the gap between human and machine vision

inferred← citeshttps://doi.org/10.48550/arxiv.2108.09768openalex-snapshot

Relating CNNs with brain: Challenges and findings

inferred← citeshttps://doi.org/10.48550/arxiv.2110.15664openalex-snapshot

3D-OOCS: Learning Prostate Segmentation with Inductive Bias

inferred← citeshttps://doi.org/10.48550/arxiv.2306.11582openalex-snapshot

Computing a human-like reaction time metric from stable recurrent vision models

inferred← citeshttps://doi.org/10.51628/001c.37507openalex-snapshot

Beyond linear regression: mapping models in cognitive neuroscience should align with research goals

inferred← citeshttps://doi.org/10.64898/2026.08.16.745063openalex-snapshot

Parametric neural control differentiates top neural network models of primate visual cortex

inferred← citeshttps://doi.org/10.70675/42802d90z0c77z4de8z829dz508145498217openalex-snapshot

Deep learning and neuroscience : a match made in heaven?

inferred← citeshttps://doi.org/10.7554/elife.54962openalex-snapshot

Efficient sampling and noisy decisions

inferred← citeshttps://doi.org/10.7554/elife.96217openalex-snapshot

Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition

inferred← citeshttps://doi.org/10.7554/elife.96217.1openalex-snapshot

Convolutional networks can model the functional modulation of MEG responses during reading

inferred← citeshttps://doi.org/10.7554/elife.96217.2openalex-snapshot

Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition

inferred← citeshttps://doi.org/10.7554/elife.96217.3openalex-snapshot

Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition

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