How Does the Brain Solve Visual Object Recognition?

2012/02/01 by James J. DiCarlo, James J. DiCarlo, Davide Zoccolan +2 · 1 voice · 33 citations
Neuroscience · #Face Recognition and Perception #Neural dynamics and brain function #Visual perception and processing mechanisms

paper · pdf · doi:10.1016/j.neuron.2012.01.010

crossref issued 2012/02/01 · crossref published 2012/02/01 · crossref published-print 2012/02/01 · openalex publication_date 2012/02/01 · crossref created 2012/02/08 · openalex created_date 2025/10/10 · crossref deposited 2025/10/12 · openalex updated_date 2026/07/29 · crossref indexed 2026/07/29

Citations

• I.—COMPUTING MACHINERY AND INTELLIGENCE
• The perceptron: A probabilistic model for information storage and organization in the brain.
• Slow Feature Analysis: Unsupervised Learning of Invariances
• NEURONAL CIRCUITS OF THE NEOCORTEX
• Selectivity for the Shape, Size, and Orientation of Objects for Grasping in Neurons of Monkey Parietal Area AIP

Cited by

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• Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation
• Deep Auto-encoder with Neural Response
• Visual experience orthogonalizes visual cortical stimulus responses via population code transformation
• The computational perspective: A catalyst for research questions in cognitive neuroscience?
• Causal neural mechanisms of context-based object recognition
• A Deeper Look at the Unsupervised Learning of Disentangled Representations in β-VAE from the Perspective of Core Object Recognition
• Compact deep neural network models of the visual cortex
• Rethinking the centrality of brain areas in understanding functional organization
• Neural representations of visual memory in inferotemporal cortex reveal a generalizable framework for translating between spikes and field potentials
• Dimensions underlying the representational alignment of deep neural networks with humans
• What Is a Visual Stream?
• Data-driven emergence of convolutional structure in neural networks
• Abstraction of Reward Context Facilitates Relative Reward Coding in Neural Populations of the Macaque Anterior Cingulate Cortex
• Broadband synergy versus oscillatory redundancy in the visual cortex
• Seeing eye-to-eye? A comparison of object recognition performance in\n humans and deep convolutional neural networks under image manipulation
• On the achievability of blind source separation for high-dimensional nonlinear source mixtures
• What happens next and when "next" happens: Mechanisms of spatial and temporal prediction
• Neocortical plasticity: an unsupervised cake but no free lunch
• Hybrid Lie semi-group and cascade structures for the generalized Gaussian derivative model for visual receptive fields
• Deep Predictive Coding Network for Object Recognition
• From retinotopic to ordinal coding: Dissecting the cortical stages of visual word recognition
• A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment
• Interpreting Encoding and Decoding Models
• On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responses
• Partial success in closing the gap between human and machine vision
• Understanding top-down attention using task-oriented ablation design
• Efficient neural encoding as revealed by bilingualism
• AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
• Dynamical Alignment: A Principle for Adaptive Neural Computation
• Memory as Structured Trajectories: Persistent Homology and Contextual Sheaves
• Crowding in humans is unlike that in convolutional neural networks

Discussions

• Important point!!! I lived that wave (eg, link). I remember conversations about why feedback connections obv. didn't matter b/c our feedforward models explained everything we were trying to understand [bsky, 3 points, 1 comments]