ArticleFrontiers in psychology2013
Recurrent Processing during Object Recognition.
Article in Frontiers in psychology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- The neural processes of illusory occlusion in object recognition.Journal of vision · 2026Article
- Synaptic Plasticity as a Function of the Temporal Derivative.bioRxiv : the preprint server for biology · 2026Article
- Modeling attention and binding in the brain through bidirectional recurrent gating.Nature communications · 2026Article
- Exposure to naturalistic occlusion promotes generalized, human-like robustness in deep neural networks.bioRxiv : the preprint server for biology · 2026Article
- Recurrent connections facilitate occluded object recognition by explaining-away.Nature communications · 2026Article
- Recurrent issues with deep neural network models of visual recognition.Scientific reports · 2025Article
- Category-specific perceptual learning of robust object recognition modelled using deep neural networks.PLoS computational biology · 2025Article
- Attentional dysfunction arises from right frontocentral and occipital network connectivity in Parkinson's disease.Neuroimage. Reports · 2025Article
- A computational deep learning investigation of animacy perception in the human brain.Communications biology · 2024Article
- A deep learning model of dorsal and ventral visual streams for DVSD.Scientific reports · 2024Article
- Spiking representation learning for associative memories.Frontiers in neuroscience · 2024Article
- Recurrent connectivity supports higher-level visual and semantic object representations in the brain.Communications biology · 2023Article
- A Computational Model Reveals Learning Dynamics During Interpretation Bias Training With Clinical Applications.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Article
- Canonical circuit computations for computer vision.Biological cybernetics · 2023Review
- Deep Neural Networks and Visuo-Semantic Models Explain Complementary Components of Human Ventral-Stream Representational Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition.Cerebral cortex communications · 2022Article
- Recurrent processing improves occluded object recognition and gives rise to perceptual hysteresis.Journal of vision · 2021Article
- Capturing the objects of vision with neural networks.Nature human behaviour · 2021Review
- Encoding of Partially Occluded and Occluding Objects in Primate Inferior Temporal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Deep Predictive Learning in Neocortex and Pulvinar.Journal of cognitive neuroscience · 2021Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of naturally occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness to occlusion derive from the role that recurrent connectivity and recurrent processing mechanisms play in the model. Furthermore, this interaction of recurrent connectivity and learning predicts that high-level visual representations should be shaped by error signals from nearby, associated brain areas over the course of visual learning. Consistent with this prediction, we show how semantic knowledge about object categories changes the nature of their learned visual representations, as well as how this representational shift supports the mapping between perceptual and conceptual knowledge. Altogether, these findings support the potential importance of ongoing recurrent processing throughout the brain's visual system and suggest ways in which object recognition can be understood in terms of interactions within and between processes over time.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.