Recurrent Processing during Object Recognition

By Randall C. O’Reilly, Dean Wyatte, Seth A. Herd, David J. Jilk. 2013

179 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

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Citations in the corpus

Deep Networks with Internal Selective Attention through Feedback Connections (2014, background): “It has been shown to be effective for object recognition [46], and has also been”
Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models (2017, background): “structure of the ventral visual stream 3,20,21 , brain seems to deploy complementary mechanisms to perform accurately in difficult object recognition situations 22,23 .”
The Visual System's Internal Model of the World (2015, background): “…hierarchical architecture of the biological visual system and share many fundamental characteristics, they represent different perspectives how the internal model can be learned and used for inference, and in my opinion, each capturing or emphasizing certain elements of the reality of the brain.”