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.
Highlights
One of the most salient features of the mammalian neocortex is the structure of its connectivity, which provides for many forms of recurrent processing, where neurons mutually influence each other through direct, bidirectional interactions
OBJECT RECOGNITION DATASET Before exploring the ways in which recurrent processing impacts the dynamics of object recognition, we briefly describe the basic set of objects on which the network was trained and tested, which we call the CU3D-100 dataset1
Naturalistic image datasets, while useful for benchmarking the ability of object recognition systems on realistic visual stimuli, are often underconstrained for studying biological principles of object recognition such as invariance or the recurrent processing effects that are of interest here
Summary
One of the most salient features of the mammalian neocortex is the structure of its connectivity, which provides for many forms of recurrent processing, where neurons mutually influence each other through direct, bidirectional interactions. We demonstrate how recurrent excitatory processing could provide a similar function in visual occlusion, which requires the organization of image fragments that span multiple receptive fields into a logical whole Gestalt and involves the filling-in of missing visual information (Kourtzi and Kanwisher, 2001; Lerner et al, 2002; Rauschenberger et al, 2006; Weigelt et al, 2007; Wyatte et al, 2012a). At a more local level, recurrent inhibitory processing produces sparse distributed representations, implemented in LVis through the use of a k-Winners-Take-All (kWTA) inhibition function (where k represents the roughly 15–25% activity levels present in neocortical networks; O’Reilly, 1998; O’Reilly and Munakata, 2000; O’Reilly et al, 2012). We show here that inhibitory recurrent dynamics and sparse distributed representations make our model more robust in the face of ambiguity, by testing recognition performance with occluded visual inputs
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