Abstract-How our brains develop disparity tuned 1 and 2 cells and then integrate binocular disparity into 3-D perception of the visual world is still largely a mystery. Moreover, computational models that take into account the role of the 6-layer architecture of the laminar cortex and temporal aspects of visual stimuli are elusive for stereo. In this paper, we present cortex-inspired computational models that simulate the development of stereo receptive fields, and use developed disparity sensitive neurons to estimate binocular disparity. Not only do the results show that the use of topdown signals in the form of supervision or temporal context greatly improves the performance of the networks, but also results in biologically compatible cortical maps-the representation of disparity selectivity is grouped, and changes gradually along the cortex. To our knowledge, this work is the first neuromorphic, end-to-end model of laminar cortex that integrates temporal context to develop internal representation, and generates accurate motor actions in the challenging problem of detecting disparity in binocular natural images. The networks reach a subpixel average error in regression, and 0.90 success rate in classification, given limited resources.Index Terms-Binocular vision, neuromorphic modeling, spatiotemporal, six-layer laminar cortical architecture.
How and under what circumstances the training effects of perceptual learning (PL) transfer to novel situations is critical to our understanding of generalization and abstraction in learning. Although PL is generally believed to be highly specific to the trained stimulus, a series of psychophysical studies have recently shown that training effects can transfer to untrained conditions under certain experimental protocols. In this article, we present a brain-inspired, neuromorphic computational model of the Where-What visuomotor pathways which successfully explains both the specificity and transfer of perceptual learning. The major architectural novelty is that each feature neuron has both sensory and motor inputs. The network of neurons is autonomously developed from experience, using a refined Hebbian-learning rule and lateral competition, which altogether result in neuronal recruitment. Our hypothesis is that certain paradigms of experiments trigger two-way (descending and ascending) off-task processes about the untrained condition which lead to recruitment of more neurons in lower feature representation areas as well as higher concept representation areas for the untrained condition, hence the transfer. We put forward a novel proposition that gated self-organization of the connections during the off-task processes accounts for the observed transfer effects. Simulation results showed transfer of learning across retinal locations in a Vernier discrimination task in a double-training procedure, comparable to previous psychophysical data (Xiao et al., 2008). To the best of our knowledge, this model is the first neurally-plausible model to explain both transfer and specificity in a PL setting.
Abstract. Engineering approaches to stereo typically use explicit search for the best matching between left and right sub-windows, which involves a high cost of search and unstable performance in the presence of binocular inconsistency and weak texture. The brain does not seem to conduct explicit search in the V1 and V2 cortex. But the mechanisms that the brain employs to integrate binocular disparity into 3-D perception is still largely a mystery. The work presented in this paper focuses on an important issue of integrated stereo: How the same cortex can perform recognition and perception by generating a topographic disparity-tuning map using top-down connections. As top-down connections with objectclass supervisory signals result in topographic class maps, the model presented here clarifies that stereo can be processed by a unified in-place learning framework in the neural layers, and can generate iconic-abstract internal representation.
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