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1.  Motion Integration by Neurons in Macaque MT Is Local, Not Global 
Direction-selective neurons in primary visual cortex have small receptive fields that encode the motions of local features. These motions often differ from the motion of the object to which they belong and must therefore be integrated elsewhere. A candidate site for this integration is visual cortical area MT (V5), in which cells with large receptive fields compute the motion of patterns. Previous studies of motion integration in MT have used stimuli that fill the receptive field, and thus do not test whether motion information is really integrated across this whole area. For each MT neuron, we identified two regions (“patches”) within the receptive field that were approximately equally effective in driving responses. We then measured responses to plaids whose component gratings overlapped within a patch, and compared them with responses to the same component gratings presented in separate patches. Cells that were selective for the direction of motion of the whole pattern when the gratings overlapped lost this selectivity when the gratings were separated and became selective instead for the direction of motion of the individual components. If MT cells simply pooled all of the inputs that endow them with a receptive field, they would encode all of the motions in the receptive field as belonging to a single object. Our results indicate instead that critical elements of the computations underlying pattern-direction selectivity in MT are done locally, on a scale smaller than the whole receptive field.
doi:10.1523/JNEUROSCI.3183-06.2007
PMCID: PMC3039841  PMID: 17215397
vision; visual motion; extrastriate; MT; V5; pattern; component
2.  Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition 
PLoS Computational Biology  2014;10(12):e1003963.
The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of “kernel analysis” that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.
Author Summary
Primates are remarkable at determining the category of a visually presented object even in brief presentations, and under changes to object exemplar, position, pose, scale, and background. To date, this behavior has been unmatched by artificial computational systems. However, the field of machine learning has made great strides in producing artificial deep neural network systems that perform highly on object recognition benchmarks. In this study, we measured the responses of neural populations in inferior temporal (IT) cortex across thousands of images and compared the performance of neural features to features derived from the latest deep neural networks. Remarkably, we found that the latest artificial deep neural networks achieve performance equal to the performance of IT cortex. Both deep neural networks and IT cortex create representational spaces in which images with objects of the same category are close, and images with objects of different categories are far apart, even in the presence of large variations in object exemplar, position, pose, scale, and background. Furthermore, we show that the top-level features in these models exceed previous models in predicting the IT neural responses themselves. This result indicates that the latest deep neural networks may provide insight into understanding primate visual processing.
doi:10.1371/journal.pcbi.1003963
PMCID: PMC4270441  PMID: 25521294
3.  Binocular integration of pattern motion signals by MT neurons and by human observers 
Analysis of the movement of a complex visual stimulus is expressed in the responses of pattern-direction selective neurons in area MT, which depend in turn on directionally selective inputs from area V1. How do MT neurons integrate their inputs? Pattern selectivity in MT breaks down when the gratings comprising a moving plaid are presented to non-overlapping regions of the (monocular) receptive field. Here we ask an analogous question, is pattern selectivity maintained when the component gratings are presented dichoptically to binocular MT neurons? We recorded from single units in area MT, measuring responses to monocular gratings and plaids, and to dichoptic plaids in which the components are presented separately to each eye. Neurons that are pattern selective when tested monocularly lose this selectivity when stimulated with dichoptic plaids. When human observers view these same stimuli, dichoptic plaids induce binocular rivalry. Yet motion signals from each eye can be integrated despite rivalry, revealing a dissociation of form and motion perception. These results reveal the role of monocular mechanisms in the computation of pattern motion in single neurons, and demonstrate that the perception of motion is not fully represented by the responses of individual MT neurons.
doi:10.1523/JNEUROSCI.4552-09.2010
PMCID: PMC2893719  PMID: 20505101
MT; V5; extrastriate cortex; visual motion; direction selectivity; binocular interaction
4.  Grouping in object recognition: The role of a Gestalt law in letter identification 
Cognitive Neuropsychology  2009;26(1):36-49.
The Gestalt psychologists reported a set of laws describing how vision groups elements to recognize objects. The Gestalt laws “prescribe for us what we are to recognize ‘as one thing’” (Köhler, 1920). Were they right? Does object recognition involve grouping? Tests of the laws of grouping have been favourable, but mostly assessed only detection, not identification, of the compound object. The grouping of elements seen in the detection experiments with lattices and “snakes in the grass” is compelling, but falls far short of the vivid everyday experience of recognizing a familiar, meaningful, named thing, which mediates the ordinary identification of an object. Thus, after nearly a century, there is hardly any evidence that grouping plays a role in ordinary object recognition. To assess grouping in object recognition, we made letters out of grating patches and measured threshold contrast for identifying these letters in visual noise as a function of perturbation of grating orientation, phase, and offset. We define a new measure, “wiggle”, to characterize the degree to which these various perturbations violate the Gestalt law of good continuation. We find that efficiency for letter identification is inversely proportional to wiggle and is wholly determined by wiggle, independent of how the wiggle was produced. Thus the effects of three different kinds of shape perturbation on letter identifiability are predicted by a single measure of goodness of continuation. This shows that letter identification obeys the Gestalt law of good continuation and may be the first confirmation of the original Gestalt claim that object recognition involves grouping.
doi:10.1080/13546800802550134
PMCID: PMC2679997  PMID: 19424881
Gestalt; Grouping; Contour integration; Good continuation; Letter identification; Object recognition; Features; Snake in the grass; Snake letters; Dot lattice
5.  Grouping in object recognition: The role of a Gestalt law in letter identification 
Cognitive neuropsychology  2009;26(1):36-49.
The Gestalt psychologists reported a set of laws describing how vision groups elements to recognize objects. The Gestalt laws “prescribe for us what we are to recognize ‘as one thing’.” (Köhler, 1920). Were they right? Does object recognition involve grouping? Tests of the laws of grouping have been favorable, but mostly assessed only detection, not identification, of the compound object. The grouping of elements seen in the detection experiments with lattices and ‘snakes in the grass’ is compelling, but falls far short of the vivid everyday experience of recognizing a familiar, meaningful, named thing, which mediates the ordinary identification of an object. Thus, after nearly a century, there is hardly any evidence that grouping plays a role in ordinary object recognition. To assess grouping in object recognition, we made letters out of grating patches and measured threshold contrast for identifying these letters in visual noise as a function of perturbation of grating orientation, phase, and offset. We define a new measure, “wiggle,” to characterize the degree to which these various perturbations violate the Gestalt law of good continuation. We find that efficiency for letter identification is inversely proportional to wiggle, and is wholly determined by wiggle, independent of how the wiggle was produced. Thus the effects of three different kinds of shape perturbation on letter identifiability are predicted by a single measure of goodness of continuation. This shows that letter identification obeys the Gestalt law of good continuation, and may be the first confirmation of the original Gestalt claim that object recognition involves grouping.
doi:10.1080/13546800802550134
PMCID: PMC2679997  PMID: 19424881
Gestalt; grouping; contour integration; good continuation; letter identification; object recognition; features; snake in the grass; snake letters; dot lattice

Results 1-5 (5)