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1.  Visual Categorization of Natural Movies by Rats 
The Journal of Neuroscience  2014;34(32):10645-10658.
Visual categorization of complex, natural stimuli has been studied for some time in human and nonhuman primates. Recent interest in the rodent as a model for visual perception, including higher-level functional specialization, leads to the question of how rodents would perform on a categorization task using natural stimuli. To answer this question, rats were trained in a two-alternative forced choice task to discriminate movies containing rats from movies containing other objects and from scrambled movies (ordinate-level categorization). Subsequently, transfer to novel, previously unseen stimuli was tested, followed by a series of control probes. The results show that the animals are capable of acquiring a decision rule by abstracting common features from natural movies to generalize categorization to new stimuli. Control probes demonstrate that they did not use single low-level features, such as motion energy or (local) luminance. Significant generalization was even present with stationary snapshots from untrained movies. The variability within and between training and test stimuli, the complexity of natural movies, and the control experiments and analyses all suggest that a more high-level rule based on more complex stimulus features than local luminance-based cues was used to classify the novel stimuli. In conclusion, natural stimuli can be used to probe ordinate-level categorization in rats.
doi:10.1523/JNEUROSCI.3663-13.2014
PMCID: PMC4122800  PMID: 25100598
2.  Visual Space and Object Space in the Cerebral Cortex of Retinal Disease Patients 
PLoS ONE  2014;9(2):e88248.
The lower areas of the hierarchically organized visual cortex are strongly retinotopically organized, with strong responses to specific retinotopic stimuli, and no response to other stimuli outside these preferred regions. Higher areas in the ventral occipitotemporal cortex show a weak eccentricity bias, and are mainly sensitive for object category (e.g., faces versus buildings). This study investigated how the mapping of eccentricity and category sensitivity using functional magnetic resonance imaging is affected by a retinal lesion in two very different low vision patients: a patient with a large central scotoma, affecting central input to the retina (juvenile macular degeneration), and a patient where input to the peripheral retina is lost (retinitis pigmentosa). From the retinal degeneration, we can predict specific losses of retinotopic activation. These predictions were confirmed when comparing stimulus activations with a no-stimulus fixation baseline. At the same time, however, seemingly contradictory patterns of activation, unexpected given the retinal degeneration, were observed when different stimulus conditions were directly compared. These unexpected activations were due to position-specific deactivations, indicating the importance of investigating absolute activation (relative to a no-stimulus baseline) rather than relative activation (comparing different stimulus conditions). Data from two controls, with simulated scotomas that matched the lesions in the two patients also showed that retinotopic mapping results could be explained by a combination of activations at the stimulated locations and deactivations at unstimulated locations. Category sensitivity was preserved in the two patients. In sum, when we take into account the full pattern of activations and deactivations elicited in retinotopic cortex and throughout the ventral object vision pathway in low vision patients, the pattern of (de)activation is consistent with the retinal loss.
doi:10.1371/journal.pone.0088248
PMCID: PMC3914958  PMID: 24505449
3.  A conceptual framework of computations in mid-level vision 
If a picture is worth a thousand words, as an English idiom goes, what should those words—or, rather, descriptors—capture? What format of image representation would be sufficiently rich if we were to reconstruct the essence of images from their descriptors? In this paper, we set out to develop a conceptual framework that would be: (i) biologically plausible in order to provide a better mechanistic understanding of our visual system; (ii) sufficiently robust to apply in practice on realistic images; and (iii) able to tap into underlying structure of our visual world. We bring forward three key ideas. First, we argue that surface-based representations are constructed based on feature inference from the input in the intermediate processing layers of the visual system. Such representations are computed in a largely pre-semantic (prior to categorization) and pre-attentive manner using multiple cues (orientation, color, polarity, variation in orientation, and so on), and explicitly retain configural relations between features. The constructed surfaces may be partially overlapping to compensate for occlusions and are ordered in depth (figure-ground organization). Second, we propose that such intermediate representations could be formed by a hierarchical computation of similarity between features in local image patches and pooling of highly-similar units, and reestimated via recurrent loops according to the task demands. Finally, we suggest to use datasets composed of realistically rendered artificial objects and surfaces in order to better understand a model's behavior and its limitations.
doi:10.3389/fncom.2014.00158
PMCID: PMC4264474  PMID: 25566044
mid-level vision; similarity; pooling; perceptual organization; summary statistics
4.  Combinatorial brain decoding of people's whereabouts during visuospatial navigation 
Complex behavior typically relies upon many different processes which are related to activity in multiple brain regions. In contrast, neuroimaging analyses typically focus upon isolated processes. Here we present a new approach, combinatorial brain decoding, in which we decode complex behavior by combining the information which we can retrieve from the neural signals about the many different sub-processes. The case in point is visuospatial navigation. We explore the extent to which the route travelled by human subjects (N = 3) in a complex virtual maze can be decoded from activity patterns as measured with functional magnetic resonance imaging. Preliminary analyses suggest that it is difficult to directly decode spatial position from regions known to contain an explicit cognitive map of the environment, such as the hippocampus. Instead, we were able to indirectly derive spatial position from the pattern of activity in visual and motor cortex. The non-spatial representations in these regions reflect processes which are inherent to navigation, such as which stimuli are perceived at which point in time and which motor movement is executed when (e.g., turning left at a crossroad). Highly successful decoding of routes followed through the maze was possible by combining information about multiple aspects of navigation events across time and across multiple cortical regions. This “proof of principle” study highlights how visuospatial navigation is related to the combined activity of multiple brain regions, and establishes combinatorial brain decoding as a means to study complex mental events that involve a dynamic interplay of many cognitive processes.
doi:10.3389/fnins.2013.00078
PMCID: PMC3657635  PMID: 23730269
fMRI; pattern classification; visual cortex; motor cortex; objects and faces
5.  Informativeness and learning: Response to Gauthier and colleagues 
Trends in cognitive sciences  2010;14(6):236-237.
doi:10.1016/j.tics.2010.03.010
PMCID: PMC2908726  PMID: 20714344
6.  The Neural Basis of Visual Object Learning 
Object vision in human and nonhuman primates is often cited as a primary example of adult plasticity in neural information processing. It has been hypothesized that visual experience leads to single neurons in the monkey brain with strong selectivity for complex objects, and to regions in the human brain with a preference for particular categories of highly familiar objects. This view suggests that adult visual experience causes dramatic local changes in the response properties of high-level visual cortex. Here, we review the current neurophysiological and neuroimaging evidence and find that the available data support a different conclusion: adult visual experience introduces moderate, relatively distributed effects that modulate a pre-existing, rich and flexible set of neural object representations.
doi:10.1016/j.tics.2009.11.002
PMCID: PMC2818494  PMID: 19945336
7.  Feedback of pVisual Object Information to Foveal Retinotopic Cortex 
Nature neuroscience  2008;11(12):1439-1445.
The mammalian visual system contains an extensive web of feedback connections projecting from “higher” cortical areas to “lower” areas including primary visual cortex. Although multiple theories have been proposed, the role of these connections in perceptual processing is not understood. Here we report a surprising new phenomenon not predicted by prior theories of feedback: the pattern of fMRI response in human foveal retinotopic cortex contains information about objects presented in the periphery, far away from the fovea. This information is position invariant, correlated with perceptual discrimination accuracy, and found only in foveal, not peripheral, retinotopic cortex. Our data cannot be explained by differential eye movements, activation from the fixation cross, or spillover activation from peripheral retinotopic cortex or from LOC. Instead, our findings indicate that position-invariant object information from higher cortical areas is fed back to foveal retinotopic cortex, enhancing task performance.
doi:10.1038/nn.2218
PMCID: PMC2789292  PMID: 18978780
8.  Interpreting fMRI data: maps, modules and dimensions 
Nature reviews. Neuroscience  2008;9(2):123-135.
Neuroimaging research over the past decade has revealed a detailed picture of the functional organization of the human brain. Here we focus on two fundamental questions that are raised by the detailed mapping of sensory and cognitive functions and illustrate these questions with findings from the object-vision pathway. First, are functionally specific regions that are located close together best understood as distinct cortical modules or as parts of a larger-scale cortical map? Second, what functional properties define each cortical map or module? We propose a model in which overlapping continuous maps of simple features give rise to discrete modules that are selective for complex stimuli.
doi:10.1038/nrn2314
PMCID: PMC2731480  PMID: 18200027
9.  A Stable Topography of Selectivity for Unfamiliar Shape Classes in Monkey Inferior Temporal Cortex 
The inferior temporal (IT) cortex in monkeys plays a central role in visual object recognition and learning. Previous studies have observed patches in IT cortex with strong selectivity for highly familiar object classes (e.g., faces), but the principles behind this functional organization are largely unknown due to the many properties that distinguish different object classes. To unconfound shape from meaning and memory, we scanned monkeys with functional magnetic resonance imaging while they viewed classes of initially novel objects. Our data revealed a topography of selectivity for these novel object classes across IT cortex. We found that this selectivity topography was highly reproducible and remarkably stable across a 3-month interval during which monkeys were extensively trained to discriminate among exemplars within one of the object classes. Furthermore, this selectivity topography was largely unaffected by changes in behavioral task and object retinal position, both of which preserve shape. In contrast, it was strongly influenced by changes in object shape. The topography was partially related to, but not explained by, the previously described pattern of face selectivity. Together, these results suggest that IT cortex contains a large-scale map of shape that is largely independent of meaning, familiarity, and behavioral task.
doi:10.1093/cercor/bhm196
PMCID: PMC2731473  PMID: 18033769
categorization; fMRI; learning; object recognition; primate; visual perception
10.  Neural Representations that Support Invariant Object Recognition 
Neural mechanisms underlying invariant behaviour such as object recognition are not well understood. For brain regions critical for object recognition, such as inferior temporal cortex (ITC), there is now ample evidence indicating that single cells code for many stimulus aspects, implying that only a moderate degree of invariance is present. However, recent theoretical and empirical work seems to suggest that integrating responses of multiple non-invariant units may produce invariant representations at population level. We provide an explicit test for the hypothesis that a linear read-out mechanism of a pool of units resembling ITC neurons may achieve invariant performance in an identification task. A linear classifier was trained to decode a particular value in a 2-D stimulus space using as input the response pattern across a population of units. Only one dimension was relevant for the task, and the stimulus location on the irrelevant dimension (ID) was kept constant during training. In a series of identification tests, the stimulus location on the relevant dimension (RD) and ID was manipulated, yielding estimates for both the level of sensitivity and tolerance reached by the network. We studied the effects of several single-cell characteristics as well as population characteristics typically considered in the literature, but found little support for the hypothesis. While the classifier averages out effects of idiosyncratic tuning properties and inter-unit variability, its invariance is very much determined by the (hypothetical) ‘average’ neuron. Consequently, even at population level there exists a fundamental trade-off between selectivity and tolerance, and invariant behaviour does not emerge spontaneously.
doi:10.3389/neuro.10.003.2009
PMCID: PMC2647334  PMID: 19242556
object recognition; inferior temporal cortex; population coding; multidimensional tuning
11.  A Preference for Contralateral Stimuli in Human Object- and Face-Selective Cortex 
PLoS ONE  2007;2(6):e574.
Visual input from the left and right visual fields is processed predominantly in the contralateral hemisphere. Here we investigated whether this preference for contralateral over ipsilateral stimuli is also found in high-level visual areas that are important for the recognition of objects and faces. Human subjects were scanned with functional magnetic resonance imaging (fMRI) while they viewed and attended faces, objects, scenes, and scrambled images in the left or right visual field. With our stimulation protocol, primary visual cortex responded only to contralateral stimuli. The contralateral preference was smaller in object- and face-selective regions, and it was smallest in the fusiform gyrus. Nevertheless, each region showed a significant preference for contralateral stimuli. These results indicate that sensitivity to stimulus position is present even in high-level ventral visual cortex.
doi:10.1371/journal.pone.0000574
PMCID: PMC1894654  PMID: 17593973

Results 1-11 (11)