Biomedical subjects
Publications and source records attributed to J L Gallant.
Neural models that simulate single spike trains can help us understand the basic principles of neural coding in vision. Keat et al. (2001) develop a hybrid model that combines spatiotemporal filtering with nonlinear spike generation. The model does a good job of predicting the responses of single retinal ganglion cells and thalamic relay neurons.
We present a generalized reverse correlation technique that can be used to estimate the spatio-temporal receptive fields (STRFs) of sensory neurons from their responses to arbitrary stimuli such as auditory vocalizations or natural visual scenes. The general solution for STRF estimation requires normalization of the stimulus-response cross-correlation by the stimulus autocorrelation matrix. When the second-order stimulus statistics are stationary, normalization involves only the diagonal elements of the Fourier-transformed auto-correlation matrix (the power spectrum). In the non-stationary case normalization requires the entire auto-correlation matrix. We present modelling studies that demonstrate the feasibility and accuracy of this method as well as neurophysiological data comparing STRFs estimated using natural versus synthetic stimulus ensembles. For both auditory and visual neurons, STRFs obtained with these different stimuli are similar, but exhibit systematic differences that may be functionally significant. This method should be useful for determining what aspects of natural signals are represented by sensory neurons and may reveal novel response properties of these neurons.
Our understanding of the neural basis of object recognition is based primarily on work with non-human primates. The problem has recently been addressed in humans using functional magnetic resonance imaging; new results indicate that the lateral occipital complex plays an important role in human object recognition.
Theoretical studies suggest that primary visual cortex (area V1) uses a sparse code to efficiently represent natural scenes. This issue was investigated by recording from V1 neurons in awake behaving macaques during both free viewing of natural scenes and conditions simulating natural vision. Stimulation of the nonclassical receptive field increases the selectivity and sparseness of individual V1 neurons, increases the sparseness of the population response distribution, and strongly decorrelates the responses of neuron pairs. These effects are due to both excitatory and suppressive modulation of the classical receptive field by the nonclassical receptive field and do not depend critically on the spatiotemporal structure of the stimuli. During natural vision, the classical and nonclassical receptive fields function together to form a sparse representation of the visual world. This sparse code may be computationally efficient for both early vision and higher visual processing.
Extrastriate area V4 is crucial for intermediate form vision and visual attention in nonhuman primates. Human neuroimaging suggests that an area in the lingual sulcus/fusiform gyrus may correspond to ventral V4 (V4v). We studied a human neurological patient, AR, with a putative V4v lesion. The lesion does not affect early visual processing (luminance, orientation, and motion perception). However, it does impair hue perception, intermediate form vision, and visual attention in the upper contralateral visual field. Form deficits occur during discrimination of illusory borders, Glass patterns, curvature, and non-Cartesian patterns. Attention deficits occur during discrimination of the relative positions of object parts, detection of low-salience targets, and orientation discrimination in the presence of distractors. This pattern of deficits is consistent with the known properties of area V4 in nonhuman primates, indicating that AR's lesion affects a cortical region functionally homologous to macaque V4.
Under natural viewing conditions primates make frequent exploratory eye movements across complex scenes. We recorded neural activity of 62 cells in visual areas V1, V2 and V4 in an awake behaving monkey that freely viewed natural images. About half of the cells studied showed a modulation in firing rate following some of the eye movements made during free viewing, though the proportions showing a discernible modulation varied across areas. These cells were also examined under controlled viewing conditions in which gratings or natural image patches were flashed in and around the classical receptive field while the animal performed a fixation task. Activity rates were generally highest with flashed gratings and lowest during free viewing. Flashed natural image patches evoked responses between these two extremes, and the responses were higher when the patches were confined to the classical receptive field than when they extended into the non-classical surround. Thus the reduction of activity during free viewing relative to that obtained with flashed gratings is partly attributable to natural images being less effective stimuli and partly to suppressive spatio-temporal neural mechanisms that are important during natural vision.
Under natural viewing conditions primates make frequent exploratory eye movements across complex scenes. We recorded neural activity of 62 cells in visual areas V1, V2 and V4 in an awake behaving monkey that freely viewed natural images. About half of the cells studied showed a modulation in firing rate following some of the eye movements made during free viewing, though the proportions showing a discernible modulation varied across areas. These cells were also examined under controlled viewing conditions in which gratings or natural image patches were flashed in and around the classical receptive field while the animal performed a fixation task. Activity rates were generally highest with flashed gratings and lowest during free viewing. Flashed natural image patches evoked responses between these two extremes, and the responses were higher when the patches were confined to the classical receptive field than when they extended into the non-classical surround. Thus the reduction of activity during free viewing relative to that obtained with flashed gratings is partly attributable to natural images being less effective stimuli and partly to suppressive spatio-temporal neural mechanisms that are important during natural vision.
Under natural viewing conditions primates make frequent exploratory eye movements across complex scenes. We recorded neural activity of 62 cells in visual areas V1, V2 and V4 in an awake behaving monkey that freely viewed natural images. About half of the cells studied showed a modulation in firing rate following some of the eye movements made during free viewing, though the proportions showing a discernible modulation varied across areas. These cells were also examined under controlled viewing conditions in which gratings or natural image patches were flashed in and around the classical receptive field while the animal performed a fixation task. Activity rates were generally highest with flashed gratings and lowest during free viewing. Flashed natural image patches evoked responses between these two extremes, and the responses were higher when the patches were confined to the classical receptive field than when they extended into the non-classical surround. Thus the reduction of activity during free viewing relative to that obtained with flashed gratings is partly attributable to natural images being less effective stimuli and partly to suppressive spatio-temporal neural mechanisms that are important during natural vision.
Focal visual attention typically produces enhanced perceptual processing at the psychological level and relatively stronger neural responses at the physiological level. A longstanding mechanistic question is whether these attentional effects pertain specifically to the attended (target) object or to the region of space it occupies. We show here that attentional response enhancement in macaque area V4 extends to behaviorally irrelevant objects in the vicinity of the target object, indicating that focal attention has a strong spatial component at the physiological level. In addition, we find that spatial attention effects typically show a striking directional asymmetry. The direction of the asymmetry varies between cells, so that some cells respond best when attention is directed to the left of the stimulus, some when attention is directed to the right, etc. Thus, attention involves not only enhanced responses to behavioral targets but also a complex modulation of responses to other stimuli in the surrounding visual space.
1. We studied the spatial interaction between stimulus and attention in macaque area V4. Monkeys were required to fixate a small spot while continuously attending to a ring-shaped target within a large array of identical rings. Meanwhile, the response of the V4 cell under study was tested by flashing behaviorally irrelevant bar stimuli in the cell's classical receptive field (CRF). The location of the attended ring was varied across four positions surrounding the CRF, and the location of the bar stimulus was varied across five positions spanning the CRF. 2. Response strength depended on two aspects of the spatial relationship between the stimulus driving the cell (the bar) and the position of attention (the target ring). First, for 49% of the cells studied, responses were greater for bar stimuli near the attended ring; i.e., the receptive field profile shifted toward the attentional focus. Second, for 84% of the cells, the overall response level depended on the direction in which attention lay relative to the stimulus in the CRF (e.g., to the left, right, above, or below). 3. This study confirms a key prediction of spatial models of attention, which postulate enhanced processing of all stimuli near the attentional focus. It also introduces the novel finding that responses are influenced by the relative direction of attention. This result indicates that area V4 carries information about the spatial relationship between visual stimuli and attention.
1. We studied the responses of 103 neurons in visual area V4 of anesthetized macaque monkeys to two novel classes of visual stimuli, polar and hyperbolic sinusoidal gratings. We suspected on both theoretical and experimental grounds that these stimuli would be useful for characterizing cells involved in intermediate stages of form analysis. Responses were compared with those obtained with conventional Cartesian sinusoidal gratings. Five independent, quantitative analyses of neural responses were carried out on the entire population of cells. 2. For each cell, responses to the most effective Cartesian, polar, and hyperbolic grating were compared directly. In 18 of 103 cells, the peak response evoked by one stimulus class was significantly different from the peak response evoked by the remaining two classes. Of the remaining 85 cells, 74 had response peaks for the three stimulus classes that were all within a factor of 2 of one another. 3. An information-theoretic analysis of the trial-by-trial responses to each stimulus showed that all but two cells transmitted significant information about the stimulus set as a whole. Comparison of the information transmitted about each stimulus class showed that 23 of 103 cells transmitted a significantly different amount of information about one class than about the remaining two classes. Of the remaining 80 cells, 55 had information transmission rates for the three stimulus classes that were all within a factor of 2 of one another. 4. To identify cells that had orderly tuning profiles in the various stimulus spaces, responses to each stimulus class were fit with a simple Gaussian model. Tuning curves were successfully fit to the data from at least one stimulus class in 98 of 103 cells, and such fits were obtained for at least two classes in 87 cells. Individual neurons showed a wide range of tuning profiles, with response peaks scattered throughout the various stimulus spaces; there were no major differences in the distributions of the widths or positions of tuning curves obtained for the different stimulus classes. 5. Neurons were classified according to their response profiles across the stimulus set with two objective methods, hierarchical cluster analysis and multidimensional scaling. These two analyses produced qualitatively similar results. The most distinct group of cells was highly selective for hyperbolic gratings. The majority of cells fell into one of two groups that were selective for polar gratings: one selective for radial gratings and one selective for concentric or spiral gratings. There was no group whose primary selectivity was for Cartesian gratings. 6. To determine whether cells belonging to identified classes were anatomically clustered, we compared the distribution of classified cells across electrode penetrations with the distribution that would be expected if the cells were distributed randomly. Cells with similar response profiles were often anatomically clustered. 7. A position test was used to determine whether response profiles were sensitive to precise stimulus placement. A subset of Cartesian and non-Cartesian gratings was presented at several positions in and near the receptive field. The test was run on 13 cells from the present study and 28 cells from an earlier study. All cells showed a significant degree of invariance in their selectivity across changes in stimulus position of up to 0.5 classical receptive field diameters. 8. A length and width test was used to determine whether cells preferring non-Cartesian gratings were selective for Cartesian grating length or width. Responses to Cartesian gratings shorter or narrower than the classical receptive field were compared with those obtained with full-field Cartesian and non-Cartesian gratings in 29 cells. Of the four cells that had shown significant preferences for non-Cartesian gratings in the main test, none showed tuning for Cartesian grating length or width that would account for their non-Cartesian res
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The neural basis of pattern recognition is a central problem in visual neuroscience. Responses of single cells were recorded in area V4 of macaque monkey to three classes of periodic stimuli that are based on spatial derivative operators: polar (concentric and radial), hyperbolic, and conventional sinusoidal (Cartesian) gratings. Of 118 cells tested, 16 percent responded significantly more to polar or hyperbolic (non-Cartesian) gratings than to Cartesian gratings and only 8 percent showed a significant preference for Cartesian gratings. Among cells selective for non-Cartesian gratings, those that preferred concentric gratings were most common. Cells selective for non-Cartesian gratings may constitute an important intermediate stage in pattern recognition and the representation of surface shape.
Cells in area V1 of the anesthetized macaque monkey were stimulated with large texture patterns composed of homogeneous regions of line elements (texels) with different orientations. To human observers, such patterns appear to segregate, with the percept of sharp boundaries between texture regions. Our objective was to investigate whether the boundaries are reflected in the responses of single cells in V1. We measured responses to individual texels at different distances from the texture border. For each cell, patterns of optimally or orthogonally orientated texels were adjusted so that only one texel fell into the receptive field and all other texels fell in the visually unresponsive regions outside. In 37 out of 156 neurons tested (24%), texels immediately adjacent to a texture border evoked reliably larger responses than identical texels farther away from the border. In 17 neurons (11%), responses to texels near the border were relatively reduced. Border enhancement effects were generally stronger than border attenuation effects. When tested with four different border configurations (two global orientations and two edge polarities), many cells showed reliable effects for only one or two configurations, consistent with cells encoding information about the orientation of the texture border or its location with respect to the segmented region. Across the sample, enhancement effects were similar for all texture borders. Modulation by the texture surround was predominantly suppressive; even the responses near texture borders were smaller than those to a single line. We compared these results with the results of a popout test in which the line in the receptive field was surrounded by homogeneous texture fields either orthogonal or parallel to the center line. The patterns of response modulation and the temporal onset of differential responses were similar in the two tests, suggesting that the two perceptual phenomena are mediated by similar neural mechanisms.
We studied the effects of contextual modulation in area V1 of anesthetized macaque monkeys. In 146 cells, responses to a single line over the center of the receptive field were compared with those to full texture patterns in which the center line was surrounded by similar lines at either the same orientation (uniform texture) or the orthogonal orientation (orientation contrast). On average, the responses to single lines were reduced by 42% when texture was presented in the surround. Uniform textures often produced stronger suppression (7% more, on average) so that lines with orientation contrast on average evoked larger responses than lines in uniform texture fields. This difference is correlated with perceptual differences between such stimuli, suggesting that physiological mechanisms contributing to the saliency ("popout") of textural stimuli operate, at least to some degree, even under anesthesia. Significant response modulation by the texture surround was seen in 112 cells (77%). Fifty-three cells (36%) responded differently to the two texture patterns; response preferences for orientation contrast (35 cells; 24%) were seen more often than preferences for uniform textures (18 cells; 12%). The remaining 59 cells (40%) were similarly suppressed by both texture surrounds. Detailed analysis of texture modulation revealed two major components of surround effects: (1) fast nonspecific ("general") suppression that occurred at about the same latency as excitatory responses and was found in all layers of striate cortex; and (2) differential response modulation that began about 60-70 ms after stimulus onset (about 15-20 ms after the onset of the excitatory response) and was less homogeneously distributed over cortical layers.