scholarly journals The Role of V1 Surround Suppression in MT Motion Integration

2010 ◽  
Vol 103 (6) ◽  
pp. 3123-3138 ◽  
Author(s):  
James M. G. Tsui ◽  
J. Nicholas Hunter ◽  
Richard T. Born ◽  
Christopher C. Pack
Keyword(s):  
Cortical Neurons ◽  
Temporal Dynamics ◽  
Macaque Monkey ◽  
Direction Selectivity ◽  
Extrastriate Cortex ◽  
Complex Stimulus ◽  
Specific Stimulus ◽  
Tuning Curves ◽  
Mt Neurons ◽  
Stimulus Features

Neurons in the primate extrastriate cortex are highly selective for complex stimulus features such as faces, objects, and motion patterns. One explanation for this selectivity is that neurons in these areas carry out sophisticated computations on the outputs of lower-level areas such as primary visual cortex (V1), where neuronal selectivity is often modeled in terms of linear spatiotemporal filters. However, it has long been known that such simple V1 models are incomplete because they fail to capture important nonlinearities that can substantially alter neuronal selectivity for specific stimulus features. Thus a key step in understanding the function of higher cortical areas is the development of realistic models of their V1 inputs. We have addressed this issue by constructing a computational model of the V1 neurons that provide the strongest input to extrastriate cortical middle temporal (MT) area. We find that a modest elaboration to the standard model of V1 direction selectivity generates model neurons with strong end-stopping, a property that is also found in the V1 layers that provide input to MT. With this computational feature in place, the seemingly complex properties of MT neurons can be simulated by assuming that they perform a simple nonlinear summation of their inputs. The resulting model, which has a very small number of free parameters, can simulate many of the diverse properties of MT neurons. In particular, we simulate the invariance of MT tuning curves to the orientation and length of tilted bar stimuli, as well as the accompanying temporal dynamics. We also show how this property relates to the continuum from component to pattern selectivity observed when MT neurons are tested with plaids. Finally, we confirm several key predictions of the model by recording from MT neurons in the alert macaque monkey. Overall our results demonstrate that many of the seemingly complex computations carried out by high-level cortical neurons can in principle be understood by examining the properties of their inputs.

Response selectivity of neurons in area MT of the macaque monkey during reversible inactivation of area V1

1992 ◽  
Vol 67 (6) ◽  
pp. 1437-1446 ◽  
Author(s):  
P. Girard ◽  
P. A. Salin ◽  
J. Bullier
Keyword(s):  
Receptive Field ◽  
Macaque Monkey ◽  
Direction Selectivity ◽  
Area 17 ◽  
Visual Responses ◽  
Area Mt ◽  
Reversible Inactivation ◽  
Mt Neurons ◽  
Preferred Direction ◽  
Blocking Index

1. Behavioral results in the monkey and clinical studies in human show remarkable residual visual capacities after a lesion of area V1. Earlier work by Rodman et al. demonstrated that visual activity can be recorded in the middle temporal area (MT) of the macaque monkey several weeks after a complete lesion of V1. These authors also tested the effect of a reversible block of area V1 on the visual responses of a small number of neurons in area MT and showed that most of these cells remain visually responsive. From the results of that study, however, it is difficult to assess the contribution of area 17 to the receptive-field selectivity of area MT neurons. To address this question, we have quantitatively measured the effects of a reversible inactivation of area 17 on the direction selectivity of MT neurons. 2. A circular part of the opercular region of area V1 was reversibly inactivated by cooling with a Peltier device. A microelectrode was positioned in the lower layers of V1 to control the total inactivation of that area. Eighty percent of the sites recorded in the retinotopically corresponding region of MT during inactivation of V1 were found to be visually responsive. The importance of the effect was assessed by calculating the blocking index (0 for no effect, 1 for complete inactivation). Approximately one-half of the quantitatively studied neurons gave a blocking index below 0.6, illustrating the strong residual responses recorded in many neurons. 3. Receptive-field properties were examined with multihistograms. It was found that, during inactivation of V1, the preferred direction changed for most neurons but remained close to the preferred direction or to its opposite in the control situation. During inactivation of V1, the average tuning curve of neurons became broader mostly because of strong reductions in the response to directions close to the preferred and nonpreferred. Very little change was observed in the responses for directions at 90 degrees to the optimal. These results are consistent with a model in which direction selectivity is present without an input from V1 but is reinforced by the spatial organization of this excitatory input. 4. Residual responses were found to be highly dependent on the state of anesthesia because they were completely abolished by the addition of 0.4-0.5% halothane to the ventilation gases. Finally, visual responses were recorded in area MT several hours after an acute lesion of area 17.(ABSTRACT TRUNCATED AT 400 WORDS)

Neural responses to visual texture patterns in middle temporal area of the macaque monkey

1992 ◽  
Vol 68 (1) ◽  
pp. 164-181 ◽  
Author(s):  
J. F. Olavarria ◽  
E. A. DeYoe ◽  
J. J. Knierim ◽  
J. M. Fox ◽  
D. C. van Essen
Keyword(s):  
Macaque Monkey ◽  
Direction Selectivity ◽  
Neural Responses ◽  
Temporal Area ◽  
Middle Temporal ◽  
Tuning Curves ◽  
Preferred Direction ◽  
Orientation Contrast ◽  
Background Texture ◽  
Moving Texture

1. We studied how neurons in the middle temporal visual area (MT) of anesthetized macaque monkeys responded to textured and nontextured visual stimuli. Stimuli contained a central rectangular ,figure- that was either uniform in luminance or consisted of an array of oriented line segments. The figure moved at constant velocity in one of four orthogonal directions. The region surrounding the figure was either uniform in luminance or contained a texture array (whose elements were identical or orthogonal in orientation to those of the figure), and it either was stationary or moved along with the figure. 2. A textured figure moving across a stationary textured background (,texture bar- stimulus) often elicited vigorous neural responses, but, on average, the responses to texture bars were significantly smaller than to solid (uniform luminance) bars. 3. Many cells showed direction selectivity that was similar for both texture bars and solid bars. However, on average, the direction selectivity measured when texture bars were used was significantly smaller than that for solid bars, and many cells lost significant direction selectivity altogether. The reduction in direction selectivity for texture bars generally reflected a combination of decreased responsiveness in the preferred direction and increased responsiveness in the null (opposite to preferred) direction. 4. Responses to a texture bar in the absence of a texture background (,texture bar alone-) were very similar to the responses to solid bars both in the magnitude of response and in the degree of direction selectivity. Conversely, adding a static texture surround to a moving solid bar reduced direction selectivity on average without a reduction in response magnitude. These results indicate that the static surround is largely responsible for the differences in direction selectivity for texture bars versus solid bars. 5. In the majority of MT cells studied, responses to a moving texture bar were largely independent of whether the elements in the bar were of the same orientation as the background elements or of the orthogonal orientation. Thus, for the class of stimuli we used, orientation contrast does not markedly affect the responses of MT neurons to moving texture patterns. 6. The optimum figure length and the shapes of the length tuning curves determined with the use of solid bars and texture bars differed significantly in most of the cells examined. Thus neurons in MT are not simply selective for a particular figure shape independent of whatever cues are used to delineate the figure.

scholarly journals Optimal Tuning Widths in Population Coding of Periodic Variables

2006 ◽  
Vol 18 (7) ◽  
pp. 1555-1576 ◽  
Author(s):  
Marcelo A. Montemurro ◽  
Stefano Panzeri
Keyword(s):  
Cortical Neurons ◽  
Neuronal Population ◽  
Population Coding ◽  
Model Parameters ◽  
Population Activity ◽  
Sensory Stimuli ◽  
Tuning Curves ◽  
Periodic Stimulus ◽  
Tuning Width ◽  
Stimulus Features

We study the relationship between the accuracy of a large neuronal population in encoding periodic sensory stimuli and the width of the tuning curves of individual neurons in the population. By using general simple models of population activity, we show that when considering one or two periodic stimulus features, a narrow tuning width provides better population encoding accuracy. When encoding more than two periodic stimulus features, the information conveyed by the population is instead maximal for finite values of the tuning width. These optimal values are only weakly dependent on model parameters and are similar to the width of tuning to orientation ormotion direction of real visual cortical neurons. A very large tuning width leads to poor encoding accuracy, whatever the number of stimulus features encoded. Thus, optimal coding of periodic stimuli is different from that of nonperiodic stimuli, which, as shown in previous studies, would require infinitely large tuning widths when coding more than two stimulus features.

Neuronal responses in extrastriate cortex to objects in optic flow fields

1997 ◽  
Vol 14 (5) ◽  
pp. 879-895 ◽  
Author(s):  
Helen Sherk ◽  
Kathleen Mulligan ◽  
Jong-Nam Kim
Keyword(s):  
Optic Flow ◽  
Cortical Neurons ◽  
Flow Fields ◽  
Target Object ◽  
Direction Selectivity ◽  
Object Motion ◽  
Extrastriate Cortex ◽  
Flow Simulations ◽  
Preferred Direction ◽  
Visual Cortical

AbstractDuring locomotion, observers respond to objects in the environment that may represent obstacles to avoid or landmarks for navigation. Although much is known about how visual cortical neurons respond to stimulus objects moving against a blank background, nothing is known about their responses when objects are embedded in optic flow fields (the patterns of motion seen during locomotion). We recorded from cells in the lateral suprasylvian visual area (LS) of the cat, an area probably analogous to area MT. In our first experiments, optic flow simulations mimicked the view of a cat trotting across a plain covered with small balls; a black bar lying on the balls served as a target object. In subsequent experiments, optic flow simulations were composed of natural elements, with target objects representing bushes, rocks, and variants of these. Cells did not respond to the target bar in the presence of optic flow backgrounds, although they did respond to it in the absence of a background. However, 273/423 cells responded to at least one of the taller, naturalistic objects embedded in optic flow simulations. These responses might represent a form of image segmentation, in that cells detected objects against a complex background. Surprisingly, the responsiveness of cells to objects in optic flow fields was not correlated with preferred direction as measured with a moving bar or whole-field texture. Because the direction of object motion was determined solely by receptive-field location, it often differed considerably from a cell's preferred direction. About a quarter of the cells responded well to objects in optic flow movies but more weakly or not at all to bars moving in the same direction as the object, suggesting that the optic flow background modified or suppressed direction selectivity.

Receptive field properties of neurons in area V3 of macaque monkey extrastriate cortex

1987 ◽  
Vol 57 (4) ◽  
pp. 889-920 ◽  
Author(s):  
D. J. Felleman ◽  
D. C. Van Essen
Keyword(s):  
Receptive Field ◽  
Binocular Disparity ◽  
Visual Fields ◽  
Direction Selectivity ◽  
Extrastriate Cortex ◽  
Thin Strip ◽  
Stimulus Motion ◽  
Form Analysis ◽  
Tuning Curves ◽  
Receptive Field Properties

Receptive field properties of 147 neurons histologically verified to be located in area V3 were investigated during semichronic recording from paralyzed anesthetized macaque monkeys. Quantitative analyses were made of neuron selectivities for direction, orientation, speed, binocular disparity, and color. The majority of neurons in V3 (76%) were strongly orientation selective; 40% demonstrated strong direction selectivity. Most cells were tuned for stimulus speed and almost half showed optimum responses at 16 degrees/s. The distribution of optimum speeds ranged primarily from 4 to 32 degrees/s. Several cells in V3 displayed multi-peaked orientation- and/or direction-tuning curves. These cells had two or more narrowly tuned peaks that were not co-axial. In some ways, they resemble higher-order hypercomplex cells of cat area 19 and may subserve a higher level of form or motion analysis than is seen at antecedent visual areas. Roughly half (45%) of the cells were selective for binocular disparity. Approximately half of these were tuned excitatory in that they showed weak responses when tested through either eye alone, but showed strong binocular facilitation centered on the fixation plane. The other disparity-selective cells were tuned inhibitory or asymmetric in their responses in front and behind the fixation plane. Contrary to previous reports, approximately 20% of the neurons in V3 were color selective in terms of showing a severalfold greater response to the best monochromatic wavelength compared with the worst. Color-tuning curves of the subset of color selective cells had, on average, a full bandwidth at half maximum response of 80-100 nm. A comparison of the receptive field properties of neurons in V3 to those in other areas of visual cortex suggests that V3, like MT, is well suited for the analysis of several aspects of stimulus motion. V3 may also be involved in some aspects of form analysis, particularly at low contrast levels. Comparison with area VP, a thin strip of cortex anterior to ventral V2, which was previously considered part of V3, indicates that direction selectivity is much more prevalent in V3 than in VP. Conversely, color-selective cells are the majority in VP but a minority in V3. This suggests that visual information is processed differently in the upper and lower visual fields.

scholarly journals Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey

1996 ◽  
Vol 8 (6) ◽  
pp. 1185-1202 ◽  
Author(s):  
Wyeth Bair ◽  
Christof Koch
Keyword(s):  
Cortical Neurons ◽  
Action Potentials ◽  
Cortical Area ◽  
Macaque Monkey ◽  
Spike Trains ◽  
Random Motion ◽  
Extrastriate Cortex ◽  
Temporal Modulation ◽  
Temporal Precision ◽  
Neuronal Excitation

How reliably do action potentials in cortical neurons encode information about a visual stimulus? Most physiological studies do not weigh the occurrences of particular action potentials as significant but treat them only as reflections of average neuronal excitation. We report that single neurons recorded in a previous study by Newsome et al. (1989; see also Britten et al. 1992) from cortical area MT in the behaving monkey respond to dynamic and unpredictable motion stimuli with a markedly reproducible temporal modulation that is precise to a few milliseconds. This temporal modulation is stimulus dependent, being present for highly dynamic random motion but absent when the stimulus translates rigidly.

scholarly journals Categorically distinct types of receptive fields in early visual cortex

2016 ◽  
Vol 115 (5) ◽  
pp. 2556-2576 ◽  
Author(s):  
Vargha Talebi ◽  
Curtis L. Baker
Keyword(s):  
Visual Cortex ◽  
System Identification ◽  
Cortical Neurons ◽  
Temporal Dynamics ◽  
High Reliability ◽  
Receptive Fields ◽  
Direction Selectivity ◽  
Natural Image ◽  
Visual Responses ◽  
Neurophysiological Studies

In the visual cortex, distinct types of neurons have been identified based on cellular morphology, response to injected current, or expression of specific markers, but neurophysiological studies have revealed visual receptive field (RF) properties that appear to be on a continuum, with only two generally recognized classes: simple and complex. Most previous studies have characterized visual responses of neurons using stereotyped stimuli such as bars, gratings, or white noise and simple system identification approaches (e.g., reverse correlation). Here we estimate visual RF models of cortical neurons using visually rich natural image stimuli and regularized regression system identification methods and characterize their spatial tuning, temporal dynamics, spatiotemporal behavior, and spiking properties. We quantitatively demonstrate the existence of three functionally distinct categories of simple cells, distinguished by their degree of orientation selectivity (isotropic or oriented) and the nature of their output nonlinearity (expansive or compressive). In addition, these three types have differing average values of several other properties. Cells with nonoriented RFs tend to have smaller RFs, shorter response durations, no direction selectivity, and high reliability. Orientation-selective neurons with an expansive output nonlinearity have Gabor-like RFs, lower spontaneous activity and responsivity, and spiking responses with higher sparseness. Oriented RFs with a compressive nonlinearity are spatially nondescript and tend to show longer response latency. Our findings indicate multiple physiologically defined types of RFs beyond the simple/complex dichotomy, suggesting that cortical neurons may have more specialized functional roles rather than lying on a multidimensional continuum.

Simulated Optic Flow and Extrastriate Cortex. II. Responses to Bar Versus Large-Field Stimuli

1997 ◽  
Vol 77 (2) ◽  
pp. 562-570 ◽  
Author(s):  
Kathleen Mulligan ◽  
Jong-Nam Kim ◽  
Helen Sherk
Keyword(s):  
Optic Flow ◽  
Preceding Paper ◽  
Forward Direction ◽  
Direction Selectivity ◽  
Image Motion ◽  
Extrastriate Cortex ◽  
Large Field ◽  
Response Properties ◽  
Tuning Curves ◽  
Direction Tuning

Mulligan, Kathleen, Jong-Nam Kim, and Helen Sherk. Simulated optic flow and extrastriate cortex. II. Responses to bar versus large-field stimuli. J. Neurophysiol. 77: 562–570, 1997. In the preceding paper we described the responses of cells in the cat's lateral suprasylvian visual area (LS) to large-field optic flow and texture movies. To assess response properties such as direction selectivity, cells were also tested with moving bar stimuli. We expected that there would be good agreement between response properties elicited with optic flow movies and those revealed with bar stimuli. We first asked how well bar response properties predicted responsiveness to optic flow movies. There was no correlation between responsiveness to movies and the degree of end-stopping, length summation, or preference for bars that accelerated and expanded. We then considered only the 322 cells that responded to both bars and optic flow or texture movies and asked how well the strength of their response to movies could be predicted from the direction-tuning curves generated with bar stimuli. One-third of these cells responded much more strongly to movies than could be predicted from their direction-tuning curves. Generally, such cells were rather well tuned for the direction of bar motion and preferred a direction substantially different from what they saw in optic flow movies. Optic flow movies shown in the forward direction were the most effective variety of movie for two-thirds of these cells. To see whether this outcome stemmed from differential direction tuning for bars and large multielement displays, in a second series of experiments we compared direction tuning for bars and large-field texture movies. Many cells showed substantially different direction tuning for the two kinds of stimulus: almost [Formula: see text] of 409 cells had tuning curves that overlapped each other by <50%. But only a small number of cells (<10%) responded much better to texture movies than to bars in the predominant direction of image motion in optic flow movies. This result, like that reported in the preceding paper, suggests that cells in LS respond differently to optic flow than to texture displays lacking optic flow motion cues.

Temporal Dynamics of Direction Tuning in Motion-Sensitive Macaque Area MT

2005 ◽  
Vol 93 (4) ◽  
pp. 2104-2116 ◽  
Author(s):  
János A. Perge ◽  
Bart G. Borghuis ◽  
Roger J. E. Bours ◽  
Martin J. M. Lankheet ◽  
Richard J. A. van Wezel
Keyword(s):  
Temporal Dynamics ◽  
Random Sequence ◽  
Nonlinear Effects ◽  
Direction Selectivity ◽  
Second Phase ◽  
Motion Direction ◽  
Area Mt ◽  
Mt Neurons ◽  
Static Nonlinearity ◽  
Preferred Direction

We studied the temporal dynamics of motion direction sensitivity in macaque area MT using a motion reverse correlation paradigm. Stimuli consisted of a random sequence of motion steps in eight different directions. Cross-correlating the stimulus with the resulting neural activity reveals the temporal dynamics of direction selectivity. The temporal dynamics of direction selectivity at the preferred speed showed two phases along the time axis: one phase corresponding to an increase in probability for the preferred direction at short latencies and a second phase corresponding to a decrease in probability for the preferred direction at longer latencies. The strength of this biphasic behavior varied between neurons from weak to very strong and was uniformly distributed. Strong biphasic behavior suggests optimal responses for motion steps in the antipreferred direction followed by a motion step in the preferred direction. Correlating spikes to combinations of motion directions corroborates this distinction. The optimal combination for weakly biphasic cells consists of successive steps in the preferred direction, whereas for strongly biphasic cells, it is a reversal of directions. Comparing reverse correlograms to combinations of stimuli to predictions based on correlograms for individual directions revealed several nonlinear effects. Correlations for successive presentations of preferred directions were smaller than predicted, which could be explained by a static nonlinearity (saturation). Correlations to pairs of (nearly) opposite directions were larger than predicted. These results show that MT neurons are generally more responsive when sudden changes in motion directions occur, irrespective of the preferred direction of the neurons. The latter nonlinearities cannot be explained by a simple static nonlinearity at the output of the neuron, but most likely reflect network interactions.

scholarly journals Combining fMRI and Eye-tracking for the Study of Social Cognition

2021 ◽  
Vol 16 ◽  
pp. 263310552110654
Author(s):  
Kristin Marie Rusch
Keyword(s):  
Social Cognition ◽  
Eye Tracking ◽  
Linear Models ◽  
Ecological Validity ◽  
Parametric Models ◽  
Neural Systems ◽  
Complex Stimulus ◽  
Specific Stimulus ◽  
Neural Processes ◽  
Stimulus Features

The study of social cognition with functional magnetic resonance imaging (fMRI) affords the use of complex stimulus material. Visual attention to distinct aspects of these stimuli can result in the involvement of remarkably different neural systems. Usually, the influence of gaze on neural signal is either disregarded or dealt with by controlling gaze of participants through instructions or tasks. However, behavioral restrictions like this limit the study’s ecological validity. Thus, it would be preferable if participants freely look at the stimuli while their gaze traces are measured. Yet several impediments hamper a combination of fMRI and eye-tracking. In our recent work on neural Theory of Mind processes in alexithymia, we propose a simple way of integrating dwell time on specific stimulus features into general linear models of fMRI data. By parametrically modeling fixations, we were able to distinguish neural processes asssociated with specific stimulus features looked at. Here, I discuss opportunities and obstacles of this approach in more detail. My goal is to motivate a wider use of parametric models — usually implemented in common fMRI software packages — to combine fMRI and eye-tracking data.

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