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.

Are the preferred directions of neurons in cat extrastriate cortex related to optic flow?

1995 ◽  
Vol 12 (5) ◽  
pp. 887-894 ◽  
Author(s):  
Helen Sherk ◽  
Jong-Nam Kim ◽  
Kathleen Mulligan
Keyword(s):  
Receptive Field ◽  
Optic Flow ◽  
Visual Analysis ◽  
Receptive Fields ◽  
Image Motion ◽  
Extrastriate Cortex ◽  
Clear Correlation ◽  
Lower Visual Field ◽  
Preferred Direction ◽  
Flow Directions

AbstractIt has been proposed that one area of extrastriate cortex in the cat, the lateral suprasylvian area (LS), plays an important role in visual analysis during locomotion (Rauschecker et al., 1987). Cells in LS reportedly tend to prefer directions along a trajectory originating at the center of gaze, and passing outward through the receptive-field center. Such directions coincide with the directions of image motion in an optic flow field, the pattern seen by locomoting observers when they fixate the point towards which they are heading (Gibson, 1950). We re-examined this issue for cells in LS with receptive fields in the lower visual field. Cells recorded posterior to Horsley-Clarke A2 showed a clear correlation between preferred direction and receptive-field location, but not that predicted: preferred directions were generally orthogonal to “optic flow” directions. Since these cells were all located posterior to those in studies showing a bias for “optic flow” directions, we hypothesized that there are two cell populations within LS, an anterior population that tends to prefer radial-outward directions, and a posterior population that tends to prefer directions orthogonal to radial. Data from earlier mapping experiments (Sherk & Mulligan, 1993) supported this idea.

scholarly journals Orientation and Direction Selectivity of Synaptic Inputs in Visual Cortical Neurons

Neuron ◽  
2003 ◽  
Vol 37 (4) ◽  
pp. 663-680 ◽  
Author(s):  
Cyril Monier ◽  
Frédéric Chavane ◽  
Pierre Baudot ◽  
Lyle J Graham ◽  
Yves Frégnac
Keyword(s):  
Cortical Neurons ◽  
Direction Selectivity ◽  
Synaptic Inputs ◽  
Visual Cortical

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.

scholarly journals Playing the electric light orchestra—how electrical stimulation of visual cortex elucidates the neural basis of perception

2015 ◽  
Vol 370 (1677) ◽  
pp. 20140206 ◽  
Author(s):  
Nela Cicmil ◽  
Kristine Krug
Keyword(s):  
Electrical Stimulation ◽  
Visual Cortex ◽  
Information Integration ◽  
Cortical Neurons ◽  
Extrastriate Cortex ◽  
Neural Basis ◽  
Electrical Microstimulation ◽  
Neuronal Mechanisms ◽  
Visual Cortical ◽  
Stimulation Of

Vision research has the potential to reveal fundamental mechanisms underlying sensory experience. Causal experimental approaches, such as electrical microstimulation, provide a unique opportunity to test the direct contributions of visual cortical neurons to perception and behaviour. But in spite of their importance, causal methods constitute a minority of the experiments used to investigate the visual cortex to date. We reconsider the function and organization of visual cortex according to results obtained from stimulation techniques, with a special emphasis on electrical stimulation of small groups of cells in awake subjects who can report their visual experience. We compare findings from humans and monkeys, striate and extrastriate cortex, and superficial versus deep cortical layers, and identify a number of revealing gaps in the ‘causal map′ of visual cortex. Integrating results from different methods and species, we provide a critical overview of the ways in which causal approaches have been used to further our understanding of circuitry, plasticity and information integration in visual cortex. Electrical stimulation not only elucidates the contributions of different visual areas to perception, but also contributes to our understanding of neuronal mechanisms underlying memory, attention and decision-making.

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.

MST Neurons Respond to Optic Flow and Translational Movement

1998 ◽  
Vol 80 (4) ◽  
pp. 1816-1827 ◽  
Author(s):  
Charles J. Duffy
Keyword(s):  
Optic Flow ◽  
Direction Selectivity ◽  
Movement Detection ◽  
Temporal Area ◽  
Medial Superior Temporal ◽  
Preferred Direction ◽  
Translational Movement ◽  
Mst Neurons ◽  
Best Responses ◽  
Stimulus Direction

Duffy, Charles J. MST neurons respond to optic flow and translational movement . J. Neurophysiol. 80: 1816–1827, 1998. We recorded the responses of 189 medial superior temporal area (MST) neurons by using optic flow, real translational movement, and combined stimuli in which matching directions of optic flow and real translational movement were presented together. One-half of the neurons (48%) showed strong responses to optic flow simulating self-movement in the horizontal plane, and 24% showed strong responses to translational movement. Combining optic flow stimuli with matching directions of translational movement caused substantial changes in both the amplitude of the best responses (44% of neurons) and the strength of direction selectivity (71% of neurons), with little effect on which stimulus direction was preferred. However, combining optic flow and translational movement such that opposite directions were presented together changed the preferred direction in 45% of the neurons with substantial changes in the strength of direction selectivity. These studies suggest that MST neurons combine visual and vestibular signals to enhance self-movement detection and disambiguate optic flow that results from either self-movement or the movement of large objects near the observer.

Responses in extrastriate cortex to optic flow during simulated turns

2002 ◽  
Vol 19 (4) ◽  
pp. 409-419 ◽  
Author(s):  
HELEN SHERK ◽  
JONG-NAM KIM
Keyword(s):  
Optic Flow ◽  
Contralateral Side ◽  
Direction Selectivity ◽  
Image Motion ◽  
Extrastriate Cortex ◽  
Straight Line ◽  
Parallel Motion ◽  
Flow Motion ◽  
Receptive Field Center

An observer locomoting along a straight path sees a pattern of optic flow in which images move approximately radially outward from his heading point. If the observer turns, the optic flow field changes markedly because each object's image now has a component of horizontal motion added to its “optic flow” motion. We tested the responses of 326 cells in the cat's extrastriate area LS (lateral suprasylvian visual area) to movies simulating the optic flow seen during locomotion in a straight line, and during various simulated turns to the left and right. About 60% of 326 cells tested responded to optic flow simulating turns. Of most interest was a subset of cells, 15% of the total, that had “turn-selective” responses. They responded significantly better to turns in a particular direction (usually to the contralateral side) than to turns in the opposite direction or to optic flow simulating straight-ahead locomotion. For each cell, we generated a display of fronto-parallel motion with a direction and speed that matched the image motion in the preferred turn movie, as seen at the receptive-field center. Most turn-selective cells responded significantly better to their preferred turn movie than to this fronto-parallel stimulus. We examined the role of cells' selectivity for stimulus direction, speed, and acceleration in determining cell preference for particular turns. Direction preference played some role for most cells, but about a third of the cells preferred turn movies that did not reflect their direction selectivity. Other factors, including the presence of opposing motion, only rarely appeared to determine cell preferences for particular turn movies.

Direction-selective adaptation in simple and complex cells in cat striate cortex

1988 ◽  
Vol 59 (4) ◽  
pp. 1314-1330 ◽  
Author(s):  
S. G. Marlin ◽  
S. J. Hasan ◽  
M. S. Cynader
Keyword(s):  
Cortical Neurons ◽  
Striate Cortex ◽  
Selective Adaptation ◽  
Direction Selectivity ◽  
Simple Cells ◽  
Complex Cells ◽  
Preferred Direction ◽  
Directional Preference ◽  
Simple And Complex Cells ◽  
Cat Striate Cortex

1. The selectivity of adaptation to unidirectional motion was examined in neurons of the cat striate cortex. Following prolonged stimulation with a unidirectional high-contrast grating, the responsivity of cortical neurons was reduced. In many units this decrease was restricted to the direction of prior stimulation. This selective adaptation produced changes in the degree of direction selectivity of the cortical units (as measured by the ratio of the response to motion in the preferred direction to that in the nonpreferred direction). 2. The initial strength of the directional preference of a given cortical unit did not determine the degree of direction-selective adaptation. Indeed, even non-direction-selective units could exhibit pronounced direction-selective adaptation. The degree of direction-selective adaptation was also independent of the overall decrease in responsivity during adaptation. 3. There was no difference between simple and complex cells in the total amount of adaptation observed. The selectivity of the adaptation, however, did differ between these two cell types. As a group, simple cells showed significant direction-selective adaptation, whereas complex cells did not. The directional preference of most simple cells decreased following preferred direction adaptation and many highly direction selective simple cells became non-direction selective. In addition, simple cells became significantly more direction selective following nonpreferred direction adaptation. 4. Some complex cells also demonstrated direction-selective adaptation. There was, however, much more variability among complex cells than simple cells. Some complex cells actually increased direction selectivity following preferred direction adaptation. These differences between simple and complex cells suggest that changes in direction selectivity following unidirectional adaptation are not due to simple neuronal fatigue of the unit being recorded, but depend on selective adaptation of afferent inputs to the unit. 5. The spontaneous activity of many cortical neurons decreased following preferred direction adaptation but increased following adaptation in the nonpreferred direction. The response to a stationary grating also decreased following preferred direction adaptation. However, there was very little change in the response to a stationary grating following adaptation in the nonpreferred direction.

Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity to large-field stimuli

1991 ◽  
Vol 65 (6) ◽  
pp. 1329-1345 ◽  
Author(s):  
C. J. Duffy ◽  
R. H. Wurtz
Keyword(s):  
Optic Flow ◽  
Receptive Fields ◽  
Flow Fields ◽  
Direction Selectivity ◽  
Single Component ◽  
Large Field ◽  
Temporal Area ◽  
Response Characteristics ◽  
Medial Superior Temporal ◽  
Mst Neurons

1. Neurons in the dorsomedial region of the medial superior temporal area (MSTd) have large receptive fields that include the fovea, are directionally selective for moving visual stimuli, prefer the motion of large fields to small spots, and respond to rotating and expanding patterns of motion as well as frontal parallel planar motion. These characteristics suggested that these neurons might contribute to the analysis of the large-field optic flow stimulation generated as an observer moves through the visual environment. 2. We tested the response of MSTd neurons in two awake monkeys by systematically presenting a set of translational and rotational stimuli to each neuron. These 100 X 100 degrees stimuli were the motion components from which all optic flow fields are derived. 3. In 220 single neurons we found 23% that responded primarily to one component of motion (planar, circular, or radial), 34% that responded to two components (planocircular or planoradial, but never circuloradial), and 29% that responded to all three components. 4. The number of stimulus components to which a neuron responded was unrelated to the size or eccentricity of its receptive field. 5. Triple-, double-, and single-component neurons varied widely in the strength of their responses to the preferred components. Grouping these neurons together revealed that they did not form discrete classes but rather a continuum of response selectivity. 6. This continuum was apparent in other response characteristics. Direction selectivity was weakest in triple-component neurons, strongest in single-component neurons. Significant inhibitory responses were less frequent in triple-component neurons than in single-component neurons. 7. There was some indication that the neurons of similar component classes occupied adjacent regions within MSTd, but all combinations of component and direction selectivity were occasionally found in immediate juxtaposition. 8. Experiments on a subset of neurons showed that the speed of motion, the dot density, and the number of different speed planes in the display had little influence on these responses. 9. We conclude that the selective responses of many MSTd neurons to the rotational and translational components of optic flow make these neurons reasonable candidates for contributing to the analysis of optic flow fields.

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