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Surface representation in the monkey primary visual cortex (V1)
International Congress Series 1250 (2003) 53 – 61
Surface representation in the monkey primary visual cortex (V1) Hidehiko Komatsu *, Masaharu Kinoshita1 Laboratory of Neural Control, National Institute for Physiological Sciences, Myodaiji, Okazaki, Aichi 444-8585, Japan
Abstract An important task of object recognition is to detect object-specific features. However, a change in the illumination of the environment and noise inherent in the retinal image such as the optic disc will deteriorate the visual information transmitted from the retina to the visual cortex. Our visual system quite successfully performs object recognition in spite of these potential impairments of the retinal image. In the present research, we studied how the primary visual cortex (V1) is involved in solving these problems. We first examined if V1 neurons are involved in the integration of local and global luminance information, and then analyzed the responses of neurons at the retinotopic representation of the blind spot in V1 during perceptual filling-in. Our results suggest that there are mechanisms in V1 that integrate local and global information and that maintain the retinotopic correspondence between neuron activities and surface perception. We think preprocessing in V1 observed in our study will generate visual signals more robust to the change in the environment or to the noise inherent in the retinal image. This will reduce the load of higher visual areas in detecting visual features specific to objects and facilitate object recognition. D 2003 Published by Elsevier B.V. Keywords: Monkey; V1; Filling-in; Brightness induction; Surface perception
1. Introduction Object image consists of two kinds of complementary features, namely contour and surface. Contour defines the shape of object. Surface attributes include color, brightness, texture and transparency, etc. Both contour and surface attributes provide important information for object recognition. This information is processed along the ventral visual pathway which is involved in object recognition [1]. The ventral visual pathway connects
* Corresponding author. Tel.: +81-564-55-7861; fax: +81-564-55-7865. E-mail address: [email protected] (H. Komatsu). 1 Present address: The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. 0531-5131/ D 2003 Published by Elsevier B.V. doi:10.1016/S0531-5131(03)00972-5
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Fig. 1. Brightness induction. Each circle is painted with the same gray scale. So, they should have the same luminance under a uniform illumination. However, the perceived brightness of each circle is quite different because of the luminance gradient of the background.
the primary visual cortex (V1) to the inferior temporal cortex or area TE that plays an essential role in object recognition. Then, what kind of information about objects does V1 send out to the higher visual areas? Neurons in V1 have small receptive fields and it has been shown that both local contour information and surface information such as color and luminance are encoded in V1. Information about surface attributes is obtained at each position in the visual field and V1 has a precise retinotopic map. However, it has been shown that perception of surface attributes is significantly affected by stimuli surrounding the surface [2 –5]. For example, perceived brightness is significantly influenced by the luminance of the surrounding visual field [2,3]. In Fig. 1, circles are placed on the background that has luminance gradient. Although all circles have the same luminance, the perceived brightness of each circle is quite different because of the difference in the luminance of the surrounding region. Furthermore, even though retinal information is partially missing because of the blood vessels on the fundus of the eye and the optic disk, we perceive as if the visual scene continues without a gap [6,7]. These facts indicate that perceived surface attribute is not a simple copy of the retinal input. Instead, there is a remarkable dissociation between these two. Some mechanisms should exist in our visual system that integrate local and global surface information and that interpolate missing information. In the present study, we examined whether V1 is involved in such processes.
2. Luminance sensitivity of surface-responsive neurons in V1 In order to study the interaction between the local and global luminance information, we recorded from V1 neurons that responded to the uniform surface and examined their
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luminance sensitivity [8]. We first tested the neuronal responses to the stimuli covering the receptive field with various degrees of luminance while holding the luminance of the surround constant (Fig. 2A). In other words, we tested the sensitivity to local luminance information. Then, we examined how the luminance of the surrounding visual field (global luminance information) affects neural activity (Fig. 2B). This was tested by recording neuronal responses while varying the luminance of the region surrounding and remote from the receptive field while holding the luminance in the receptive field constant. We used two awake Japanese monkeys (Macaca fuscata) performing a visual fixation task. We penetrated microelectrodes into the parafoveal representation of V1 and recorded from single and multiple unit activities. We concentrated on sampling from neurons that responded to the uniform stimulus covering the receptive field (surface stimulus). We examined the sensitivity of surface-responsive neurons to the luminance of the surface stimulus. We first mapped the receptive field (RF) using high contrast small stimulus that evoked strong response. The extent of the surface stimulus was at least three times larger than the size of the RF. Visual stimuli were presented on the calibrated CRT display in front of the monkey. Fig. 3 shows the responses of an example of surface-responsive neurons to the surface stimuli with various luminances. Spike density plots of the responses to four different luminances are shown in Fig. 3A. The magnitude of the response clearly differed depending on the luminance of the surface stimulus. The luminance selectivity is particularly obvious in the later period of the response. Fig. 3B shows the average response magnitude during the later half of the stimulus presentation in relation to the
Fig. 2. Schema of the visual stimuli used to examine the luminance sensitivity of V1 neurons. In each panel, a cross represents the position of the fixation point and an ellipse represents the receptive field of a cell. (A) Stimuli for surface luminance test. A homogeneously painted square stimulus (surface stimulus) was presented on the receptive field and the luminance of the stimulus was changed. (B) Stimuli for surround luminance test. A concentric square stimulus was presented. The receptive field was covered by the central square. The luminance of the surrounding square was changed while the luminance of the central square was kept constant.
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Fig. 3. Responses of an example of surface-responsive neurons to different luminance of the surface stimulus. (A) Spike density plot of the responses. Each line represents different luminance of the surface stimulus. The surface stimulus is presented for 1.2 s as shown by the upward deflection of the line at the bottom. (B) Relationship between the luminance of the surface stimulus and the response magnitude. Error bar is S.D.
luminance of the surface stimulus. The response monotonically increased with the increase in the luminance of the stimulus.
3. Interaction between local and global luminance information in V1 A large majority of surface-responsive neurons was sensitive to the luminance of the surface. Fig. 4 shows another example of such neurons. This neuron preferred dark stimulus. For this neuron, we examined the sensitivity to the luminance of the surface stimulus using two different luminances of the background (30 and 1 cd/m2). Although the general response profile was the same, the magnitude of the response evoked by surface stimuli with the same luminance significantly changed depending on the luminance of the background. The luminance sensitivity curve shifted along the horizontal axis about 1 log unit as a result of the change in the background luminance of 1.5 log unit. This result clearly shows that not only the luminance of the surface stimulus but also the luminance of the surround had a strong effect on the response of this neuron.
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Fig. 4. Relationships between the luminance of the surface stimulus and the response of a single neuron that were recorded at two different luminances of the background.
The effect of the surround luminance differed from cell to cell. To formally examine the effect of the luminance of the surround, we presented a concentric square stimulus as shown in Fig. 2B, and recorded the response with various luminances of the outer square while the luminance of the center square was kept constant. In this experiment, the same size of the central square as the above experiment was employed. We examined the effect of the surround luminance in 81 neurons. Of these, 25 neurons (30.9%) showed no significant change in the response due to the change in the luminance of the surround (type I neuron). This type of neuron seems to encode only the local luminance of the surface covering the receptive field. The remaining two-thirds showed significant effects from the luminance of the surround. These neurons were classified into two groups according to the effect of the surround. In one group of neurons (type II neuron), the effect of the luminance of the surface stimulus and that of the surround was the opposite. For example, an increase in the luminance of the surface stimulus increased the response of a neuron, whereas the increase in the luminance of the surround decreased the response. Twenty-five neurons were classified as type II. Responses of this type of neuron qualitatively correlated with the change in the perceived brightness of the surface covering the RF. For example, the neuron in Fig. 5 (cell 1) showed stronger activity when the surface covering the RF had higher luminance. On the other hand, this neuron showed stronger activity when the surround was darker. In such a situation, the central square, namely the surface covering the RF, was perceived brighter. So, in either case, this neuron exhibited stronger activity when the surface covering the RF was perceived brighter. In contrast, the effect of the luminance of the surface stimulus and that of the surround was in the same direction for the remaining one-third of neurons (type III neuron) (n = 26, 32.1%). For example, an increase in the luminance of both surface stimulus and surround increased the response of a type III neuron shown in Fig. 5 (cell 2). So, it is as if this type of neuron integrates the light intensity of a wide region including both inside and outside
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Fig. 5. Effect of the luminance of the surface stimulus (top, see Fig. 2A) and the surrounding square (bottom, see Fig. 2B) in two neurons. Both cell 1 and cell 2 increased the responses due to an increase in the luminance of the surface stimulus. However, the luminance of the surround had the opposite effect.
of the RF. We interpret this type of neuron represents illumination level of a wide region around the RF. We found that the effect of the surround tend to gradually build up in about 200 ms. It has been shown that brightness induction also has low-pass temporal property in which modulation of the surround luminance over 3 –4 Hz will diminish the induced brightness change in the central surface [9– 11]. Slow buildup of the effect of the surround luminance observed in V1 may correspond to such low-pass temporal property of brightness induction.
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Layer distributions of neurons without surround effect and those with surround effect were different. We identified layer 4 by its characteristic high spontaneous discharge. Type I neurons that did not show significant effect from the surround were mainly found in layer 4. On the other hand, type II and III neurons were distributed in superficial and deep layers as well. It has been previously shown that lateral geniculate nucleus (LGN) neurons in monkeys do not show modulation by the luminance of the surround [12] and a similar result has been noted in cat LGN [11]. Therefore, we assume that the inputs from LGN to layer 4 of V1 carries local information about the luminance of the surface and activates type I neurons. Then, integration of surround luminance information will take place in V1. Fig. 6 shows one possible mechanism of such integration of local and global luminance information. At the bottom of the figure, the spatial relationship between the concentric square stimulus and the RF is indicated. The central square activates a type I neuron that represents its luminance. Other type I neurons are activated by the surrounding square. Outputs from these type I neurons are summed to a type III neuron shown at the middle. This neuron represents the average luminance level of a wide region covering both the central square and the surround. Finally, a type II neuron shown at the top receives differential input from the type I neuron that receives input from the central square and the type III neuron. The classical receptive field of this type II neuron is formed by the input from the type I neuron, and the input from the type III neuron causes contextual modulation. As a result, this type II neuron at the top signals the luminance contrast between the central region and the surrounding region.
Fig. 6. A schematic model of the integration of the surface luminance (local) information and the surround luminance (global) information in V1.
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4. Neural responses during perceptual filling-in at the blind spot Blind spot is located approximately on the horizontal meridian at about 15j from the fovea and the extent is about 5j horizontally and 7j vertically [7]. Blind spot corresponds to the optic disk in the retina. Optic disk forms the exit of the optic nerve from the retina as well as the entrance of blood vessels, and there is no photoreceptor. In spite of this, we see no gap in the visual image at the blind spot even when one eye is closed, because surface attributes such as color, brightness and texture are perceived to continue smoothly across the blind spot [6,7]. As the blind spot of each eye is in the opposite visual hemi-field, there is a region in V1 representing the visual field corresponding to the blind spot in each hemisphere. We will refer to this region as the ‘‘blind spot region’’ in V1. We examined whether neurons at the blind spot region in V1 are activated when perceptual filling-in occurs at the blind spot [13]. We used three awake Japanese monkeys performing a visual fixation task. We first conducted systematic receptive field mapping in binocular viewing and identified the blind spot region in V1. At the blind spot region, receptive fields were centered within the visual field corresponding to the blind spot. Then, one eye was occluded and a uniform rectangular stimulus covering the blind spot was presented. We found that some neurons, particularly those in layer 6, were clearly activated. These neurons tended to prefer large stimuli. On average, they exhibited spatial summation up to about 3 degree in size and did not clearly discriminate stimulus size over 2j. On the other hand, these neurons showed only weak response to small stimuli. Therefore, the activities of these neurons encode the presence of a large surface on the blind spot.
5. Surface representation in V1 These experiments indicate several important aspects of surface representation in V1. First, V1 can send information about surface interior as well as object contour to higher visual areas. There are some mechanisms in V1 for spatial integration of luminance information and surface interpolation at the blind spot. As a result, retinotopic correspondence between perceived surface attributes and neuron activities is maintained in V1. What is the functional significance of these responses observed in V1? An important task of object recognition is to detect object-specific features that are invariable to the change in the environment or to the noise inherent in the retinal image. Obviously, the blood vessels on the retina and optic disk disrupt the retinal image and generate noise. Filling-in of surface and completion of contour at the level of V1 can generate signals free from the noise inherent in the retinal image. With regard to the integration of surround luminance, we need to consider the optics related to the brightness perception. The light intensity coming into the eye from a surface, namely surface luminance, is determined as the product of surface reflectance and the illumination. Therefore, if the illumination in the environment changes, surface luminance changes. For the purpose of object recognition, estimation of surface reflectance is more important because this is a feature inherent to the surface itself and provides useful information about the surface property of the object. If the
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luminances of the surface and surround are compared, this results in the ratio of the surface reflectance and this parameter is invariant to the change in the environmental illumination. We think preprocessing in V1 observed in our study will generate visual signals more robust to the change in the environment or to the noise inherent in the retinal image. This will reduce the load of higher visual areas in detecting visual features specific to objects and facilitate object recognition.
Acknowledgements This work is supported by the ‘Research for the Future’ Program (RFTF) from the Japan Society for the Promotion of Science (JSPS RFTF96L00202).
References [1] L.G. Ungerleider, M. Mishkin, Two cortical visual systems, in: D.J. Ingle, R.J.W. Manfield, M.A. Goodale (Eds.), Analysis of Visual Behavior, MIT Press, Cambridge, MA, 1982, pp. 549 – 586. [2] E. Hering, Outlines of a Theory of the Light Sense, Harvard Univ. Press, Cambridge, MA, 1964. [3] S. Torii, Y. Uemura, Effects of inducing luminance and area on test-threshold luminance, Percept. Mot. Skills 21 (1965) 779 – 782. [4] E.H. Adelson, Perceptual organization and the judgment of brightness, Science 262 (1993) 2042 – 2044. [5] A.L. Gilchrist, Perceived lightness depends on perceived spatial arrangement, Science 195 (1977) 185 – 187. [6] V.S. Ramachandran, R.L. Gregory, Perceptual filling in of artificially induced scotomas in human vision, Nature 350 (1991) 699 – 702. [7] H. Komatsu, I. Murakami, Behavioral evidence of filling-in at the blind spot of the monkey, Vis. Neurosci. 11 (1994) 1103 – 1113. [8] M. Kinoshita, H. Komatsu, Neural representation of the luminance and brightness of a uniform surface in the macaque primary visual cortex, J. Neurophysiol. 86 (2001) 2559 – 2570. [9] R.L. De Valois, M.A. Webster, K.K. De Valois, B. Lingelbach, Temporal properties of brightness and color induction, Vis. Res. 26 (1986) 887 – 897. [10] A.F. Rossi, M.A. Paradiso, Temporal limits of brightness induction and mechanisms of brightness perception, Vis. Res. 36 (1996) 1391 – 1398. [11] A.F. Rossi, M.A. Paradiso, Neural correlates of perceived brightness in the retina, lateral geniculate nucleus, and striate cortex, J. Neurosci. 19 (1999) 6145 – 6156. [12] R.L. De Valois, P.L. Pease, Contours and contrast: responses of monkey lateral geniculate nucleus cells to luminance and color figures, Science 171 (1971) 694 – 696. [13] H. Komatsu, M. Kinoshita, I. Murakami, Neural responses in the retinotopic representation of the blind spot in the macaque V1 to stimuli for perceptual filling-in, J. Neurosci. 20 (2000) 9310 – 9319.
Surface representation in the monkey primary visual cortex (V1) Hidehiko Komatsu *, Masaharu Kinoshita1 Laboratory of Neural Control, National Institute for Physiological Sciences, Myodaiji, Okazaki, Aichi 444-8585, Japan
Abstract An important task of object recognition is to detect object-specific features. However, a change in the illumination of the environment and noise inherent in the retinal image such as the optic disc will deteriorate the visual information transmitted from the retina to the visual cortex. Our visual system quite successfully performs object recognition in spite of these potential impairments of the retinal image. In the present research, we studied how the primary visual cortex (V1) is involved in solving these problems. We first examined if V1 neurons are involved in the integration of local and global luminance information, and then analyzed the responses of neurons at the retinotopic representation of the blind spot in V1 during perceptual filling-in. Our results suggest that there are mechanisms in V1 that integrate local and global information and that maintain the retinotopic correspondence between neuron activities and surface perception. We think preprocessing in V1 observed in our study will generate visual signals more robust to the change in the environment or to the noise inherent in the retinal image. This will reduce the load of higher visual areas in detecting visual features specific to objects and facilitate object recognition. D 2003 Published by Elsevier B.V. Keywords: Monkey; V1; Filling-in; Brightness induction; Surface perception
1. Introduction Object image consists of two kinds of complementary features, namely contour and surface. Contour defines the shape of object. Surface attributes include color, brightness, texture and transparency, etc. Both contour and surface attributes provide important information for object recognition. This information is processed along the ventral visual pathway which is involved in object recognition [1]. The ventral visual pathway connects
* Corresponding author. Tel.: +81-564-55-7861; fax: +81-564-55-7865. E-mail address: [email protected] (H. Komatsu). 1 Present address: The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. 0531-5131/ D 2003 Published by Elsevier B.V. doi:10.1016/S0531-5131(03)00972-5
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H. Komatsu, M. Kinoshita / International Congress Series 1250 (2003) 53–61
Fig. 1. Brightness induction. Each circle is painted with the same gray scale. So, they should have the same luminance under a uniform illumination. However, the perceived brightness of each circle is quite different because of the luminance gradient of the background.
the primary visual cortex (V1) to the inferior temporal cortex or area TE that plays an essential role in object recognition. Then, what kind of information about objects does V1 send out to the higher visual areas? Neurons in V1 have small receptive fields and it has been shown that both local contour information and surface information such as color and luminance are encoded in V1. Information about surface attributes is obtained at each position in the visual field and V1 has a precise retinotopic map. However, it has been shown that perception of surface attributes is significantly affected by stimuli surrounding the surface [2 –5]. For example, perceived brightness is significantly influenced by the luminance of the surrounding visual field [2,3]. In Fig. 1, circles are placed on the background that has luminance gradient. Although all circles have the same luminance, the perceived brightness of each circle is quite different because of the difference in the luminance of the surrounding region. Furthermore, even though retinal information is partially missing because of the blood vessels on the fundus of the eye and the optic disk, we perceive as if the visual scene continues without a gap [6,7]. These facts indicate that perceived surface attribute is not a simple copy of the retinal input. Instead, there is a remarkable dissociation between these two. Some mechanisms should exist in our visual system that integrate local and global surface information and that interpolate missing information. In the present study, we examined whether V1 is involved in such processes.
2. Luminance sensitivity of surface-responsive neurons in V1 In order to study the interaction between the local and global luminance information, we recorded from V1 neurons that responded to the uniform surface and examined their
H. Komatsu, M. Kinoshita / International Congress Series 1250 (2003) 53–61
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luminance sensitivity [8]. We first tested the neuronal responses to the stimuli covering the receptive field with various degrees of luminance while holding the luminance of the surround constant (Fig. 2A). In other words, we tested the sensitivity to local luminance information. Then, we examined how the luminance of the surrounding visual field (global luminance information) affects neural activity (Fig. 2B). This was tested by recording neuronal responses while varying the luminance of the region surrounding and remote from the receptive field while holding the luminance in the receptive field constant. We used two awake Japanese monkeys (Macaca fuscata) performing a visual fixation task. We penetrated microelectrodes into the parafoveal representation of V1 and recorded from single and multiple unit activities. We concentrated on sampling from neurons that responded to the uniform stimulus covering the receptive field (surface stimulus). We examined the sensitivity of surface-responsive neurons to the luminance of the surface stimulus. We first mapped the receptive field (RF) using high contrast small stimulus that evoked strong response. The extent of the surface stimulus was at least three times larger than the size of the RF. Visual stimuli were presented on the calibrated CRT display in front of the monkey. Fig. 3 shows the responses of an example of surface-responsive neurons to the surface stimuli with various luminances. Spike density plots of the responses to four different luminances are shown in Fig. 3A. The magnitude of the response clearly differed depending on the luminance of the surface stimulus. The luminance selectivity is particularly obvious in the later period of the response. Fig. 3B shows the average response magnitude during the later half of the stimulus presentation in relation to the
Fig. 2. Schema of the visual stimuli used to examine the luminance sensitivity of V1 neurons. In each panel, a cross represents the position of the fixation point and an ellipse represents the receptive field of a cell. (A) Stimuli for surface luminance test. A homogeneously painted square stimulus (surface stimulus) was presented on the receptive field and the luminance of the stimulus was changed. (B) Stimuli for surround luminance test. A concentric square stimulus was presented. The receptive field was covered by the central square. The luminance of the surrounding square was changed while the luminance of the central square was kept constant.
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Fig. 3. Responses of an example of surface-responsive neurons to different luminance of the surface stimulus. (A) Spike density plot of the responses. Each line represents different luminance of the surface stimulus. The surface stimulus is presented for 1.2 s as shown by the upward deflection of the line at the bottom. (B) Relationship between the luminance of the surface stimulus and the response magnitude. Error bar is S.D.
luminance of the surface stimulus. The response monotonically increased with the increase in the luminance of the stimulus.
3. Interaction between local and global luminance information in V1 A large majority of surface-responsive neurons was sensitive to the luminance of the surface. Fig. 4 shows another example of such neurons. This neuron preferred dark stimulus. For this neuron, we examined the sensitivity to the luminance of the surface stimulus using two different luminances of the background (30 and 1 cd/m2). Although the general response profile was the same, the magnitude of the response evoked by surface stimuli with the same luminance significantly changed depending on the luminance of the background. The luminance sensitivity curve shifted along the horizontal axis about 1 log unit as a result of the change in the background luminance of 1.5 log unit. This result clearly shows that not only the luminance of the surface stimulus but also the luminance of the surround had a strong effect on the response of this neuron.
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Fig. 4. Relationships between the luminance of the surface stimulus and the response of a single neuron that were recorded at two different luminances of the background.
The effect of the surround luminance differed from cell to cell. To formally examine the effect of the luminance of the surround, we presented a concentric square stimulus as shown in Fig. 2B, and recorded the response with various luminances of the outer square while the luminance of the center square was kept constant. In this experiment, the same size of the central square as the above experiment was employed. We examined the effect of the surround luminance in 81 neurons. Of these, 25 neurons (30.9%) showed no significant change in the response due to the change in the luminance of the surround (type I neuron). This type of neuron seems to encode only the local luminance of the surface covering the receptive field. The remaining two-thirds showed significant effects from the luminance of the surround. These neurons were classified into two groups according to the effect of the surround. In one group of neurons (type II neuron), the effect of the luminance of the surface stimulus and that of the surround was the opposite. For example, an increase in the luminance of the surface stimulus increased the response of a neuron, whereas the increase in the luminance of the surround decreased the response. Twenty-five neurons were classified as type II. Responses of this type of neuron qualitatively correlated with the change in the perceived brightness of the surface covering the RF. For example, the neuron in Fig. 5 (cell 1) showed stronger activity when the surface covering the RF had higher luminance. On the other hand, this neuron showed stronger activity when the surround was darker. In such a situation, the central square, namely the surface covering the RF, was perceived brighter. So, in either case, this neuron exhibited stronger activity when the surface covering the RF was perceived brighter. In contrast, the effect of the luminance of the surface stimulus and that of the surround was in the same direction for the remaining one-third of neurons (type III neuron) (n = 26, 32.1%). For example, an increase in the luminance of both surface stimulus and surround increased the response of a type III neuron shown in Fig. 5 (cell 2). So, it is as if this type of neuron integrates the light intensity of a wide region including both inside and outside
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Fig. 5. Effect of the luminance of the surface stimulus (top, see Fig. 2A) and the surrounding square (bottom, see Fig. 2B) in two neurons. Both cell 1 and cell 2 increased the responses due to an increase in the luminance of the surface stimulus. However, the luminance of the surround had the opposite effect.
of the RF. We interpret this type of neuron represents illumination level of a wide region around the RF. We found that the effect of the surround tend to gradually build up in about 200 ms. It has been shown that brightness induction also has low-pass temporal property in which modulation of the surround luminance over 3 –4 Hz will diminish the induced brightness change in the central surface [9– 11]. Slow buildup of the effect of the surround luminance observed in V1 may correspond to such low-pass temporal property of brightness induction.
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Layer distributions of neurons without surround effect and those with surround effect were different. We identified layer 4 by its characteristic high spontaneous discharge. Type I neurons that did not show significant effect from the surround were mainly found in layer 4. On the other hand, type II and III neurons were distributed in superficial and deep layers as well. It has been previously shown that lateral geniculate nucleus (LGN) neurons in monkeys do not show modulation by the luminance of the surround [12] and a similar result has been noted in cat LGN [11]. Therefore, we assume that the inputs from LGN to layer 4 of V1 carries local information about the luminance of the surface and activates type I neurons. Then, integration of surround luminance information will take place in V1. Fig. 6 shows one possible mechanism of such integration of local and global luminance information. At the bottom of the figure, the spatial relationship between the concentric square stimulus and the RF is indicated. The central square activates a type I neuron that represents its luminance. Other type I neurons are activated by the surrounding square. Outputs from these type I neurons are summed to a type III neuron shown at the middle. This neuron represents the average luminance level of a wide region covering both the central square and the surround. Finally, a type II neuron shown at the top receives differential input from the type I neuron that receives input from the central square and the type III neuron. The classical receptive field of this type II neuron is formed by the input from the type I neuron, and the input from the type III neuron causes contextual modulation. As a result, this type II neuron at the top signals the luminance contrast between the central region and the surrounding region.
Fig. 6. A schematic model of the integration of the surface luminance (local) information and the surround luminance (global) information in V1.
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4. Neural responses during perceptual filling-in at the blind spot Blind spot is located approximately on the horizontal meridian at about 15j from the fovea and the extent is about 5j horizontally and 7j vertically [7]. Blind spot corresponds to the optic disk in the retina. Optic disk forms the exit of the optic nerve from the retina as well as the entrance of blood vessels, and there is no photoreceptor. In spite of this, we see no gap in the visual image at the blind spot even when one eye is closed, because surface attributes such as color, brightness and texture are perceived to continue smoothly across the blind spot [6,7]. As the blind spot of each eye is in the opposite visual hemi-field, there is a region in V1 representing the visual field corresponding to the blind spot in each hemisphere. We will refer to this region as the ‘‘blind spot region’’ in V1. We examined whether neurons at the blind spot region in V1 are activated when perceptual filling-in occurs at the blind spot [13]. We used three awake Japanese monkeys performing a visual fixation task. We first conducted systematic receptive field mapping in binocular viewing and identified the blind spot region in V1. At the blind spot region, receptive fields were centered within the visual field corresponding to the blind spot. Then, one eye was occluded and a uniform rectangular stimulus covering the blind spot was presented. We found that some neurons, particularly those in layer 6, were clearly activated. These neurons tended to prefer large stimuli. On average, they exhibited spatial summation up to about 3 degree in size and did not clearly discriminate stimulus size over 2j. On the other hand, these neurons showed only weak response to small stimuli. Therefore, the activities of these neurons encode the presence of a large surface on the blind spot.
5. Surface representation in V1 These experiments indicate several important aspects of surface representation in V1. First, V1 can send information about surface interior as well as object contour to higher visual areas. There are some mechanisms in V1 for spatial integration of luminance information and surface interpolation at the blind spot. As a result, retinotopic correspondence between perceived surface attributes and neuron activities is maintained in V1. What is the functional significance of these responses observed in V1? An important task of object recognition is to detect object-specific features that are invariable to the change in the environment or to the noise inherent in the retinal image. Obviously, the blood vessels on the retina and optic disk disrupt the retinal image and generate noise. Filling-in of surface and completion of contour at the level of V1 can generate signals free from the noise inherent in the retinal image. With regard to the integration of surround luminance, we need to consider the optics related to the brightness perception. The light intensity coming into the eye from a surface, namely surface luminance, is determined as the product of surface reflectance and the illumination. Therefore, if the illumination in the environment changes, surface luminance changes. For the purpose of object recognition, estimation of surface reflectance is more important because this is a feature inherent to the surface itself and provides useful information about the surface property of the object. If the
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luminances of the surface and surround are compared, this results in the ratio of the surface reflectance and this parameter is invariant to the change in the environmental illumination. We think preprocessing in V1 observed in our study will generate visual signals more robust to the change in the environment or to the noise inherent in the retinal image. This will reduce the load of higher visual areas in detecting visual features specific to objects and facilitate object recognition.
Acknowledgements This work is supported by the ‘Research for the Future’ Program (RFTF) from the Japan Society for the Promotion of Science (JSPS RFTF96L00202).
References [1] L.G. Ungerleider, M. Mishkin, Two cortical visual systems, in: D.J. Ingle, R.J.W. Manfield, M.A. Goodale (Eds.), Analysis of Visual Behavior, MIT Press, Cambridge, MA, 1982, pp. 549 – 586. [2] E. Hering, Outlines of a Theory of the Light Sense, Harvard Univ. Press, Cambridge, MA, 1964. [3] S. Torii, Y. Uemura, Effects of inducing luminance and area on test-threshold luminance, Percept. Mot. Skills 21 (1965) 779 – 782. [4] E.H. Adelson, Perceptual organization and the judgment of brightness, Science 262 (1993) 2042 – 2044. [5] A.L. Gilchrist, Perceived lightness depends on perceived spatial arrangement, Science 195 (1977) 185 – 187. [6] V.S. Ramachandran, R.L. Gregory, Perceptual filling in of artificially induced scotomas in human vision, Nature 350 (1991) 699 – 702. [7] H. Komatsu, I. Murakami, Behavioral evidence of filling-in at the blind spot of the monkey, Vis. Neurosci. 11 (1994) 1103 – 1113. [8] M. Kinoshita, H. Komatsu, Neural representation of the luminance and brightness of a uniform surface in the macaque primary visual cortex, J. Neurophysiol. 86 (2001) 2559 – 2570. [9] R.L. De Valois, M.A. Webster, K.K. De Valois, B. Lingelbach, Temporal properties of brightness and color induction, Vis. Res. 26 (1986) 887 – 897. [10] A.F. Rossi, M.A. Paradiso, Temporal limits of brightness induction and mechanisms of brightness perception, Vis. Res. 36 (1996) 1391 – 1398. [11] A.F. Rossi, M.A. Paradiso, Neural correlates of perceived brightness in the retina, lateral geniculate nucleus, and striate cortex, J. Neurosci. 19 (1999) 6145 – 6156. [12] R.L. De Valois, P.L. Pease, Contours and contrast: responses of monkey lateral geniculate nucleus cells to luminance and color figures, Science 171 (1971) 694 – 696. [13] H. Komatsu, M. Kinoshita, I. Murakami, Neural responses in the retinotopic representation of the blind spot in the macaque V1 to stimuli for perceptual filling-in, J. Neurosci. 20 (2000) 9310 – 9319.