The cortical demands of two kinds of perceptual task
0
Citation
0
Reference
10
Related Paper
Abstract:
Many authors distinguish "first-order" object recognition from "second-order" tasks that are poorly suited to template matching and seem to demand other kinds of perceptual computation. "Second-order" tasks include detecting symmetry, Glass patterns, modulations of white noise, and coarse patterns composed of small balanced elements. Past treatments have suggested various special computations, particular to each task, that observers might make. We take a more general approach. We suppose a complete set of receptive fields (like those of V1 cells) and ask how many receptive fields are required to perform as well as human observers. This is like defining efficiency as the fraction of available information (e.g. dots or area) that would be required by an ideal observer, but applied to receptive fields rather than to components (e.g. dots) of the stimulus. With mild assumptions about the receptive fields, this reveals a dichotomy between "first-order" ordinary identification tasks that require on the order of ten receptive fields and "second-order" tasks that require thousands or millions. The necessary cortical wiring is greatly affected by the hundred-or-more-fold increase in the number of receptive fields used. Meeting abstract presented at VSS 2012Keywords:
Surround suppression
Stimulus (psychology)
Information is integrated across the visual field to transform local features into a global percept. We now know that V1 neurons provide more spatial integration than originally thought due to the existence of their nonclassical inhibitory surrounds. To understand spatial integration in the visual cortex, we have studied the nature and extent of center and surround influences on neuronal response. We used drifting sinusoidal gratings in circular and annular apertures to estimate the sizes of the receptive field's excitatory center and suppressive surround. We used combinations of stimuli inside and outside the receptive field to explore the nature of the surround influence on the receptive field center as a function of the relative and absolute contrast of stimuli in the two regions. We conclude that the interaction is best explained as a divisive modulation of response gain by signals from the surround. We then develop a receptive field model based on the ratio of signals from Gaussian-shaped center and surround mechanisms. We show that this model can account well for the variations in receptive field size with contrast that we and others have observed and for variations in size with the state of contrast adaptation. The model achieves this success by simple variations in the relative gain of the two component mechanisms of the receptive field. This model thus offers a parsimonious explanation of a variety of phenomena involving changes in apparent receptive field size and accounts for these phenomena purely in terms of two receptive field mechanisms that do not themselves change in size. We used the extent of the center mechanism in our model as an indicator of the spatial extent of the central excitatory portion of the receptive field. We compared the extent of the center to measurements of horizontal connections within V1 and determined that horizontal intracortical connections are well matched in extent to the receptive field center mechanism. Input to the suppressive surround may come in part from feedback signals from higher areas. PMID: 12424292 Funding information This work was supported by: NEI NIH HHS, United States Grant ID: EY02017
Surround suppression
Percept
Cite
Citations (0)
Surround suppression
Stimulus (psychology)
Spatial frequency
Cite
Citations (0)
Initial models proposed that these operations are weighted sums, with weights given by a neuron’s receptive field. These models explain the basic features of response selectivity. They were later extended to explain a number of suppressive effects originating within and outside the region of the receptive field. The resulting models rely on division. In this division, the receptive field feeds into the numerator, and the denominator is provided by a larger, non-classical suppressive field. While the receptive field confers to a neuron the basic selectivity for stimulus properties, the suppressive field modulates responsiveness. A divisive suppressive field confers to neurons in early visual system a number of computational advantages. Recent evidence in higher cortical areas suggests that the modulation of divisive suppression is the primary means of operation of visual attention. In this chapter I summarize research in receptive fields and suppressive fields in lateral geniculate nucleus (LGN) and in primary visual cortex (V1). In the following, I refer to a “suppressive field” as though this term had wide acceptance. In reality, the concept has been proposed only for LGN neurons (Levick et al., 1972), and lies forgotten since 30 years. My hope is that it will find wide acceptance to describe responses of both LGN and V1 neurons. Receptive fields in LGN
Surround suppression
Lateral geniculate nucleus
Stimulus (psychology)
Binocular neurons
Cite
Citations (48)
Surround suppression is a phenomenon whereby stimulation of the extraclassical receptive field suppressively modulates the visual responses of neurons in the primary visual cortex (V1) (also known as area 17). It is known that surround suppression tunes to spatial frequencies (SFs) that are much lower and broader than the frequencies to which the classical receptive field tunes. In this study, we tested the effects of varying SFs on surround suppression by using a circular sinusoidal grating patch that covered both the classical receptive field and the extraclassical receptive field. Using area-summation tuning curves, we found high-SF-tuned surround suppression in the cat V1. This high-SF-tuned surround suppression causes the SF tuning to shift to low SF for large stimuli. By simulating a model neuron lacking a suppressive surround mechanism, we confirmed that these preferred SF shifts do not occur in the absence of surround suppression. We surmise that the high-SF-tuned suppression, which shifts the preferred SF according to size, functionally contributes to the scale-invariant processing of visual images in V1.
Surround suppression
Spatial frequency
Orientation column
Cite
Citations (22)
Shifting attention among visual stimuli at different locations modulates neuronal responses in heterogeneous ways, depending on where those stimuli lie within the receptive fields of neurons. Yet how attention interacts with the receptive-field structure of cortical neurons remains unclear. We measured neuronal responses in area V4 while monkeys shifted their attention among stimuli placed in different locations within and around neuronal receptive fields. We found that attention interacts uniformly with the spatially-varying excitation and suppression associated with the receptive field. This interaction explained the large variability in attention modulation across neurons, and a non-additive relationship among stimulus selectivity, stimulus-induced suppression and attention modulation that has not been previously described. A spatially-tuned normalization model precisely accounted for all observed attention modulations and for the spatial summation properties of neurons. These results provide a unified account of spatial summation and attention-related modulation across both the classical receptive field and the surround.
Surround suppression
Stimulus (psychology)
Summation
Cite
Citations (22)
Stimulus (psychology)
Surround suppression
Cite
Citations (29)
Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Appendix References Decision letter Author response Article and author information Metrics Abstract Shifting attention among visual stimuli at different locations modulates neuronal responses in heterogeneous ways, depending on where those stimuli lie within the receptive fields of neurons. Yet how attention interacts with the receptive-field structure of cortical neurons remains unclear. We measured neuronal responses in area V4 while monkeys shifted their attention among stimuli placed in different locations within and around neuronal receptive fields. We found that attention interacts uniformly with the spatially-varying excitation and suppression associated with the receptive field. This interaction explained the large variability in attention modulation across neurons, and a non-additive relationship among stimulus selectivity, stimulus-induced suppression and attention modulation that has not been previously described. A spatially-tuned normalization model precisely accounted for all observed attention modulations and for the spatial summation properties of neurons. These results provide a unified account of spatial summation and attention-related modulation across both the classical receptive field and the surround. https://doi.org/10.7554/eLife.17256.001 eLife digest At any moment, our brain receives an enormous amount of information from our senses. However, we are not aware of all of this information; only the information we decide to focus on is perceived in detail. This ability to focus our attention is important for survival. The neurons involved in vision respond best to information that comes from a small 'window' in what is being seen. When something appears in this window (known as the neuron's receptive field), the activity of the neuron either increases or decreases. How does focusing attention on an object change the neuron's response? Verhoef and Maunsell investigated this question by recording electrical activity in an area of the brain called V4 in monkeys as they focused their attention on objects in different locations of the neuron's receptive field. The recordings show that a single rule determines when attention influences a neuron's activity. If an object inside the neuron's receptive field decreases the activity of the neuron, then attention can change that neuron's activity. Attention then changes the activity of the neuron by either removing or further boosting the influence of these objects. Verhoef and Maunsell then developed a mathematical model based on these results, and found that the model could explain why the activity of a neuron changes when attention is focused on objects at different locations in its receptive field. The next step is to understand exactly how the brain works to either remove or boost the influence of an object that causes a neuron's activity to decrease. https://doi.org/10.7554/eLife.17256.002 Introduction Our eyes are constantly bombarded by a welter of visual stimuli, only a small fraction of which can be processed thoroughly (Chun et al., 2011; Kastner and Ungerleider, 2000). Spatial attention sifts through the plethora of stimuli – enhancing perception at behaviorally-relevant locations – but the underlying neural principles of this process are not fully understood (Chun et al., 2011; Kastner and Ungerleider, 2000; Posner, 1980; Carrasco, 2011; Roelfsema et al., 1998; Anton-Erxleben and Carrasco, 2013). Neuronal responses modulate as attention shifts among stimuli at different receptive-field locations (Moran and Desimone, 1985; Treue and Maunsell, 1996; Reynolds et al., 1999; Martínez-Trujillo and Treue, 2002; Ghose and Maunsell, 2008; Lee and Maunsell, 2010; Ni et al., 2012; Recanzone and Wurtz, 2000; Luck et al., 1997; Motter, 1993; Chelazzi et al., 1998; Zénon and Krauzlis, 2012). These response modulations can be complicated. For example, depending on the stimulus configuration, attending to a non-preferred stimulus can either increase or suppress activity (e.g. Treue and Maunsell, 1996). Normalization models of attention provide a succinct framework in which these complex response modulations can be understood (Reynolds et al., 1999; Ghose, 2009; Reynolds and Heeger, 2009; Lee, 2009; Boynton, 2009). However, only a few studies have directly tested these models against the responses of individual neurons to various stimulus configurations (Ni et al., 2012; Lee, 2009; Sanayei et al., 2015; Xiao et al., 2014). Normalization models of attention assume that attention acts on stimulus-induced excitation and suppression to modulate neuronal responses. Importantly, both excitation and suppression vary spatially within the receptive field: excitation is largely restricted to the classical receptive field (cRF), while suppression extends far beyond into the surround (Cavanaugh et al., 2002a, 2002b; Sceniak et al., 1999; Desimone and Schein, 1987; Carandini et al., 1997). Crucially, how attention interacts with the receptive field structure of neurons remains unclear. For example, the way that attention acts on neuronal responses when shifted among stimuli inside the cRF versus when shifted to stimuli inside the surround has not been compared directly (Motter, 1993; Sanayei et al., 2015; Sundberg et al., 2009). Differences may occur because feedforward-, feedback- and intracortical circuitries are thought to contribute differentially to the suppressive and excitatory inputs associated with stimuli in either the cRF or the surround (Angelucci et al., 2014), and because the cRF and the surround presumably serve different functional roles (Angelucci et al., 2014; Schwartz and Simoncelli, 2001; Vinje and Gallant, 2000). More generally, it remains unknown if and how attention operates on the spatially-varying excitation and suppression of a neuron's receptive field. This is a pivotal open question because, as we will show below, the interaction between attention and the receptive field structure determines which neurons are most affected by attention and consequently are most likely to influence attentional behavior. We measured how attention affects neuronal responses to various stimulus configurations both inside and outside the cRF of V4 neurons, and fitted normalization models to the responses of individual neurons. We find that the principles that drive attention modulation are remarkably similar within the classical receptive field and the surround. We show that stimuli induce excitation and suppression that varies spatially, and that attention interacts with this spatially-varying excitation and suppression. This interaction explained the large differences in attention modulations across neurons, and a non-additive relationship among stimulus selectivity, stimulus-induced suppression and attention modulation. A spatially-tuned normalization model, wherein attention multiplies both the excitatory and spatially-varying normalization term, precisely accounted for all neuronal responses to either single or multiple stimuli, either attended or unattended, presented inside either the cRF or the surround. The model relates stimulus selectivity, stimulus-induced suppression and attention-related modulation to each other, and unifies spatial summation and attention-related modulation across different regions of the receptive field. Results Task and behavioral performance We trained two rhesus monkeys to perform a visual-detection task in which spatial attention was controlled and measured. In each trial, a sequence of stimuli was presented at four locations equidistant from the fixation point (Figure 1A). Stimuli were full-contrast static Gabor stimuli with one of two orthogonal orientations. The monkey's task was to detect a faint white spot (target; Figure 1A right) that appeared in the center of one Gabor during a randomly selected stimulus presentation. We manipulated attention in blocks of trials by cueing the monkey at the start of each block as to which stimulus location was most likely to contain the target (Materials and methods). In 91% of trials the target was presented at the cued location (valid cue; position of the black circle Figure 1A). On the remaining 9% of trials the target appeared with equal probability at one of the three uncued locations: either next to the cued location (invalid near; position of the yellow circle Figure 1A), or at one of two locations contralateral to the cued location (invalid far; position of the blue circles Figure 1A). Figure 1 Download asset Open asset Task and performance. (A) Every trial consisted of a sequence of stimulus presentations. On each stimulus presentation (200 ms duration; 200–1020 inter-stimulus interval), Gabor stimuli of two orthogonal orientations could be presented at four possible stimulus locations. The monkey was rewarded for detecting a faint white spot (target) in the center of one Gabor during one stimulus presentation. For 91% of trials the target was presented at the cued location (location of the black circle; valid trials). On the remaining 9% of trials the target was presented at one of three uncued locations: adjacent to the cued location (location of the yellow circle; invalid near), or at one of two locations on the opposite side of the fixation point (location of the blue circles; invalid far). Colored circles in (A) are shown for illustrative purposes, never presented during the task. (B) Average performance across recording sessions for monkey M1. Proportion correct (± SEM based on N = 52 sessions; proportion correct at equal target strength: Valid: 0.79; Invalid near: 0.42; Invalid far: 0.30) as a function of target strength for trials in which the target occurred at the cued (gray: valid) or uncued (yellow: invalid near; blue: invalid far) location. Target strength is defined as the opacity of the target. The pictograms below the target-strength axis illustrate the nature of the target-strength manipulation but do not represent actual target-strength values used during the recordings. (C) Average performance across recording sessions for monkey M2 (N = 78 sessions; proportion correct at equal target strength: Valid: 0.56; Invalid near: 0.24; Invalid far: 0.05). https://doi.org/10.7554/eLife.17256.003 The attention cue considerably affected behavioral performance in the task: targets were much more likely detected at a cued location than at an uncued location, even when the uncued location was adjacent to the cued location (Figure 1B,C; valid vs. invalid near: monkey M1: p=8 × 10−27, M2: p=1 × 10−26; valid vs. invalid far: M1: p=1 × 10−36, M2: p=2 × 10−58; paired t-test on the average proportion correct across sessions; M1: N = 52; M2: N = 78). The improved performance indicates that the monkeys preferentially attended to the cued stimulus location, which allowed us to compare neuronal responses among conditions in which attention was directed to different stimulus locations within neurons' cRF or surround. Experimental conditions and example neurons We examined the principles by which attention affects neuronal responses to stimuli inside the classical receptive field (cRF) or within the surround (sRF). Using chronically implanted microelectrode arrays, we recorded from 728 neurons in visual area V4 in the left hemisphere of two monkeys (monkey M1: 264; M2: 464) while they performed the visual-detection task in which spatial attention was controlled. All results presented here are based on the activity of these 728 single neurons, but all findings were confirmed in the responses of 12,067 multi-unit clusters (M1: 4709; M2: 7358). During each session we simultaneously measured the activity of multiple neurons, and optimized the orientation and position of stimuli for a randomly selected unit. The neurons' receptive field centers were located in the lower right visual field (black dots in Figure 2A for an example session). Figure 2 with 1 supplement see all Download asset Open asset Stimulus conditions. (A) Neurons' receptive field centers were located in the lower right visual field: black dots indicate receptive-field centers of 16 simultaneously recorded neurons from one recording session. White circles (1,2,3) indicate the three stimulus locations near the neurons' receptive field for this example session. Within a block of trials, only two stimulus locations were used: locations 1+2 or 1+3. (B) Nine possible stimulus combinations resulting from two stimulus locations and two orthogonal orientations. (C) Two receptive-field configurations: cRF-cRF stimulus configuration with two stimuli inside the neuron's classical receptive field. White dotted circle illustrates the cRF. (D) sRF-cRF stimulus configuration with one stimulus inside a neuron's cRF and an adjacent stimulus in its surround. Each stimulus location near the neurons' receptive fields (stimulus location 1,2,3 in 2A) had a corresponding stimulus location on the opposite side of the fixation point (stimuli near Away in C, D; see also Figure 2—figure supplement 1). (E) Pictograms illustrate for one Gabor pair the stimulus configurations used to calculate all indices. Cyan circles indicate the preferred Gabor (P), orange circles the non-preferred Gabor (N). Solid circles represent task conditions wherein attention was directed toward a stimulus location near the neurons' receptive field (PAttN, PNAtt). Dashed circles indicate that the stimulus was unattended and attention was directed toward another location. https://doi.org/10.7554/eLife.17256.004 In different blocks of trials, we measured neuronal responses to stimuli presented at three different receptive-field locations (stimulus locations 1, 2, 3 in Figure 2A). Within a block of trials, only two of these stimulus locations were used: e.g. location 1+2 or 1+3 in Figure 2A (Materials and methods). During each stimulus presentation within a trial, we presented one, two, or no stimuli at the two stimulus locations near the receptive field (Figure 2B). Depending on the location of each neuron's receptive field, stimuli fell either inside the cRF or within the surround. We distinguished between two receptive-field configurations: one in which the two stimulus locations both lay inside the neuron's cRF (cRF-cRF, Figure 2C), and another in which one stimulus location was positioned inside the neuron's cRF while the other stimulus location was positioned inside its surround (sRF-cRF, Figure 2D). Because we tested the responses to stimuli shown at two locations pairings (e.g. locations 1+2 in Figure 2C vs. 1+3 in Figure 2D), 309 neurons were tested in both a cRF-cRF and an sRF-cRF configuration (M1: 97; M2: 212). We classified locations as belonging to the cRF or sRF using stimulus presentations that included only one Gabor (Figure 2B; Materials and methods). Locations where either stimulus orientation generated a response were considered to lie within the cRF. Those where neither stimulus orientation generated a response were considered to lie within the surround (Figure 2—figure supplement 1). In different blocks of trials, the monkeys directed their attention toward all possible stimulus locations, one attended location per block of trials, each time ignoring the other stimulus locations. Attention was directed toward stimulus locations near the neurons' receptive fields (e.g. locations 1, 2, or 3 in Figure 2A), or toward stimulus locations away from the receptive fields ('Away' in Figure 2C,D; attend away), i.e. to stimulus locations on the opposite side of the fixation point from the neuron's receptive field. We quantified the stimulus selectivity of the neurons separately for each stimulus configuration. For each of four Gabor pairs (Figure 2B) at each pair of stimulus locations (i.e. location pairings 1+2 or 1+3, Figure 2A), we used a selectivity index ('Selectivity', Figure 2E): (P−N)/(P+N), that ranges from zero (unselective) to one (completely selective). Here P is the response to the component Gabor of a Gabor pair that generated the stronger average response when presented alone (preferred), and N is the response to other component Gabor that generated the weaker average response when presented alone (non-preferred). Note that the preferred and non-preferred Gabor within a pair were presented at two different receptive-field locations, and could have the same or a different orientation (Figure 2B). Thus stimulus selectivity between members of a Gabor pair could arise from a neuron's orientation selectivity and from its preference for spatial locations. In subsequent analyses, we will show that the relationship between attention modulation and stimulus selectivity does not depend on whether the stimulus feature is space or orientation. What is critical for attention modulation is a differential response to the component stimuli of a compound stimulus. For both the cRF-cRF and sRF-cRF condition, we measured each neuron's stimulus-induced suppression for each Gabor pair at each pair of stimulus locations using a stimulus-induced suppression index: (P−PN)/(P+PN) (middle right pictogram Figure 2E). PN is the response to the Gabor pair (P and N defined as before). This index is negative when the neuronal response increases when a non-preferred stimulus is added to the preferred stimulus, and positive when the neuronal response is suppressed by the addition of a non-preferred stimulus to the preferred stimulus. By definition, neurons do not respond to a stimulus when it appears alone inside the surround, so the surround stimulus is invariably assigned as non-preferred (N). For both the selectivity index and the stimulus-induced suppression index, the responses to the preferred (P), non-preferred (N), and their combined presentation (PN) were measured in the same attention state: when attention was directed away from the neuron's receptive field (attend away). These responses are shown in the bar-plot insets in Figure 3A–D. Figure 3 Download asset Open asset Example attention modulations. Responses of four different neurons to a selected Gabor pair are shown (measured in different sessions). (A) Example 1: cRF-cRF configuration. Left panel shows this neuron's receptive-field map with the two stimulus locations at which the Gabors were presented overlaid (white-gray dots). Right panel PSTHs show the neuronal responses to the Gabor pair when attention was directed toward the preferred Gabor (cyan line; PAttN), the non-preferred Gabor (orange line; PNAtt), or a stimulus on the opposite side of the fixation point (green dashed line; PN; attend away). Bar-plot inset shows the responses of this neuron to a Gabor pair (PN) and its component Gabors (P, N), all measured in the attend away condition. This neuron's response was selective to the component Gabors of the Gabor pair (P vs. N), suppressed by the addition of a non-preferred Gabor to a preferred Gabor (P vs. PN), and strongly modulated when attention was shifted between the two component Gabors of the Gabor pair (PAttN vs. PNAtt). (B) Example 2: another neuron in the cRF-cRF configuration. This neuron showed weak selectivity, hardly any suppression, and little attention modulation. (C) Example 3: sRF-cRF configuration with one Gabor inside the neuron's cRF, and one Gabor inside its surround. By definition, the cRF Gabor is preferred (P) and the silent surround Gabor is non-preferred (N). The neuron responded highly selectively to the cRF and the surround Gabor when presented alone (P vs. N), showed surround suppression (P vs. PN), and was modulated by attention (PAttN vs. PNAtt). (D) Example 4: another neuron in the sRF-cRF configuration. This neuron was highly selective to the component Gabors of the Gabor pair, but only weakly suppressed by the surround Gabor, and showed little attention modulation. The insets show the average waveforms of the recorded neurons (blue) plus that of the multi-unit activity measured at the same electrode (grey). Shading around the mean represents ± 2 median absolute deviation (MAD). Scale bars indicate 50 μV and 0.1 ms. The receptive-field maps were normalized to the maximum response for each neuron during receptive-field mapping (RF max), dark blue shows the baseline response. Error bars represent ± SEM. https://doi.org/10.7554/eLife.17256.006 Figure 3A–D shows examples of attention-related response modulations of four different neurons to one selected Gabor pair: two neurons in the cRF-cRF configuration (A, B) and two neurons in the sRF-cRF configuration (C, D). The neuron in Figure 3A responded selectively to the two component Gabors of the Gabor pair shown inside the neuron's cRF (inset: P vs. N; selectivity index=0.44). Its response to the preferred Gabor was suppressed when the non-preferred Gabor was added to it (inset: P vs. PN; suppression index=0.22). The position of attention profoundly affected this neuron's responses: Compared to when attention was directed away from the neuron's receptive field (dashed green line; PN; attend away), attention to the preferred Gabor increased this neuron's response (cyan line; PAttN), whereas attention to the non-preferred Gabor suppressed its response (orange line; PNAtt). Attention-related modulation was quantified using an attention-modulation index: (PAtt N−PNAtt) / (PAtt N + PNAtt) (lower pictogram Figure 2E), which is positive when the neuronal response increases when attention is directed toward the preferred Gabor, compared to when attention is directed toward the non-preferred Gabor. The attention-modulation index for example 1 was 0.48. In contrast to example 1, the response of the neuron in Figure 3B was poorly selective to the component Gabors of the Gabor pair (P vs. N; selectivity index=0.066), showed little suppression when a non-preferred Gabor was placed alongside a preferred Gabor (P vs. PN; suppression index=0.04), and was only weakly modulated when attention shifted between the preferred and the non-preferred Gabor within the cRF (cyan vs. orange line; attention-modulation index=0.04). Figure 3C shows the responses of a neuron to a Gabor pair in another stimulus configuration, in which one Gabor was placed inside the neuron's cRF and another Gabor inside its surround (sRF-cRF). As expected, the neuron responded much more to the cRF Gabor than to the surround Gabor (P vs. N; selectivity=0.963). When the surround Gabor was placed alongside the cRF Gabor, the neuron's response was greatly reduced, the hallmark of surround suppression (P vs. PN; suppression index=0.336). The neuron showed strong attention-related modulation: Compared to when attention was removed from both the cRF and the surround Gabor (dashed green line; attend away), attention to the cRF Gabor increased this neuron's response (cyan line), while attention to the surround Gabor sharply decreased its response (orange line; attention-modulation index=0.58). The response of the fourth example neuron in Figure 3D was highly selective to the component Gabors of the Gabor pair (P vs. N), only slightly suppressed by the surround Gabor (P vs. PN), and its firing rate was barely modulated by attention (cyan vs. orange line; selectivity index=0.9; suppression index=0.05; attention-modulation index=0.08). These examples illustrate the diverse stimulus selectivities, stimulus interactions (i.e. stimulus-induced suppression) and attention-related modulations in the neuronal responses in visual cortex. Next, we asked how variability in stimulus selectivity and stimulus-induced suppression relates to variability in attention modulation within the cRF and the surround across the sample of recorded neurons. Relationship among selectivity, stimulus-induced suppression and attention modulation We first examined the relationship between selectivity and attention modulation. Shifting attention between two Gabors inside the cRF was associated with larger response changes for neurons with more selective responses to the component Gabors of the Gabor pair (Figure 4A; cRF-cRF configuration; p=4 × 10−109 for a non-zero slope; linear regression) (Reynolds et al., 1999). Attention-related modulation was also stronger for neurons that responded more selectively to the cRF and surround stimulus (Figure 4B; sRF-cRF configuration; p=3 × 10−76 for a non-zero slope; linear regression). Low selectivity can occur in the sRF-cRF configuration when the cRF stimulus produces little response because it has a non-preferred orientation or is positioned at a weakly responsive cRF location. Comparing Figure 4A and B shows that attention-related modulation increases more with selectivity in the cRF-cRF than in the sRF-cRF configuration (p=5 × 10−4 for different slopes in each receptive-field configuration; general linear model). Figure 4 with 1 supplement see all Download asset Open asset First-order analyses suggest that attention modulation follows different principles for stimuli inside the cRF and the surround. (A, B) Average attention modulation as a function of the stimulus selectivity in the cRF-cRF and sRF-cRF configuration respectively. Low selectivity occurs in the sRF-cRF configuration when neurons respond weakly to the cRF stimulus, e.g. because of a non-preferred orientation or a weakly responsive cRF location, and have a baseline response to the surround stimulus. (C, D) Histogram of all stimulus-induced suppression indices measured in the cRF-cRF and sRF-cRF configuration respectively. The suppression index is negative when neurons increase their response when a non-preferred stimulus is added to the preferred stimulus (enhancing), and positive when neurons decrease their response when a non-preferred stimulus is added to the preferred stimulus (suppressing). Black bars indicate indices associated with Gabor pairs for which the suppression index differed significantly from zero (p<0.01; permutation t-test; see also Figure 4—figure supplement 1). Triangle points to the mean suppression index. (E, F) Average attention modulation as a function of stimulus-induced suppression in the cRF-cRF and sRF-cRF configuration respectively. Error bars represent ± SEM. (G, H) Stimulus-induced suppression versus stimulus selectivity for all Gabor pairs in the cRF-cRF (N = 1769) and sRF-cRF (N = 1768) configuration respectively. https://doi.org/10.7554/eLife.17256.007 We next examined stimulus-induced suppression. V4 neuronal responses on average decrease when a non-preferred stimulus is added to a preferred stimulus inside their cRF (Figure 4C; average suppression index = 0.08, p=4 × 10−104; t-test for a difference from zero) (Reynolds et al., 1999). Similarly, stimulating the surround decreases the average responses of V4 neurons (Figure 4D; average suppression index = 0.04, p=2 × 10−28; t-test) (Schein and Desimone, 1990). However, stimuli inside the surround suppressed the neuronal responses less than stimuli inside the cRF: the average suppression index for the surround condition (sRF-cRF) was significantly smaller than the average suppression index for the cRF condition (M1: p=9 × 10−6; M2: p=4 × 10−15; t-test; see below and Figure 7 for further discussion). The black bars in Figure 4C and D represent neurons that were significantly (p<0.01) suppressed by the non-preferred (surround) stimulus. See Figure 4—figure supplement 1 for some example neurons with significant surround suppression (see also Figure 3C). Surround suppression was also weaker than cRF suppression when comparing only suppression indices that differed significantly from zero (p<0.001). Extending previous findings in area MT (Ni et al., 2012; Lee, 2009), we find that V4 neurons with stronger stimulus-induced suppression by cRF stimuli also showed stronger attention modulation (Figure 4E; p=1 × 10−31 for a non-zero slope; linear regression). Furthermore, and consistent with a previous study (Sundberg et al., 2009), attention modulation was also stronger for neurons whose responses were more suppressed by a surround stimulus (Figure 4F; p=5 × 10−4 for a non-zero slope; linear regression). However, comparing Figure 4E and F shows that attention-related modulation increases more with stimulus-induced suppression in the sRF-cRF than in the cRF-cRF configuration (p=0.005 for different slopes in each receptive-field configuration; general linear model). A previous study in V4 examining the relationship among stimulus selectivity, sensory interaction (akin to stimulus-induced suppression) and attention modulation, found a strong correlation between stimulus selectivity and sensory interaction (Reynolds et al., 1999). In the present study, however, stimulus selectivity and stimulus-induced suppression were not significantly correlated with each other across neurons (Figure 4G,H Pearson correlation = 0.02, p=0.32; see Discussion for further comments on the difference between studies). This finding shows that the correlations between stimulus selectivity and attention modulation, and that between stimulus-induced suppression and attention modulation, are not explained by an underlying correlation between selectivity and suppression. Furthermore, and in contrast to previous studies, the lack of a correlation between both indices allowed us to examine the separate contributions of selectivity and suppression to the magnitude of attention modulation. The above-mentioned different relationships between stimulus selectivity, stimulus-induced suppression and attention-related modulation in the cRF-cRF and the sRF-cRF configuration, suggest that the rules that govern attention modulation
Surround suppression
Stimulus (psychology)
Modulation (music)
Cite
Citations (0)
Author(s): Williams, Nathaniel; Olson, Carl | Abstract: Primate inferotemporal cortex (ITC) neurons respond with declining strength to repeated presentations of large natural images. This phenomenon - repetition suppression - has been assumed to arise at the level of ITC because ITC neurons possess the large receptive fields and sophisticated selectivity to recognize images as repetitions. It was recently discovered that V2 neurons exhibit repetition suppression under identical conditions. How do V2 neurons, with classical receptive fields encompassing only a small fraction of the image, recognize it as a repetition? One possibility is that they are sensitive to repetition of content outside the classical receptive field, in the surround. To assess this, we recorded neuronal responses to displays while independently controlling repetition of elements in the classical receptive field and the surround. We found that content in the surround contributed to repetition suppression and that this occurred relatively late in the response, consistent with being mediated by feedback.
Surround suppression
Repetition (rhetorical device)
Cite
Citations (0)
Nature and Interaction of Signals From the Receptive Field Center and Surround in Macaque V1 Neurons
Information is integrated across the visual field to transform local features into a global percept. We now know that V1 neurons provide more spatial integration than originally thought due to the existence of their nonclassical inhibitory surrounds. To understand spatial integration in the visual cortex, we have studied the nature and extent of center and surround influences on neuronal response. We used drifting sinusoidal gratings in circular and annular apertures to estimate the sizes of the receptive field's excitatory center and suppressive surround. We used combinations of stimuli inside and outside the receptive field to explore the nature of the surround influence on the receptive field center as a function of the relative and absolute contrast of stimuli in the two regions. We conclude that the interaction is best explained as a divisive modulation of response gain by signals from the surround. We then develop a receptive field model based on the ratio of signals from Gaussian-shaped center and surround mechanisms. We show that this model can account well for the variations in receptive field size with contrast that we and others have observed and for variations in size with the state of contrast adaptation. The model achieves this success by simple variations in the relative gain of the two component mechanisms of the receptive field. This model thus offers a parsimonious explanation of a variety of phenomena involving changes in apparent receptive field size and accounts for these phenomena purely in terms of two receptive field mechanisms that do not themselves change in size. We used the extent of the center mechanism in our model as an indicator of the spatial extent of the central excitatory portion of the receptive field. We compared the extent of the center to measurements of horizontal connections within V1 and determined that horizontal intracortical connections are well matched in extent to the receptive field center mechanism. Input to the suppressive surround may come in part from feedback signals from higher areas.
Surround suppression
Percept
Cite
Citations (803)
Surround suppression
Stimulus (psychology)
Cite
Citations (0)