Figure 3 - uploaded by Yang Li
Content may be subject to copyright.
A single normalization only explains spatial integration in V1 input layers (A) The fitting performances of the DoG (left) and ratio-of-Gaussian (RoG) (right) models to a site in layer 3 (top) and layer 4Ca (bottom). A schematic of the model structure is shown on the top of the tuning. The filled and hollow dots represent the raw data of the patch-size and annulus-size tuning curves, respectively. The solid and dashed blue lines show the model fit tunings to the patch-size and annulus-size tuning curves, respectively. Error bars indicate SEM. (B) Comparisons of GoF (see STAR Methods) for the DoG model and RoG model. The blue and red dots are sites from output and input layers, respectively (input, N = 95; output, N = 134). (C) The averaged GoF for input and output layers from DoG and RoG models. Error bars indicate SEM. See also Figure S2.

A single normalization only explains spatial integration in V1 input layers (A) The fitting performances of the DoG (left) and ratio-of-Gaussian (RoG) (right) models to a site in layer 3 (top) and layer 4Ca (bottom). A schematic of the model structure is shown on the top of the tuning. The filled and hollow dots represent the raw data of the patch-size and annulus-size tuning curves, respectively. The solid and dashed blue lines show the model fit tunings to the patch-size and annulus-size tuning curves, respectively. Error bars indicate SEM. (B) Comparisons of GoF (see STAR Methods) for the DoG model and RoG model. The blue and red dots are sites from output and input layers, respectively (input, N = 95; output, N = 134). (C) The averaged GoF for input and output layers from DoG and RoG models. Error bars indicate SEM. See also Figure S2.

Source publication
Article
Full-text available
Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response p...

Contexts in source publication

Context 1
... confirmed that either form of suppression could well explain patch-size tuning curves across V1 layers (see fitting details in STAR Methods) in our dataset (see Figures S2A-S2F). However, when neural responses to stimulus annuli at different inner diameters (annulus-size tuning curve) were also considered (see STAR Methods for details), none of the two descriptive models could capture the characteristics of spatial integration in V1 output layers (blue curves in Figure 3A). A single subtractive suppression (DoG model) failed to capture response patterns elicited by patches and annuli in all V1 layers ( Figure 3A, first column), and although a single divisive suppression (RoG model) fitted the patch-size and annulus-size tuning curves in input layers well, it failed to account for tunings in the output layers ( Figure 3A, second column). ...
Context 2
... when neural responses to stimulus annuli at different inner diameters (annulus-size tuning curve) were also considered (see STAR Methods for details), none of the two descriptive models could capture the characteristics of spatial integration in V1 output layers (blue curves in Figure 3A). A single subtractive suppression (DoG model) failed to capture response patterns elicited by patches and annuli in all V1 layers ( Figure 3A, first column), and although a single divisive suppression (RoG model) fitted the patch-size and annulus-size tuning curves in input layers well, it failed to account for tunings in the output layers ( Figure 3A, second column). According to anatomical studies (Callaway, 1998;Lund, 1988;Sincich and Horton, 2005), responses in V1 output layers should be explained by the interaction of feedforward drives from input layers and suppression in output layers ( Rossi et al., 2020;Wang et al., 2020). ...
Context 3
... when neural responses to stimulus annuli at different inner diameters (annulus-size tuning curve) were also considered (see STAR Methods for details), none of the two descriptive models could capture the characteristics of spatial integration in V1 output layers (blue curves in Figure 3A). A single subtractive suppression (DoG model) failed to capture response patterns elicited by patches and annuli in all V1 layers ( Figure 3A, first column), and although a single divisive suppression (RoG model) fitted the patch-size and annulus-size tuning curves in input layers well, it failed to account for tunings in the output layers ( Figure 3A, second column). According to anatomical studies (Callaway, 1998;Lund, 1988;Sincich and Horton, 2005), responses in V1 output layers should be explained by the interaction of feedforward drives from input layers and suppression in output layers ( Rossi et al., 2020;Wang et al., 2020). ...
Context 4
... divisive and subtractive suppressions at the second stage, as well as the excitation and divisive suppression at the first stage (RoG model), were all modeled as summated responses with Gaussian functions for their pooling weights in space (represented by s exc_in , s div_in , s div_out , and s sub_out , respectively; see STAR Methods). In the CN model, its first stage explained both patch-and annulus-size tuning curves in input layers ( Figure 3C), and, more importantly, its second stage made excellent performances for fitting those tunings in output layers recorded in the same probe placement (Fig- ure 4B for an example site, and the inset of Figure 4B is the distribution of the goodness of fit for individual sites). ...
Context 5
... also tried other combinations of subtractive or divisive suppressions (Figures 4C and S3; Equations 10, 11, 12, 13, and 14). Our results showed that the cascaded structure and the divisive suppression at the second stage were important to capture the spatial integration in output layers ( Figure S3). The operational sequence for the divisive and subtractive suppression had a minor effect. ...
Context 6
... Figure S6F). However, a model with a single divisive and subtractive operation added to an excited Gaussian was not enough to capture the relative response increase and its spatial range for both patch and annulus tunings (the rightmost column in Figures S3A-S3C). These results suggested that two divisions are needed. ...
Context 7
... is largely because the patch-size tuning curve (responses to grating patches of different sizes) was solely used in most studies and there were not enough constraints for differing models (also see our confirmation in Figure S2). However, when experimental conditions with more spatial configurations were included in the model evaluations, the medium SS in V1 input layer could only be fitted by the traditional RoG model with a divisive computation (Figure 3), and an additional normalization followed by a subtraction was required to explain the strong SS in output layer (Figure 4). Most studies ( Alitto et al., 2019;Cavanaugh et al., 2002;DeAngelis et al., 1994;Fisher et al., 2017;Keller et al., 2020a;Levitt and Lund, 2002;Vangeneugden et al., 2019;Yu et al., 2022) have used 1D models (DoG or RoG) to describe the SS in patch-size tuning, but a 2D model ( Roberts et al., 2005;Sce- niak et al., 2006) is more appropriate to describe the properties of spatial integration because the cortical neuron at a given V1 depth and visual stimuli used in visual space are both in 2D space. ...
Context 8
... types of 2D CN models, shown above, suggested that cascaded global normalizations in a model were necessary to explain the spatial integration in V1, and all other results and conclusions based on the 1D CN model are also held with those based on these 2D models with cascaded normalizations (Figures 4 and S5 and Tables S2 and S3 for the comparisons). We kept the 1D CN model (Fig- ures 3 and 4) in result sections for easy comparison to the massive studies using 1D models. ...
Context 9
... confirmed that either form of suppression could well explain patch-size tuning curves across V1 layers (see fitting details in STAR Methods) in our dataset (see Figures S2A-S2F). However, when neural responses to stimulus annuli at different inner diameters (annulus-size tuning curve) were also considered (see STAR Methods for details), none of the two descriptive models could capture the characteristics of spatial integration in V1 output layers (blue curves in Figure 3A). A single subtractive suppression (DoG model) failed to capture response patterns elicited by patches and annuli in all V1 layers ( Figure 3A, first column), and although a single divisive suppression (RoG model) fitted the patch-size and annulus-size tuning curves in input layers well, it failed to account for tunings in the output layers ( Figure 3A, second column). ...
Context 10
... when neural responses to stimulus annuli at different inner diameters (annulus-size tuning curve) were also considered (see STAR Methods for details), none of the two descriptive models could capture the characteristics of spatial integration in V1 output layers (blue curves in Figure 3A). A single subtractive suppression (DoG model) failed to capture response patterns elicited by patches and annuli in all V1 layers ( Figure 3A, first column), and although a single divisive suppression (RoG model) fitted the patch-size and annulus-size tuning curves in input layers well, it failed to account for tunings in the output layers ( Figure 3A, second column). According to anatomical studies (Callaway, 1998;Lund, 1988;Sincich and Horton, 2005), responses in V1 output layers should be explained by the interaction of feedforward drives from input layers and suppression in output layers ( Rossi et al., 2020;Wang et al., 2020). ...
Context 11
... when neural responses to stimulus annuli at different inner diameters (annulus-size tuning curve) were also considered (see STAR Methods for details), none of the two descriptive models could capture the characteristics of spatial integration in V1 output layers (blue curves in Figure 3A). A single subtractive suppression (DoG model) failed to capture response patterns elicited by patches and annuli in all V1 layers ( Figure 3A, first column), and although a single divisive suppression (RoG model) fitted the patch-size and annulus-size tuning curves in input layers well, it failed to account for tunings in the output layers ( Figure 3A, second column). According to anatomical studies (Callaway, 1998;Lund, 1988;Sincich and Horton, 2005), responses in V1 output layers should be explained by the interaction of feedforward drives from input layers and suppression in output layers ( Rossi et al., 2020;Wang et al., 2020). ...
Context 12
... divisive and subtractive suppressions at the second stage, as well as the excitation and divisive suppression at the first stage (RoG model), were all modeled as summated responses with Gaussian functions for their pooling weights in space (represented by s exc_in , s div_in , s div_out , and s sub_out , respectively; see STAR Methods). In the CN model, its first stage explained both patch-and annulus-size tuning curves in input layers ( Figure 3C), and, more importantly, its second stage made excellent performances for fitting those tunings in output layers recorded in the same probe placement (Fig- ure 4B for an example site, and the inset of Figure 4B is the distribution of the goodness of fit for individual sites). ...
Context 13
... also tried other combinations of subtractive or divisive suppressions (Figures 4C and S3; Equations 10, 11, 12, 13, and 14). Our results showed that the cascaded structure and the divisive suppression at the second stage were important to capture the spatial integration in output layers ( Figure S3). The operational sequence for the divisive and subtractive suppression had a minor effect. ...
Context 14
... Figure S6F). However, a model with a single divisive and subtractive operation added to an excited Gaussian was not enough to capture the relative response increase and its spatial range for both patch and annulus tunings (the rightmost column in Figures S3A-S3C). These results suggested that two divisions are needed. ...
Context 15
... is largely because the patch-size tuning curve (responses to grating patches of different sizes) was solely used in most studies and there were not enough constraints for differing models (also see our confirmation in Figure S2). However, when experimental conditions with more spatial configurations were included in the model evaluations, the medium SS in V1 input layer could only be fitted by the traditional RoG model with a divisive computation (Figure 3), and an additional normalization followed by a subtraction was required to explain the strong SS in output layer (Figure 4). Most studies ( Alitto et al., 2019;Cavanaugh et al., 2002;DeAngelis et al., 1994;Fisher et al., 2017;Keller et al., 2020a;Levitt and Lund, 2002;Vangeneugden et al., 2019;Yu et al., 2022) have used 1D models (DoG or RoG) to describe the SS in patch-size tuning, but a 2D model ( Roberts et al., 2005;Sce- niak et al., 2006) is more appropriate to describe the properties of spatial integration because the cortical neuron at a given V1 depth and visual stimuli used in visual space are both in 2D space. ...
Context 16
... types of 2D CN models, shown above, suggested that cascaded global normalizations in a model were necessary to explain the spatial integration in V1, and all other results and conclusions based on the 1D CN model are also held with those based on these 2D models with cascaded normalizations (Figures 4 and S5 and Tables S2 and S3 for the comparisons). We kept the 1D CN model (Fig- ures 3 and 4) in result sections for easy comparison to the massive studies using 1D models. ...

Citations

... The direction-specific microsaccade modulation (DSMM) in V2 can be explained by either a mechanism from an extraretinal source (microsaccade mechanism) or a mechanism due to RF sensitivity to microsaccade-induced stimulus motion on the retina (RF mechanism). Although we used large squares with uniform luminance to avoid any stimulus changes in the classical RFs during microsaccade generation, the nonclassical RF of V2 might still capture the microsaccade-induced motions/displacements over the edge of the squares (surround modulation 30 ). ...
Article
Full-text available
Microsaccades play a critical role in refreshing visual information and have been shown to have direction-specific influences on human perception. However, the neural mechanisms underlying such direction-specific effects remains unknown. Here, we report the emergence of direction-specific microsaccade modulation in the middle layer of V2 but not in V1: responses of V2 neurons after microsaccades moved toward their receptive fields were stronger than those when microsaccades moved away. The decreased responses from V1 to V2, which are correlated with the amplitude of microsaccades away from receptive fields, suggest topographically location-specific suppression from an oculomotor source. Consistent with directional effects in V2, microsaccades function as a guide for monkeys’ behavior in a peripheral detection task; both can be explained by a dynamic neural network. Our findings suggest a V1-bypassing suppressive circuit for direction-specific microsaccade modulation in V2 and its functional influence on visual sensitivity, which highlights the optimal sampling nature of microsaccades. How microsaccades modulate visual coding and perception remains incompletely understood. Here, the authors identify an emerging suppression specific to microsaccade directions that alters responses in macaque V2 and impacts perceptual decisions.