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CNs capture spatial integration in V1 output layers (A) Schematic of the CN model structure. Stimuli are shown at the bottom. The tuning curves in input layers are fitted by the RoG model. The responses in output layers depend on excitatory drive from input layers, subtractive suppression, and divisive suppression. The red arrow indicates the direction of the excitatory input. The names of the four spatial spreads are at the upper right corner of the Gaussian curve. (B) The solid and dashed blue lines show the model fit tunings to the raw data for the same L3 site in Figure 2A. The GoF is 0.96. (Inset) Histograms of GoF for the CN model for 134 recording sites in output layers. The mean GoF is 0.93. Error bars indicate SEM. (C) Comparison of GoF (black curve in the left axis) and explained variance (purple curve in the right axis; see STAR Methods) among different forms of the CN model. Operators (-or O) at the top represent the operations used by the corresponding model, with the lower operators executed before the upper operators (if any). Error bars indicate SEM. (D) Comparison of spatial spreads in the CN model. exc_in and div_in represent the spatial spreads of feedforward excitation and divisive suppression in input layers (1.96s exc_in , 1.96s div_in in Equation 8), respectively; div_out and sub_out represent the spatial spreads of divisive and subtractive suppression in output layers (1.96s div_out , 1.96s sub_out in Equation 9), respectively (visual space in the left axis, cortical space in the right axis) (Dow et al., 1981). Error bars indicate SEM. (E) The relationship between the spatial spreads of both divisive suppressions in input and output layers and local connections. FF, feedforward connection; LC, local connection. Scale bar, 2 mm. See also Figures S3-S5.

CNs capture spatial integration in V1 output layers (A) Schematic of the CN model structure. Stimuli are shown at the bottom. The tuning curves in input layers are fitted by the RoG model. The responses in output layers depend on excitatory drive from input layers, subtractive suppression, and divisive suppression. The red arrow indicates the direction of the excitatory input. The names of the four spatial spreads are at the upper right corner of the Gaussian curve. (B) The solid and dashed blue lines show the model fit tunings to the raw data for the same L3 site in Figure 2A. The GoF is 0.96. (Inset) Histograms of GoF for the CN model for 134 recording sites in output layers. The mean GoF is 0.93. Error bars indicate SEM. (C) Comparison of GoF (black curve in the left axis) and explained variance (purple curve in the right axis; see STAR Methods) among different forms of the CN model. Operators (-or O) at the top represent the operations used by the corresponding model, with the lower operators executed before the upper operators (if any). Error bars indicate SEM. (D) Comparison of spatial spreads in the CN model. exc_in and div_in represent the spatial spreads of feedforward excitation and divisive suppression in input layers (1.96s exc_in , 1.96s div_in in Equation 8), respectively; div_out and sub_out represent the spatial spreads of divisive and subtractive suppression in output layers (1.96s div_out , 1.96s sub_out in Equation 9), respectively (visual space in the left axis, cortical space in the right axis) (Dow et al., 1981). Error bars indicate SEM. (E) The relationship between the spatial spreads of both divisive suppressions in input and output layers and local connections. FF, feedforward connection; LC, local connection. Scale bar, 2 mm. See also Figures S3-S5.

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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...

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... capture spatial integration in V1 output layers There are two stages in the CN model (see model details in STAR Methods) ( Figure 4A Article ll (patch-size tuning and annulus-size tuning) in the input layers recorded in a given probe placement. The second stage of the CN model aimed to account for spatial integration in output layers recorded in the same probe placement. ...
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... there are five computational components (excitation and suppressions) in the CN model: two components at first stage and three components at the second stage. The two components at the first stage (the RoG model) are an excitation (represented by exc_in) and a divisive suppression (represented by div_in) ( Figure 4A and Equation 8); and the three components at the second stage are an excitation (represented by ff), a divisive suppression (represented by div_out), and a subtractive suppression (represented by sub_out) ( Figure 4A and Equation 9). The 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). ...
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... there are five computational components (excitation and suppressions) in the CN model: two components at first stage and three components at the second stage. The two components at the first stage (the RoG model) are an excitation (represented by exc_in) and a divisive suppression (represented by div_in) ( Figure 4A and Equation 8); and the three components at the second stage are an excitation (represented by ff), a divisive suppression (represented by div_out), and a subtractive suppression (represented by sub_out) ( Figure 4A and Equation 9). The 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). ...
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... 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). ...
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... 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). ...
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... difference between the whole model and our original CN model (Equations 8 and 9) was that the whole model explains responses in output layers without knowing any information about responses in the input layers (in other words, model parameters K 1 , K 2 , s 1 , s 2 for the first stage in Equation 16 were optimized without any constraint from data in input layers), but our original CN model explained responses in output layers by knowing information about responses in the input layers in the same recording session (because model parameters of the first stage in the model have to explain responses in the input layers). The whole model could also fit the patch-and annulus-size tuning curves in the output layers well ( Figure S4A). More importantly, model parameters (K 1 , K 2 , s 1 , s 2 ) in the whole model corresponding to those at the first stage in our CN model (Equation 8) could predict the responses of the input layers from the same recording session. ...
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... importantly, model parameters (K 1 , K 2 , s 1 , s 2 ) in the whole model corresponding to those at the first stage in our CN model (Equation 8) could predict the responses of the input layers from the same recording session. The predicted tuning curves, the CRFs, and the SSIs, solely based on information in the output layers of each recording session, were all significantly correlated with those in the input layers ( Figures S4B and S4C). This result suggests that the CN model has a good generalizability. ...
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... the first stage of the CN model (input layers), the spread of divisive suppression (1.96s div_in ) is larger than the excitatory spread (1.96s exc_in ), which is consistent with previous studies (Sceniak et al., 2001). In the second stage of the CN model (output layers), we found that subtractive suppression occupied a local range, whereas divisive suppression occupied a much larger spatial range ( Figure 4D). The divisive suppressions in both input layers and output layers are two to three times larger than the feedforward excitation in input layers (1.96s exc_in ), indicating that both divisive suppressions play a global role, while subtractive suppression is a rather local computation. ...
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... far, we have shown performances and contributions of different computational components in the CN model in 1D space (Figures 4 and 5). We found that models with CNs in two-dimensional (2D) space ( Figure S5) can also account for our data, and conclusions based on 2D models are held similarly, which will be elaborated on in the STAR Methods and supplemental materials and discussion. ...
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... 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. ...
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... the other hand, the activation of an artificial neuron is normalized by the entire feature map, which has a uniform weight in space under the BN ( Ioffe and Szegedy, 2015). In contrast, the spatial extents of normalizations are about two to four times larger than the excitatory spatial extents in V1 (Figure 4). Recently, some modified CNNs considering biological normalization or spatial schemes have shown improvements in different ways ( Giraldo and Schwartz, 2019;Hasani et al., 2019;Iyer et al., 2020;Ren et al., 2016), while the structures and spatial extents are not similar to our results in V1 (Figure 4). ...
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... contrast, the spatial extents of normalizations are about two to four times larger than the excitatory spatial extents in V1 (Figure 4). Recently, some modified CNNs considering biological normalization or spatial schemes have shown improvements in different ways ( Giraldo and Schwartz, 2019;Hasani et al., 2019;Iyer et al., 2020;Ren et al., 2016), while the structures and spatial extents are not similar to our results in V1 (Figure 4). In addition, our results and other previous studies all show significant laminar variances ( Bijanzadeh et al., 2018;Henry et al., 2013Wang et al., 2020;Xing et al., 2012;Yeh et al., 2009) of neural properties, which suggests the importance of interlaminar processing, while the modulation within layers is limited in CNNs ( Figure 6E). ...
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... can hardly reveal the underlying interactions between excitation and inhibition-like results from intracellular recordings or dynamic models ( Angelucci et al., 2017;Haider and McCormick, 2009;Li et al., 2020;Ozeki et al., 2009;Schwabe et al., 2006;Shushruth et al., 2012). Furthermore, there is a significant difference in the spatial profile of the 1D and 2D Gaussian, especially when fitting small-sized patch stimuli (Figures 4B and S5B). The spatial profile in the Gaussian form has been a widely used function in the descriptive model; whether the spatial connection weights among the population neurons conform to the Gaussian shape is an interesting question for further exploration. ...
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... capture spatial integration in V1 output layers There are two stages in the CN model (see model details in STAR Methods) ( Figure 4A Article ll (patch-size tuning and annulus-size tuning) in the input layers recorded in a given probe placement. The second stage of the CN model aimed to account for spatial integration in output layers recorded in the same probe placement. ...
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... there are five computational components (excitation and suppressions) in the CN model: two components at first stage and three components at the second stage. The two components at the first stage (the RoG model) are an excitation (represented by exc_in) and a divisive suppression (represented by div_in) ( Figure 4A and Equation 8); and the three components at the second stage are an excitation (represented by ff), a divisive suppression (represented by div_out), and a subtractive suppression (represented by sub_out) ( Figure 4A and Equation 9). The 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). ...
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... there are five computational components (excitation and suppressions) in the CN model: two components at first stage and three components at the second stage. The two components at the first stage (the RoG model) are an excitation (represented by exc_in) and a divisive suppression (represented by div_in) ( Figure 4A and Equation 8); and the three components at the second stage are an excitation (represented by ff), a divisive suppression (represented by div_out), and a subtractive suppression (represented by sub_out) ( Figure 4A and Equation 9). The 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). ...
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... 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). ...
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... 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). ...
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... difference between the whole model and our original CN model (Equations 8 and 9) was that the whole model explains responses in output layers without knowing any information about responses in the input layers (in other words, model parameters K 1 , K 2 , s 1 , s 2 for the first stage in Equation 16 were optimized without any constraint from data in input layers), but our original CN model explained responses in output layers by knowing information about responses in the input layers in the same recording session (because model parameters of the first stage in the model have to explain responses in the input layers). The whole model could also fit the patch-and annulus-size tuning curves in the output layers well ( Figure S4A). More importantly, model parameters (K 1 , K 2 , s 1 , s 2 ) in the whole model corresponding to those at the first stage in our CN model (Equation 8) could predict the responses of the input layers from the same recording session. ...
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... importantly, model parameters (K 1 , K 2 , s 1 , s 2 ) in the whole model corresponding to those at the first stage in our CN model (Equation 8) could predict the responses of the input layers from the same recording session. The predicted tuning curves, the CRFs, and the SSIs, solely based on information in the output layers of each recording session, were all significantly correlated with those in the input layers ( Figures S4B and S4C). This result suggests that the CN model has a good generalizability. ...
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... the first stage of the CN model (input layers), the spread of divisive suppression (1.96s div_in ) is larger than the excitatory spread (1.96s exc_in ), which is consistent with previous studies (Sceniak et al., 2001). In the second stage of the CN model (output layers), we found that subtractive suppression occupied a local range, whereas divisive suppression occupied a much larger spatial range ( Figure 4D). The divisive suppressions in both input layers and output layers are two to three times larger than the feedforward excitation in input layers (1.96s exc_in ), indicating that both divisive suppressions play a global role, while subtractive suppression is a rather local computation. ...
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... far, we have shown performances and contributions of different computational components in the CN model in 1D space (Figures 4 and 5). We found that models with CNs in two-dimensional (2D) space ( Figure S5) can also account for our data, and conclusions based on 2D models are held similarly, which will be elaborated on in the STAR Methods and supplemental materials and discussion. ...
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... 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. ...
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... the other hand, the activation of an artificial neuron is normalized by the entire feature map, which has a uniform weight in space under the BN ( Ioffe and Szegedy, 2015). In contrast, the spatial extents of normalizations are about two to four times larger than the excitatory spatial extents in V1 (Figure 4). Recently, some modified CNNs considering biological normalization or spatial schemes have shown improvements in different ways ( Giraldo and Schwartz, 2019;Hasani et al., 2019;Iyer et al., 2020;Ren et al., 2016), while the structures and spatial extents are not similar to our results in V1 (Figure 4). ...
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... contrast, the spatial extents of normalizations are about two to four times larger than the excitatory spatial extents in V1 (Figure 4). Recently, some modified CNNs considering biological normalization or spatial schemes have shown improvements in different ways ( Giraldo and Schwartz, 2019;Hasani et al., 2019;Iyer et al., 2020;Ren et al., 2016), while the structures and spatial extents are not similar to our results in V1 (Figure 4). In addition, our results and other previous studies all show significant laminar variances ( Bijanzadeh et al., 2018;Henry et al., 2013Wang et al., 2020;Xing et al., 2012;Yeh et al., 2009) of neural properties, which suggests the importance of interlaminar processing, while the modulation within layers is limited in CNNs ( Figure 6E). ...
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... can hardly reveal the underlying interactions between excitation and inhibition-like results from intracellular recordings or dynamic models ( Angelucci et al., 2017;Haider and McCormick, 2009;Li et al., 2020;Ozeki et al., 2009;Schwabe et al., 2006;Shushruth et al., 2012). Furthermore, there is a significant difference in the spatial profile of the 1D and 2D Gaussian, especially when fitting small-sized patch stimuli (Figures 4B and S5B). The spatial profile in the Gaussian form has been a widely used function in the descriptive model; whether the spatial connection weights among the population neurons conform to the Gaussian shape is an interesting question for further exploration. ...

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 ). ...
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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.