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. 2022 Sep 14;610(7930):135–142. doi: 10.1038/s41586-022-05196-w

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© The Author(s) 2022, corrected publication 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 9 — a, Schematic of the linear regression-based model used to predict the number of spikes evoked by saccades on a vertical grating. The model is based on the response of neurons to pseudo-saccades (visual component) and to saccades on a gray screen (non-visual component). Results from the sum of the two inputs are shown in Fig. 5g in which the model explains 86% of the observed variance. b, Model prediction results for all neurons that respond to pseudo-saccades and saccades on a gray monitor. Left, predicted number of spikes from the response to pseudo-saccades alone (x-axis) plotted against the observed values (y-axis). This model explains only 40% of the observed variance (gain 0.97, p < 0.0001). Right, predicted number of spikes from the response to saccades on a gray screen alone (x-axis) plotted against the observed values (y-axis). This model explains only 69% of the observed variance (gain 0.89, p < 0.0001). c, Model prediction results for layer 2/3 and 4 neurons that respond to pseudo-saccades and saccades on a gray monitor. Left, predicted number of spikes from the summed response to pseudo-saccades and saccades on a gray monitor (x-axis) plotted against the observed values (y-axis). This model explains 80% of the observed variance (gain 0.49, p < 0.0001). Center, predicted number of spikes from the response to pseudo-saccades alone (x-axis) plotted against the observed values (y-axis). This model explains 80% of the observed variance (gain 0.62, p < 0.0001). Right, predicted number of spikes from the response to saccades on a gray screen alone (x-axis) plotted against the observed values (y-axis). This model explains only 49% of the observed variance (gain 1.49, p < 0.0001). Note the lack of difference in prediction accuracy between the summation model (left) and the visual response model (center). The response to saccades on a grating in layer 2/3 is mainly shaped by the visual inputs. d, Same as in (c), but for layer 5 neurons. Left, gain 0.63, p < 0.0001. Center, gain 1.22, p < 0.0001. Right, gain 0.85, p < 0.0001. Note the increase in prediction accuracy in the summation model (left) compared to the other models. The response to saccades on a grating in layer 5 is shaped by the combination of visual and non-visual inputs. e, Same as in (c), but for layer 6 neurons. Left, gain 0.66, p < 0.0001. Center, gain 1.17, p < 0.0001. Right, gain 0.88, p < 0.0001. Again, note the increase in prediction accuracy in the summation model (left) compared to the other models.