Predictive-coding and dynamical-systems accounts of mouse cortical activity tha…

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Predictive-coding and dynamical-systems accounts of mouse cortical activity that lean on recurrent E→E loops

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  • 1Citationpaper:paper-153270da8b2b{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference {ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference {ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...

  • 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference {ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...

  • 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference {ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...

  • 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference {ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...

  • 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference {ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...

  • 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference {ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...

  • 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference {ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...

  • 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference {ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...

  • 10Citationpaper:paper-aa86308d9757Predictive coding's most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...content/14_predictive_coding.md:line 9Open reference Predictive coding’s most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference0 Predictive coding’s most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference1 Predictive coding’s most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference2 Predictive coding’s most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference3 Predictive coding’s most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference4 Predictive coding’s most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference5 Predictive coding’s most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference6 Predictive coding’s most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference7 Latency of prediction-error / deviance signals in mouse V1. Onset latency (ms post-deviant or post-expected event) of reported prediction-error signals across three mouse V1 studies. Marker shape encodes study and population definition. The shaded band marks a ≈30–80 ms cortico-cortical loop window typical of V1↔higher-area interactions in mouse cortex. Paradigms differ (sequence-learning suppression vs. oddball...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference8 Latency of prediction-error / deviance signals in mouse V1. Onset latency (ms post-deviant or post-expected event) of reported prediction-error signals across three mouse V1 studies. Marker shape encodes study and population definition. The shaded band marks a ≈30–80 ms cortico-cortical loop window typical of V1↔higher-area interactions in mouse cortex. Paradigms differ (sequence-learning suppression vs. oddball...

  • 2Citationpaper:paper-b19acbdbbd6c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference9 Latency of prediction-error / deviance signals in mouse V1. Onset latency (ms post-deviant or post-expected event) of reported prediction-error signals across three mouse V1 studies. Marker shape encodes study and population definition. The shaded band marks a ≈30–80 ms cortico-cortical loop window typical of V1↔higher-area interactions in mouse cortex. Paradigms differ (sequence-learning suppression vs. oddball...

  • 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference0 The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference1 showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...

  • 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference2 The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference3 showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...

  • 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference4 The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference5 showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...

  • 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference6 The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference7 showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...

  • 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference8 The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. 3Citationpaper:paper-247576352882{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference9 showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...

  • 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference0 The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference1 showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...

  • 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference2 The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference3 showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...

  • 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference4 The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference5 showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...

  • 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference6 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference7 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference8 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 4Citationpaper:paper-d7fee8dd4415{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference9 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference0 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference1 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference2 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference3 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference4 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference5 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference6 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference7 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference8 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 5Citationpaper:paper-78e291abc5a8{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference9 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference0 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference1 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference2 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference3 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference4 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference5 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference6 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference7 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference8 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 6Citationpaper:paper-2c0a745bdf7d{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference9 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference0 Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference1 recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...

  • 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference2 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference3 argue that unexpected visual stimuli boost the L2/3 V1 neurons most selective for that stimulus, supported by a pulvinar-VIP-SOM disinhibitory motif — a dedicated, feature-selective prediction-error signal. 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference4 reach the opposite conclusion: visuomotor “mismatch” signals in mouse V1 can be reproduced by purely sensory perturbations and are explained by feature selectivity plus convergence of...

  • 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference5 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference6 argue that unexpected visual stimuli boost the L2/3 V1 neurons most selective for that stimulus, supported by a pulvinar-VIP-SOM disinhibitory motif — a dedicated, feature-selective prediction-error signal. 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference7 reach the opposite conclusion: visuomotor “mismatch” signals in mouse V1 can be reproduced by purely sensory perturbations and are explained by feature selectivity plus convergence of...

  • 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference8 7Citationpaper:paper-02c8d20ac0ce{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference9 argue that unexpected visual stimuli boost the L2/3 V1 neurons most selective for that stimulus, supported by a pulvinar-VIP-SOM disinhibitory motif — a dedicated, feature-selective prediction-error signal. 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference0 reach the opposite conclusion: visuomotor “mismatch” signals in mouse V1 can be reproduced by purely sensory perturbations and are explained by feature selectivity plus convergence of...

  • 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference1 The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference2 identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference3 showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....

  • 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference4 The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference5 identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference6 showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....

  • 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference7 The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference8 identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while 8Citationpaper:paper-39de736d98ee{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference9 showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....

  • 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference0 The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference1 identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference2 showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....

  • 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference3 The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference4 identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference5 showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....

  • 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference6 The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference7 identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while 9Citationpaper:paper-c60ef9d7671c{ref}sec-attractor-network-models evaluated content-addressable and continuous attractor descriptions against the mouse record. Two complementary theoretical lenses now claim the same circuit motif — dense E→E recurrence within and between mouse cortical areas — but ask different questions of it. Predictive coding casts recurrence as a substrate for sending top-down predictions and reading out residual errors, an...content/14_predictive_coding.md:line 5Open reference8 showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....

  • ... 62 additional anchors in refs_json

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  13. [Gallimore2023a] paper:paper-c0f0ae0b432b “Predictive coding's most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...”
  14. [Knudstrup2024] paper:paper-805e7b60808f “Predictive coding's most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...”
  15. [Tang2023a] paper:paper-47481d2d0ea8 “Predictive coding's most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...”
  16. [Luo2023] paper:paper-5fd6be578429 “Predictive coding's most testable claim in mouse V1 is that learned regularities generate stimulus-selective responses to violations. The empirical record now contains four classes of evidence — late suppression of expected stimuli, deviance detection in oddball sequences, omission-evoked firing, and feature-selective amplification of unexpected stimuli — each measured in a different paradigm, and each examined in t...”
  17. [Knudstrup2025] paper:paper-917867088bf8 “The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. [Knudstrup2025] showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...”
  18. [Becker2026] paper:paper-c18b250bd455 “The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. [Knudstrup2025] showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...”
  19. [Hamm2016] paper:paper-39f1d9599679 “The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. [Knudstrup2025] showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...”
  20. [Goltstein2018] paper:paper-accd87268d6e “The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. [Knudstrup2025] showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...”
  21. [Peterka2026] paper:paper-9ba2fd7b1eff “The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. [Knudstrup2025] showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...”
  22. [Wyrick2023] paper:paper-b9b66bddfd7c “The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. [Knudstrup2025] showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...”
  23. [Kirchberger2023] paper:paper-c3bc60ccca02 “The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. [Knudstrup2025] showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...”
  24. [Shin2023b] paper:paper-448ea197001d “The L2/3 deviance signals at 100–230 ms exceed the timing of a single feedforward sweep but fit comfortably within one to two cortico-cortical loop closures, an interpretation supported by causal feedback perturbations. [Knudstrup2025] showed that mouse V1 deviance responses depend on both first- and second-order sequence structure, scale with predictability, and exceed what short-term adaptation can explain; [Becke...”
  25. [Furutachi2024] paper:paper-41aa556ea384 “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  26. [Fu2015] paper:paper-f22eb24f5525 “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  27. [Roth2016] paper:paper-b92f6eb9a796 “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  28. [Huang2025b] paper:paper-a93a660fe891 “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  29. [Fisek2023] paper:paper-95604f6d0bfb “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  30. [Larkum2004] paper:paper-ef245d6ce6aa “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  31. [Kok2016] paper:paper-8565881e1948 “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  32. [Jordan2023] paper:paper-532287620633 “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  33. [Vasilevskaya2023] paper:paper-5ad2ab12d45c “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  34. [Hoffmann2024] paper:paper-2000202c9b1b “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  35. [Pakan2016] paper:paper-acf576c0a2ed “Predictive coding in its Rao–Ballard form predicts that distinct cell classes carry predictions and errors, with feedforward and feedback laminar pathways distinguishing the two. The cell-class identity of error coders in mouse V1 remains the most actively contested element of this microcircuit story. [Furutachi2024] recorded mouse V1 L2/3 with pulvinar and interneuron perturbations and reported that unexpected visu...”
  36. [Muzzu2021] paper:paper-dbc169750608 “[Furutachi2024] argue that unexpected visual stimuli boost the L2/3 V1 neurons most selective for that stimulus, supported by a pulvinar-VIP-SOM disinhibitory motif — a dedicated, feature-selective prediction-error signal. [Muzzu2021] reach the opposite conclusion: visuomotor "mismatch" signals in mouse V1 can be reproduced by purely sensory perturbations and are explained by feature selectivity plus convergence of...”
  37. [Jordan2020] paper:paper-d0689814037c “[Furutachi2024] argue that unexpected visual stimuli boost the L2/3 V1 neurons most selective for that stimulus, supported by a pulvinar-VIP-SOM disinhibitory motif — a dedicated, feature-selective prediction-error signal. [Muzzu2021] reach the opposite conclusion: visuomotor "mismatch" signals in mouse V1 can be reproduced by purely sensory perturbations and are explained by feature selectivity plus convergence of...”
  38. [Audette2023] paper:paper-cf40e30198fe “The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. [Audette2023] identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while [Holey2024] showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....”
  39. [Holey2024] paper:paper-9fb9bbd0f0af “The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. [Audette2023] identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while [Holey2024] showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....”
  40. [Zhou2024] paper:paper-610ffac7d8b6 “The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. [Audette2023] identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while [Holey2024] showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....”
  41. [Raltschev2025] paper:paper-fc7890a3b076 “The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. [Audette2023] identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while [Holey2024] showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....”
  42. [Shymkiv2025] paper:paper-2e49080d67c3 “The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. [Audette2023] identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while [Holey2024] showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....”
  43. [Ding2026c] paper:paper-dddd25cc4361 “The same predictive-coding logic has been tested in mouse auditory cortex with comparable partial success. [Audette2023] identified specific prediction-error signals in mouse auditory cortex and argued that errors may be computed early in sensory processing, while [Holey2024] showed that when the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off....”

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