Abstract
Animals adapt to a constantly changing world by predicting their environment and the consequences of their actions. The predictive coding hypothesis proposes that the brain generates predictions and continuously compares them with sensory inputs to guide behavior. However, how the brain reconciles conflicting top-down predictions and bottom-up sensory information remains unclear. To address this question, we simultaneously imaged neuronal populations in the mouse somatosensory barrel cortex and posterior parietal cortex during an auditory-cued texture discrimination task. In mice that had learned the task with fixed tone–texture matching, the presentation of mismatched pairing induced conflicts between tone-based texture predictions and actual texture inputs. When decisions were based on the predicted rather than the actual texture, top-down information flow was dominant and texture representations in both areas were modified, whereas dominant bottom-up information flow led to correct representations and behavioral choice. Our findings provide evidence for hierarchical predictive coding in the mouse neocortex.
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Data availability
A subset of the data is available at a Zenodo repository60 due to space limitations. The full dataset is available from the corresponding authors upon request. Source data are provided with this paper.
Code availability
Example data processing and analysis code is available at a Zenodo repository60.
References
Keller, G. B. & Mrsic-Flogel, T. D. Predictive processing: a canonical cortical computation. Neuron 100, 424–435 (2018).
Norman, K. J. et al. Post-error recruitment of frontal sensory cortical projections promotes attention in mice. Neuron 109, 1202–1213 (2021).
Pakan, J. M. P., Currie, S. P., Fischer, L. & Rochefort, N. L. The impact of visual cues, reward, and motor feedback on the representation of behaviorally relevant spatial locations in primary visual cortex. Cell Rep. 24, 2521–2528 (2018).
Schultz, W. & Dickinson, A. Neuronal coding of prediction errors. Annu. Rev. Neurosci. 23, 473–500 (2000).
Starkweather, C. K., Gershman, S. J. & Uchida, N. The medial prefrontal cortex shapes dopamine reward prediction errors under state uncertainty. Neuron 98, 616–629 (2018).
Garner, A. R. & Keller, G. B. A cortical circuit for audio-visual predictions. Nat. Neurosci. 25, 98–105 (2021).
Fiser, A. et al. Experience-dependent spatial expectations in mouse visual cortex. Nat. Neurosci. 19, 1658–1664 (2016).
Samuelsen, C. L., Gardner, M. P. H. & Fontanini, A. Effects of cue-triggered expectation on cortical processing of taste. Neuron 74, 410–422 (2012).
Zhang, S. et al. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660–665 (2014).
Libby, A. & Buschman, T. J. Rotational dynamics reduce interference between sensory and memory representations. Nat. Neurosci. 24, 715–726 (2021).
Schmack, K., Bosc, M., Ott, T., Sturgill, J. F. & Kepecs, A. Striatal dopamine mediates hallucination-like perception in mice. Science 372, eabf4740 (2021).
Corlett, P. R. et al. Hallucinations and strong priors. Trends Cogn. Sci. 23, 114–127 (2019).
Gilday, O. D. & Mizrahi, A. Learning-induced odor modulation of neuronal activity in auditory cortex. J. Neurosci. 43, 1375–1386 (2023).
Kwon, S. E., Yang, H., Minamisawa, G. & O’Connor, D. H. Sensory and decision-related activity propagate in a cortical feedback loop during touch perception. Nat. Neurosci. 19, 1243–1249 (2016).
Semedo, J. D., Zandvakili, A., Machens, C. K., Yu, B. M. & Kohn, A. Cortical areas interact through a communication subspace. Neuron 102, 249–259 (2019).
Semedo, J. D. et al. Feedforward and feedback interactions between visual cortical areas use different population activity patterns. Nat. Commun. 13, 1099 (2022).
Ebrahimi, S. et al. Emergent reliability in sensory cortical coding and inter-area communication. Nature 605, 713–721 (2022).
Javadzadeh, M. & Hofer, S. B. Dynamic causal communication channels between neocortical areas. Neuron 110, 2470–2483 (2022).
Veuthey, T. L., Derosier, K., Kondapavulur, S. & Ganguly, K. Single-trial cross-area neural population dynamics during long-term skill learning. Nat. Commun. 11, 4057 (2020).
Campo, A. T. et al. Feed-forward information and zero-lag synchronization in the sensory thalamocortical circuit are modulated during stimulus perception. Proc. Natl Acad. Sci. USA 116, 7513–7522 (2019).
Chen, J. L. et al. Pathway-specific reorganization of projection neurons in somatosensory cortex during learning. Nat. Neurosci. 18, 1101–1108 (2015).
Lyamzin, D. & Benucci, A. The mouse posterior parietal cortex: anatomy and functions. Neurosci. Res. 140, 14–22 (2019).
Song, Y. H. et al. A neural circuit for auditory dominance over visual perception. Neuron 93, 940–954 (2017).
Mohan, H. et al. Sensory representation of an auditory cued tactile stimulus in the posterior parietal cortex of the mouse. Sci. Rep. 8, 7739 (2018).
Raposo, D., Kaufman, M. T. & Churchland, A. K. A category-free neural population supports evolving demands during decision-making. Nat. Neurosci. 17, 1784–1792 (2014).
Gallero-Salas, Y. et al. Sensory and behavioral components of neocortical signal flow in discrimination tasks with short-term memory. Neuron 109, 1–14 (2020).
Gilad, A. & Helmchen, F. Spatiotemporal refinement of signal flow through association cortex during learning. Nat. Commun. 11, 1744 (2020).
Gilad, A., Gallero-Salas, Y., Groos, D. & Helmchen, F. Behavioral strategy determines frontal or posterior location of short-term memory in neocortex. Neuron 99, 814–828 (2018).
Pho, G. N., Goard, M. J., Woodson, J., Crawford, B. & Sur, M. Task-dependent representations of stimulus and choice in mouse parietal cortex. Nat. Commun. 9, 2596 (2018).
Rindner, D. J., Proddutur, A. & Lur, G. Cell-type-specific integration of feedforward and feedback synaptic inputs in the posterior parietal cortex. Neuron 110, 3760–3773 (2022).
Park, I. M., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nat. Neurosci. 17, 1395–1403 (2014).
Chen, J. L., Voigt, F. F., Javadzadeh, M., Krueppel, R. & Helmchen, F. Long-range population dynamics of anatomically defined neocortical networks. eLife 5, e14679 (2016).
Zhuang, J. et al. An extended retinotopic map of mouse cortex. eLife 6, e18372 (2017).
Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv https://doi.org/10.1101/061507 (2017).
Helmer, M. et al. On stability of canonical correlation analysis and partial least squares with application to brain-behavior associations. Preprint at bioRxiv https://doi.org/10.1101/2020.08.25.265546 (2023).
Audette, N. J., Zhou, W., La Chioma, A. & Schneider, D. M. Precise movement-based predictions in the mouse auditory cortex. Curr. Biol. 32, 4925–4940 (2022).
Hamm, J. P., Shymkiv, Y., Han, S., Yang, W. & Yuste, R. Cortical ensembles selective for context. Proc. Natl Acad. Sci. USA 118, e2026179118 (2021).
Parras, G. G. et al. Neurons along the auditory pathway exhibit a hierarchical organization of prediction error. Nat. Commun. 8, 2148 (2017).
Hyman, J. M., Holroyd, C. B. & Seamans, J. K. A novel neural prediction error found in anterior cingulate cortex ensembles. Neuron 95, 447–456 (2017).
Rodgers, C. C. & DeWeese, M. R. Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents. Neuron 82, 1157–1170 (2014).
Srinath, R., Ruff, D. A. & Cohen, M. R. Attention improves information flow between neuronal populations without changing the communication subspace. Curr. Biol. 31, 5299–5313 (2021).
Olcese, U., Iurilli, G. & Medini, P. Cellular and synaptic architecture of multisensory integration in the mouse neocortex. Neuron 79, 579–593 (2013).
Hamm, J. P. & Yuste, R. Somatostatin interneurons control a key component of mismatch negativity in mouse visual cortex. Cell Rep. 16, 597–604 (2016).
Schulz, A., Miehl, C., Berry, M. J. & Gjorgjieva, J. The generation of cortical novelty responses through inhibitory plasticity. eLife 10, e65309 (2021).
Harris, J. A. et al. Hierarchical organization of cortical and thalamic connectivity. Nature 575, 195–202 (2019).
Deneux, T. et al. Context-dependent signaling of coincident auditory and visual events in primary visual cortex. eLife 8, e44006 (2019).
Van Derveer, A. B., Ross, J. M. & Hamm, J. P. Robust multisensory deviance detection in the mouse parietal associative area. Curr. Biol. 33, 3969–3976 (2023).
Gilissen, S. R. J., Farrow, K., Bonin, V. & Arckens, L. Reconsidering the border between the visual and posterior parietal cortex of mice. Cereb. Cortex 31, 1675–1692 (2021).
Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).
Goard, M. J., Pho, G. N., Woodson, J. & Sur, M. Distinct roles of visual, parietal, and frontal motor cortices in memory-guided sensorimotor decisions. eLife 5, e13764 (2016).
Zhong, L. et al. Causal contributions of parietal cortex to perceptual decision-making during stimulus categorization. Nat. Neurosci. 22, 963–973 (2019).
Oude Lohuis, M. N., Marchesi, P., Pennartz, C. M. A. & Olcese, U. Functional (ir)relevance of posterior parietal cortex during audiovisual change detection. J. Neurosci. 42, 5229–5245 (2022).
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N. & Harvey, C. D. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 170, 986–999 (2017).
Morcos, A. S. & Harvey, C. D. History-dependent variability in population dynamics during evidence accumulation in cortex. Nat. Neurosci. 19, 1672–1681 (2016).
Harvey, C. D., Coen, P. & Tank, D. W. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012).
Kira, S., Safaai, H., Morcos, A. S., Panzeri, S. & Harvey, C. D. A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions. Nat. Commun. 14, 2121 (2023).
Han, S., Yang, W. & Yuste, R. Two-color volumetric imaging of neuronal activity of cortical columns. Cell Rep. 27, 2229–2240 (2019).
Buetfering, C. et al. Behaviorally relevant decision coding in primary somatosensory cortex neurons. Nat. Neurosci. 25, 1225–1236 (2022).
Chen, J. L., Carta, S., Soldado-Magraner, J., Schneider, B. L. & Helmchen, F. Behaviour-dependent recruitment of long-range projection neurons in somatosensory cortex. Nature 499, 336–340 (2013).
Han, S. Behavior-relevant top-down cross-modal predictions in mouse neocortex. Zenodo https://doi.org/10.5281/zenodo.10078711 (2023).
Acknowledgements
We thank P. Bethge for managing transgenic mouse lines, F. Voigt and H. Kasper for help with optics, and M. Wieckhorst for the behavior training software. We also thank C. Lewis, J. Chen and J. Hamm for their feedback on the paper. This work was supported by a Sinergia grant from the Swiss National Science Foundation (CRSII5_180316 to F.H.) and a Forschungskredit grant from University of Zurich (K-41220-07-01 to S.H.). F.H. received funding from the University Research Priority Program (URPP) ‘Adaptive Brain Circuits in Development and Learning’ (AdaBD).
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S.H. performed the experiments and analyzed the data. S.H. and F.H. conceived the study, designed the experiments and wrote the paper.
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Extended data
Extended Data Fig. 1 Additional behavior analysis.
(a) Each day, all the trials were split into 2–5 subsessions of 100–150 trials. Three unstable performer mice that did not reach expert threshold in daily average all showed expert performance in individual subsessions. (b) Lick rate over time for naïve and expert mice. (c) Response time of naive (performance<55%, 16 mice, 80 sessions) and expert mice (performance>75%, 13 mice, 74 sessions). (d) Percentage of trials with lick during tone on the lick port of final choice for naïve and expert mice. (e) Lick rate during texture for naïve and expert mice. (f) Lick probability over trial time on the lick ports according to texture identity (left) or the opposite lick port (right), during sessions where mismatched trials were rewarded according to texture (same for g–i). (g) Response time for different trial types. Mismatch-choose-tone condition shows a shorter response time overall. (h) Percentage of trials in each session, in which licks on the final choice spout were recorded during tone presentation. (i) Lick rate during texture presentation for different trial types. (j) Z-scored pupil diameter in different trial types. (k) Body movement (normalized between 0 and 1 within each day) across trial types. (l) Face movement (normalized between 0 and 1 within each day) across trial types. (m) Statistics of pupil diameter across trial time. ((b–e): Wilcoxon rank-sum test, (f–m): Wilcoxon signed-rank test; (a–e, j–m) mice and session numbers are the same as Fig. 1; (f–i) 6 mice, 17 sessions; *P < 0.05; **P < 0.01; ***P < 0.001; Supplementary Table 1).
Extended Data Fig. 2 Additional information on imaging and task-tuned neurons.
(a) The locations of S1, PPC-A and PPC-RL were determined by widefield sensory mapping using whisker, visual and hindlimb stimulation (top) under light anesthesia, as well as visual field sign mapping (bottom). (b) Example ΔF/F (black) and deconvolved spike rate (red) of two simultaneously imaged S1 and PPC-A populations. Due to space limitation, only 50 neurons are shown for each area. Colored stripes in the background indicate task windows. Some neurons were silent in the example time period shown in the plot. (c) Number of imaged neurons for each area are not significantly different. (d) Example single trial activities of texture discriminative neurons. Three example neurons from the same imaging session are shown for each texture preference; choice window was resampled to be the 0.5-s window before the reward window. Trial structure and color code are the same as in (b). (e) Distribution of PPC task-responsive neurons along the medial-lateral (M-L) axis. Task-responsive neurons in PPC-RL and PPC-A were assigned to 3 spatial bins along the M-L axis. Percentage was calculated using the number of total neurons in each area. Shuffled data are represented in gray, where the neurons that the match number of responsive neurons were randomly drawn from the population. (S1: 14 mice, 118 sessions; PPC-RL: 14 mice, 78 sessions; PPC-A: 9 mice, 40 sessions; Wilcoxon signed-rank test; *P < 0.05; **P < 0.01; ***P < 0.001; Supplementary Table 1).
Extended Data Fig. 3 Response amplitude of tone-discriminative neurons.
(a) Averaged normalized spike rate of tone 1 discriminative neurons in S1, PPC-RL and PPC-A, in matched and mismatched trials. The spike rate of each neuron was normalized to be between 0 and 1 within each session. (b) The mean response amplitudes of all tone discriminative neurons in the tone window, across areas and trial types. (c) Selectivity index of tone discriminative neurons during tone window, in different trial types. Significance level was determined from shuffled data where the trial labels were shuffled. (S1: 119 neurons; PPC-A: 170 neurons; PPC-RL: 94 neurons; Wilcoxon rank-sum test; *P < 0.05; **P < 0.01; ***P < 0.001; Supplementary Table 1).
Extended Data Fig. 4 Response amplitude of texture-discriminative neurons without the influence of choice.
(a) The mean texture response amplitudes of all texture discriminative neurons that were not choice-responsive, across areas and trial types. Each dot represents the response of one neuron in one imaging session. (b) Selectivity index of texture discriminative neurons during texture window, in different trial types. Selectivity index was calculated as the difference between the average response to the preferred texture and the average response to the nonpreferred texture. (c) The mean texture response amplitudes of all texture discriminative neurons, in trials where mice did not lick during the texture window (no early licks). (d) Selectivity index of neurons in (c). (a,b: S1: 1213 neurons; PPC-A: 239neurons; PPC-RL: 481 neurons; c,d: S1 1486 neurons; PPC-A 400 neurons; PPC-RL 618 neurons; Wilcoxon rank-sum test; ***P < 0.001; Supplementary Table 1).
Extended Data Fig. 5 Response amplitude of texture-discriminative neurons identified by GLM model.
(a) Scheme of GLM regression. Two discrete regressors, texture and choice, were used to model the average activity during choice window, for each neuron. Regression performance was measured by the correlation value of predicted activity with real activity. Task responsive neurons were identified by comparing these correlation values against models generated by shuffling one regressor at a time. (b) Percentage of texture- and choice-responsive neurons identified by GLM from texture window. (c) Percentage of texture- and choice-discriminative neurons identified by GLM from texture window. (d) Percentage of task responsive neurons in Fig. 2d that are also identified by GLM method. (e) Percentage of task discriminative neurons in Fig. 2e that are also identified by GLM method. (f) The mean texture response amplitudes of all GLM-identified texture discriminative neurons during texture window. Each dot represents the response of one neuron in one imaging session. (g) Selectivity index of texture discriminative neurons in (f). (S1: 1478 neurons; PPC-A: 458 neurons; PPC-RL: 910 neurons; Wilcoxon rank-sum test; ***P < 0.001; Supplementary Table 1).
Extended Data Fig. 6 Choice and reward encoding in S1 and PPC.
(a) Neuronal population encoding of choice (top panels) and reward (bottom panels). Line colors indicate area identity; solid and dash lines indicate texture identity of the trial. (b) Discrimination index (DI) of the choice decoder and reward decoder, in the tone window and texture window, separately. Asterisks above each box indicate the significance with shuffled data (gray bars) where neuron identities were shuffled, while trial and time correspondence were kept the same. Asterisks across boxes indicate comparison between trial types. (c) DI of texture decoder before texture onset (last 0.3 s of tone window, top panel) and after texture onset (first 0.3 s of texture window, bottom panel). (d) Neuronal population encoding strength of choice (top panels) and reward (bottom panels) in single modality experiments. (e) DI of choice decoder and reward decoder in single modality experiments, in tone window and texture window, separately. Asterisks are represented as in (b). (f) Texture decoder axis similarity (projection axis correlation) from decoders trained with each trial type, separately. For each trial type, trials were randomly split into two subsets, and two separate decoders were trained. The averaged axis was used for decoder similarity between trial types, and cross folds represent the similarity between two independent decoders trained with the same trial type. Sessions with less than 30 trials for each trial type were excluded to ensure enough training samples. (S1: 14 mice, 118 sessions; PPC-RL: 14 mice, 78 sessions; PPC-A: 9 mice, 40 sessions; *P < 0.05; **P < 0.01; ***P < 0.001; Supplementary Table 1).
Extended Data Fig. 7 Interaction between S1 and PPC in single modality trials.
(a) Lagged canonical correlation between S1 and PPC-A averaged across all sessions, for matched stimuli (tone + texture), tone only, and texture only conditions. Note the stronger top-down (A to S1) interaction in tone-only condition and stronger bottom-up (S1 to A) interaction in texture only condition. (b) Information flow index computed from (a). (c) Quantification of information flow index between S1 and PPC-A. (d) Averaged lagged canonical correlation between S1 and PPC-RL. Note the slightly stronger top-down (RL to S1) interaction during tone window in tone only condition. (e) Information flow index computed from (d). (f) Quantification of information flow index between S1 and PPC-RL (S1↔PPC-A: 9 mice, 40 sessions; S1↔PPC-RL: 13 mice, 71 sessions; Wilcoxon rank-sum test; *P < 0.05; **P < 0.01; Supplementary Table 1).
Extended Data Fig. 8 Top-down and bottom-up interaction analysis with Pearson correlation.
(a) Top-down and bottom-up interaction strength were evaluated in the same way as Fig. 6a, except that the correlation was calculated using Pearson correlation instead of CCA. (b) Population correlation between S1 and PPC-A (green), and S1 and PPC-RL (blue). (c) Average canonical correlation strength of (b) in each task window. (d) Information flow index (IFI) of S1–PPC-A interaction using Pearson correlation. (e) Quantification of (d). (f) Information flow index of S1–PPC-RL interaction using Pearson correlation. (g) Quantification of (f). (h) Information flow index of S1–PPC-A interaction in single modality conditions. (i) Quantification of (h). (j) Information flow index of S1–PPC-RL interaction in single modality conditions. (k) Quantification of (j). (S1↔PPC-A: 9 mice, 40 sessions; S1↔PPC-RL: 13 mice, 71 sessions; Wilcoxon signed-rank test; *P < 0.05; Supplementary Table 1).
Extended Data Fig. 9 Trial-to-trial population encoding strength analysis of S1 and PPC-A.
(a) Trial-to-trial PPC-A texture encoding strength (averaged texture decoder projection strength in texture window) vs. S1 texture encoding strength, in an example session. The projection strength is averaged within the corresponding task window and z-scored within each session. Response coefficient is calculated using the center of mass of all data points (y value divided by x value). (b) PPC-A vs. S1 texture response coefficient of all sessions, using absolute values. (c) Trial-to-trial PPC-A tone encoding strength (averaged tone decoder projection strength in tone window) vs. S1 texture encoding strength (texture decoder projection strength in texture window), in an example session. (d) PPC-A tone vs. S1 texture response coefficient of all sessions. (e) Trial-to-trial PPC-A texture encoding strength vs. tone encoding strength, in an example session. (f) PPC-A texture vs. tone response coefficient of all sessions. (S1: 14 mice, 118 sessions; PPC-RL: 14 mice, 78 sessions; PPC-A: 9 mice, 40 sessions; Wilcoxon signed-rank test; *P < 0.05; ***P < 0.001; n.s. nonsignificant; Supplementary Table 1).
Extended Data Fig. 10 Texture-preferring sessions and tone-preferring sessions.
(a) Rate of choosing texture in mismatch trials, in 10 highest sessions (purple) and 10 lowest sessions (yellow). The 10 highest sessions where mice were prone to choosing texture in mismatch trials were defined as texture-preferring sessions. The 10 lowest sessions were defined as tone-preferring sessions. Color code remains the same for the rest of this figure. (b) Lick rate on the correct lick port. (c) Response time. (d) Percentage of trial with lick during tone. (e) Lick rate during texture. (f) Percentage of tone responsive neurons (left) and tone discriminative neurons (right) in these sessions. (g) Percentage of texture responsive neurons (left) and texture discriminative neurons (right) in these sessions. (h) Neuronal population encoding strength of tone (top panels) and texture (bottom panels) in correct trials. Solid and dash lines indicate texture identity of the trial. (i) Tone decoder discrimination index (DI) in tone window. (j) Tone decoder DI in the first 0.3 s of texture window. (k) Texture decoder AUC in the last 0.3 s of tone window. (l) Texture decoder DI in the first 0.3 s of texture window. (m) Information flow index of S1–PPC-A interaction in tone window (left) and texture window (right), during correct trials. (n) Information flow index of S1–PPC-RL interaction in the last 0.3 s of tone window (left) and the first 0.3 s of texture window (right), during correct trials. (10 sessions for each condition; 3 mice for texture-preferring sessions, 4 mice for tone-preferring sessions; Wilcoxon rank-sum test; *P < 0.05; **P < 0.01; ***P < 0.001; Supplementary Table 1).
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Han, S., Helmchen, F. Behavior-relevant top-down cross-modal predictions in mouse neocortex. Nat Neurosci 27, 298–308 (2024). https://doi.org/10.1038/s41593-023-01534-x
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DOI: https://doi.org/10.1038/s41593-023-01534-x
