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Correlations enhance the behavioral readout of neural population activity in association cortex

Nature Neuroscience volume 24, pages 975–986 (2021)Cite this article

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Abstract

Noise correlations (that is, trial-to-trial covariations in neural activity for a given stimulus) limit the stimulus information encoded by neural populations, leading to the widely held prediction that they impair perceptual discrimination behaviors. However, this prediction neglects the effects of correlations on information readout. We studied how correlations affect both encoding and readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations reduced the encoded stimulus information, but, seemingly paradoxically, were higher when mice made correct rather than incorrect choices. Single-trial behavioral choices depended not only on the stimulus information encoded by the whole population, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, they enhanced the conversion of sensory information into behavioral choices, overcoming their detrimental information-limiting effects. Thus, correlations in association cortex can benefit task performance even if they decrease sensory information.

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Fig. 1: Response properties and across-time and across-neuron correlations in mouse PPC during perceptual discrimination tasks.
Fig. 2: A simple encoding-readout model shows how different readouts determine the impact of correlations on task performance.
Fig. 3: Exploration of the parameter space of the encoding-readout model.
Fig. 4: Across-time and across-neuron correlations in PPC activity influence mouse choices.
Fig. 5: Simulated mouse choices show that the best-fit enhanced-by-consistency readout improves task performance in the presence of information-limiting correlations in the PPC.
Fig. 6: A biophysical model for the enhanced-by-consistency readout model.

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Data availability

The sound localization task data that support the findings of the current study can be downloaded at https://gin.g-node.org/MMoroni/PPC_AC_2p_sound_localization/ (ref. 44).

The evidence accumulation task data that support the findings of the current study can be downloaded at https://gin.g-node.org/MMoroni/PPC_2p_evidence_accumulation/ (ref. 43).

Code availability

The code for the biophysical information transmission model (Fig. 6) is available for download at https://github.com/gbondanelli/BiophysicalReadout/.

The code for the encoding and readout model (Figs. 2 and 3) is available for download at https://github.com/moni90/encoding_readout_model/.

The code for data analysis is available from the corresponding authors upon reasonable request.

References

  1. Ni, A. M., Ruff, D. A., Alberts, J. J., Symmonds, J. & Cohen, M. R. Learning and attention reveal a general relationship between population activity and behavior. Science 359, 463–465 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Kohn, A., Coen-Cagli, R., Kanitscheider, I. & Pouget, A. Correlations and neuronal population information. Annu Rev. Neurosci. 39, 237–256 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Panzeri, S., Harvey, C. D., Piasini, E., Latham, P. E. & Fellin, T. Cracking the neural code for sensory perception by combining statistics, intervention and behavior. Neuron 93, 491–507 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gawne, T. J. & Richmond, B. J. How independent are the messages carried by adjacent inferior temporal cortical neurons? J. Neurosci. 13, 2758–2771 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Averbeck, B. B., Latham, P. E. & Pouget, A. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7, 358–366 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Zohary, E., Shadlen, M. N. & Newsome, W. T. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140–143 (1994).

    Article  CAS  PubMed  Google Scholar 

  7. Moreno-Bote, R. et al. Information-limiting correlations. Nat. Neurosci. 17, 1410–1417 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bartolo, R., Saunders, R. C., Mitz, A. R. & Averbeck, B. B. Information-limiting correlations in large neural populations. J. Neurosci. 40, 1668–1678 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Rumyantsev, O. I. et al. Fundamental bounds on the fidelity of sensory cortical coding. Nature 580, 100–105 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Gold, J. I. & Shadlen, M. N. Neural computations that underlie decisions about sensory stimuli. Trends Cogn. Sci. 5, 10–16 (2001).

    Article  PubMed  Google Scholar 

  11. Zariwala, H. A., Kepecs, A., Uchida, N., Hirokawa, J. & Mainen, Z. F. The limits of deliberation in a perceptual decision task. Neuron 78, 339–351 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mazurek, M. E. & Shadlen, M. N. Limits to the temporal fidelity of cortical spike rate signals. Nat. Neurosci. 5, 463–471 (2002).

    Article  CAS  PubMed  Google Scholar 

  13. Diesmann, M., Gewaltig, M. O. & Aertsen, A. Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529–533 (1999).

    Article  CAS  PubMed  Google Scholar 

  14. Zandvakili, A. & Kohn, A. Coordinated neuronal activity enhances corticocortical communication. Neuron 87, 827–839 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Alonso, J. M., Usrey, W. M. & Reid, R. C. Precisely correlated firing in cells of the lateral geniculate nucleus. Nature 383, 815–819 (1996).

    Article  CAS  PubMed  Google Scholar 

  16. Salinas, E. & Sejnowski, T. J. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539–550 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zylberberg, J., Pouget, A., Latham, P. E. & Shea-Brown, E. Robust information propagation through noisy neural circuits. PLoS Comput. Biol. 13, e1005497 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Runyan, C. A., Piasini, E., Panzeri, S. & Harvey, C. D. Distinct timescales of population coding across cortex. Nature 548, 92–96 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Morcos, A. S. & Harvey, C. D. History-dependent variability in population dynamics during evidence accumulation in cortex. Nat. Neurosci. 19, 1672–1681 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Panzeri, S., Schultz, S. R., Treves, A. & Rolls, E. T. Correlations and the encoding of information in the nervous system. Proc. Biol. Sci. 266, 1001–1012 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Averbeck, B. B. & Lee, D. Effects of noise correlations on information encoding and decoding. J. Neurophysiol. 95, 3633–3644 (2006).

    Article  PubMed  Google Scholar 

  26. Nogueira, R. et al. The effects of population tuning and trial-by-trial variability on information encoding and behavior. J. Neurosci. 40, 1066–1083 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Romo, R., Hernandez, A., Zainos, A. & Salinas, E. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38, 649–657 (2003).

    Article  CAS  PubMed  Google Scholar 

  28. Reich, D. S., Mechler, F. & Victor, J. D. Independent and redundant information in nearby cortical neurons. Science 294, 2566–2568 (2001).

    Article  CAS  PubMed  Google Scholar 

  29. Koch, C., Rapp, M. & Segev, I. A brief history of time (constants). Cereb. Cortex 6, 93–101 (1996).

    Article  CAS  PubMed  Google Scholar 

  30. Reyes, A. D. Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro. Nat. Neurosci. 6, 593–599 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Shahidi, N., Andrei, A. R., Hu, M. & Dragoi, V. High-order coordination of cortical spiking activity modulates perceptual accuracy. Nat. Neurosci. 22, 1148–1158 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Histed, M. H. & Maunsell, J. H. Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony. Proc. Natl Acad. Sci. USA 111, E178–E187 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Emiliani, V., Cohen, A. E., Deisseroth, K. & Hausser, M. All-optical interrogation of neural circuits. J. Neurosci. 35, 13917–13926 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Shadlen, M. N. & Newsome, W. T. The variable discharge of cortical neurons: implications for connectivity, computation and information coding. J. Neurosci. 18, 3870–3896 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ostojic, S., Brunel, N. & Hakim, V. How connectivity, background activity and synaptic properties shape the cross-correlation between spike trains. J. Neurosci. 29, 10234–10253 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rosenbaum, R., Smith, M. A., Kohn, A., Rubin, J. E. & Doiron, B. The spatial structure of correlated neuronal variability. Nat. Neurosci. 20, 107–114 (2017).

    Article  CAS  PubMed  Google Scholar 

  37. de la Rocha, J., Doiron, B., Shea-Brown, E., Josic, K. & Reyes, A. Correlation between neural spike trains increases with firing rate. Nature 448, 802–806 (2007).

    Article  PubMed  Google Scholar 

  38. Cossell, L. et al. Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518, 399–403 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Marshel, J. H. et al. Cortical layer-specific critical dynamics triggering perception. Science 365, eaaw5202 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Pitkow, X., Liu, S., Angelaki, D. E., DeAngelis, G. C. & Pouget, A. How can single sensory neurons predict behavior? Neuron 87, 411–423 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nirenberg, S., Carcieri, S. M., Jacobs, A. L. & Latham, P. E. Retinal ganglion cells act largely as independent encoders. Nature 411, 698–701 (2001).

    Article  CAS  PubMed  Google Scholar 

  42. Karpas, E.D., Maoz, O., Kiani, R. & Schneidman, E. Strongly correlated spatiotemporal encoding and simple decoding in the prefrontal cortex. Preprint at bioRxiv https://doi.org/10.1101/693192 (2019).

  43. Morcos, A. S. et al. Dataset of ‘History-dependent variability in population dynamics during evidence accumulation in cortex’. G-Node https://doi.gin.g-node.org/10.12751/g-node.g1xyem/ (2021).

  44. Runyan, C. A. et al. Dataset of ‘Distinct timescales of population coding across cortex’. G-Node https://doi.org/10.12751/g-node.tqbad8 (2021)

  45. Aronov, D. & Tank, D. W. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system. Neuron 84, 442–456 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Greenberg, D. S. & Kerr, J. N. Automated correction of fast motion artifacts for two-photon imaging of awake animals. J. Neurosci. Methods 176, 1–15 (2009).

    Article  PubMed  Google Scholar 

  47. Vogelstein, J. T. et al. Fast nonnegative deconvolution for spike train inference from population calcium imaging. J. Neurophysiol. 104, 3691–3704 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Boser, B. E., Guyon, I. M. & Vapnik, V. N. A training algorithm for optimal margin classifiers. in Fifth Annual Workshop on Computational Learning Theory (ed. Haussler, D.) 144–152 (ACM Press, 1992).

  49. Chang, C. C. L. & Merz, C. J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).

    Article  Google Scholar 

  50. Lin, H.-T. L., Merz, C.-J. & Weng, R. C. A note on Platt’s probabilistic outputs for support vector machines. Mach. Learn. 68, 267–276 (2007).

    Article  Google Scholar 

  51. Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S. & Movshon, J. A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996).

    Article  CAS  PubMed  Google Scholar 

  52. Kang, I. & Maunsell, J. H. Potential confounds in estimating trial-to-trial correlations between neuronal response and behavior using choice probabilities. J. Neurophysiol. 108, 3403–3415 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Seabold, S. P. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. in Proceedings of the 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) 92–96 (SciPy, 2010).

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Acknowledgements

We thank E. Piasini for early contributions; members of our laboratories for helpful discussions; G. Iurilli, C. Kayser, E. Piasini, C. Becchio and J. Drugowitsch for feedback; and M. Libera for technical support. This work was supported by National Institute of Health grants from the NIMH BRAINS program R01 MH107620 (to C.D.H.), NINDS R01 NS089521 (to C.D.H.), the BRAIN Initiative R01 NS108410 (to C.D.H. and S.P.) and U19 NS107464 (to S.P.) and the Fondation Bertarelli (S.P).

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Author notes

  1. These authors contributed equally: Martina Valente, Giuseppe Pica, Giulio Bondanelli, Monica Moroni.

Authors and Affiliations

  1. Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy

    Martina Valente, Giuseppe Pica, Giulio Bondanelli, Monica Moroni & Stefano Panzeri

  2. Centro Interdisciplinare Mente e Cervello (CIMeC), University of Trento, Rovereto, Italy

    Martina Valente

  3. Department of Neurobiology, Harvard Medical School, Boston, MA, USA

    Caroline A. Runyan, Ari S. Morcos & Christopher D. Harvey

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Contributions

S.P. and C.D.H. conceived, designed and supervised the study. C.A.R. and A.S.M. acquired the experimental data. M.V., G.P., G.B. and M.M. performed computations. S.P., C.D.H., M.V., G.P., G.B. and M.M. wrote the paper, with feedback from all authors.

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Correspondence to Christopher D. Harvey or Stefano Panzeri.

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Peer review information Nature Neuroscience thanks Yong Gu, Joel Zylberberg, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Response properties and across-time and across-neuron correlations in PPC during perceptual discrimination tasks for different trials categories.

Panels a-l refer to PPC data during the sound localization task and across-time correlations. a-c, Accuracy of a linear decoder of the stimulus applied to the joint population activity at two different time points, for recorded (black) or trial-shuffled (gray) population vectors, for ‘easy’ trials with high level of sensory evidence (a, sound locations further from the midline than 45 deg), ‘difficult’ trials with low level of sensory evidence (b, sound locations closer to the midline than 45 deg) and behaviorally correct trials only (c). Errorbars report mean ± SEM across n = 6 sessions and all time point pairs within the specified lag range. For all comparisons, P = 10−4, two-sided permutation test. d-f, Distribution of the signal-noise angle γ (over n = 6 sessions and all time point pairs within a 2 s lag), for ‘easy’ trials (d), ‘difficult’ trials (e) and behaviorally correct trials only (f). Boxplots show the median (line), quartiles (box) and whiskers extend to ±1.5*interquartile range. Red dotted line: theoretical value of the critical angle γC between the information-limiting and information-enhancing regime. g-h, Pairwise noise correlations in time-lagged activity, for correct and error trials, for ‘easy’ (g) and ‘difficult’ (h) trials. Errorbars report mean ± SEM across n = 6 sessions, all time point pairs within the specified lag range and all cell pairs. For all comparisons, P = 10−4, two-sided permutation test. i-j, Population-wise noise correlations in time-lagged activity, for correct and error trials, for ‘easy’ (i) and ‘difficult’ (j) trials. Errorbars report mean ± SEM across n = 6 sessions and all time point pairs within the specified lag. In i, P = 0.0380 for Lag 0-1 s, n.s. P = 0.1510 for Lag 1-2 s, two-sided permutation test. In j, P = 0.0480 for Lag 0-1 s, P = 0.001 for Lag 1-2 s, two-sided permutation test. k, Relation between pairwise and population-wise noise correlations. Each dot represents the average across n = 6 session and all time points with a given lag. The black line indicates the linear fit. l, Accuracy of a linear, quadratic and radial basis function SVM decoder of stimulus identity applied to joint population activity at two different time points for real recorded population vectors. Errorbars report mean ± SEM across n = 6 sessions and all time point pairs within the specified lag range. Panels m-x refer to PPC data during the evidence accumulation task and across-neuron correlations. m-o, Same as in a-c. p-r, Same as in d-f. s-t, Same as in g-h. u-v, Same as in i-j. For the evidence accumulation task, ‘easy’ and ‘difficult’ trials were defined as trials with net evidence ≥4 or <4 respectively. In panels m-v errorbars report mean ± SEM across n = 11 sessions, Early and Late Delay epochs and 100 pairs of neuronal pools. In m-o, for all comparisons, P = 10−4. In s, P = 0.0120. In t, P = 9 × 10−4. In u, P = 0.001. In v, P = 0.8641. For all comparisons, two-sided permutation test. w, Same as in k. Each dot represents the average across n = 11 sessions for a given delay epoch. x, Same as in l, with errorbars reporting mean ± SEM across all n = 11 sessions, Early and Late Delay epochs, and 100 pairs of randomly split neuronal pools.

Extended Data Fig. 2 Parameter exploration of the two-pools encoding-readout model and comparison with PPC data.

a, Population-wise noise correlations ν as a function of the pairwise noise correlations ρ, for different values of the active neurons 2N (N neurons per pool). Here we assumed that all neurons were active (M = 2N). For ρ = 0, the population-wise correlation is equal to ν = 1/(2N) b, Population-wise noise correlations ν as a function of the average over all pairs of pairwise noise correlations ρ (where · denotes the average over neuron pairs), for different fraction of active neurons 2N/M (total number of neurons given by M = 2N + K). By decreasing the fraction of active neurons, the constant of proportionality between ν and ρ increases. c, Blue line: critical signal-noise angle γC below which correlations are information-limiting in the model, as a function of the number of neurons per pool N, computed using the experimental value of the PPC across-time population-wise correlation for the sound localization task. Red line: critical value γC,BP for the angle above which the task performance in correlated data is higher than that in shuffled data. The experimental distribution of PPC signal-noise angles is reported for comparison (n = 6 sessions and all time point pairs within a 2 s lag). Horizontal gray line indicates the median. Box edges indicate the first and third quartile. d, Same as c for the evidence accumulation task and across-neuron PPC correlations.

Extended Data Fig. 3 Extension of the encoding readout model to multiple features, multiple stimuli per category and multiple categories.

a, Left: schematic of the encoding readout models with two neural features, two categories, two stimuli per category. Each axis represents the activity of a single feature. Colored ellipses: 95% confidence intervals for the simulated neural responses to two different stimuli (s=1, s=-1). Dashed black line: stimulus axis. Gray shaded areas: regions of the response space in which stimulus information is encoded consistently across pools and the behavioral readout efficacy is enhanced. b, Difference in stimulus classification accuracy between correlated and shuffled responses computed using a linear decoder applied to the joint population activity across the two features, as a function of the signal-noise angle γ. c, Difference in task performance between correlated and shuffled responses predicted by an enhanced-by-consistency readout of simulated neural activity, as a function of the signal-noise angle γ. In panel b-c, the red dashed lines delimit the parameter range where correlations are information-limiting but task performance is enhanced for correlated data. Data are mean ± SEM over n = 10 simulations with 50,000 trials each, with \(d = \sqrt {0.02}\), ρ = 0.8, σ = 0.2, η = = 0.7. d-f, Same as in a-c, but for an encoding model with two neural features, two categories, and multiple (n = 2) stimuli per category. Within each category, stimulus-specific distributions are symmetrically displaced on either side of the between-category signal axis. Within each category, the noise axes of the individual distributions are aligned to each other and aligned to the vector of differences of mean activity. Data are mean ± SEM over n = 10 simulations with 50,000 trials each. We set half the distance between the centers of the distributions of the two categories to \(d = \sqrt {0.02}\), and the distance between the centers of the distributions of individual stimuli within each category to d2 = 03. In simulations, we set ρ = 0.8, σ = 0.13 (for the distributions of individual stimuli within each category), η = 0.7. g-i, Same as in a-c, but for an encoding readout model with two pools and multiple (n = 3) stimulus categories. Mean responses to the three stimulus categories are aligned along a unique signal axis, and the noise axes of individual distributions form an angle γ with the stimulus axis. Data are mean ± SEM over n = 10 simulations with 50,000 trials each, with \(d = \sqrt {0.02}\) (distance between the distributions across individual categories), ρ = 0.8, σ = 0.2 (for individual distributions), η = 0.7. j-l, Same as in a-c, for a model with three one-dimensional neural features. Neural activity is considered consistent if the same stimulus is decoded from all three features. Data are mean ± SEM over n = 10 simulations with 50,000 trials each, with \(d = \sqrt {0.02}\), ρ = 0.8, σ = 0.2, η = 0.7.

Extended Data Fig. 4 Exploration of the parameter space of the encoding readout model.

Same as in Fig. 3, for an encoding readout model with N = 10 neurons in each pool. a, Difference in the accuracy of a linear decoder of stimulus applied to correlated and shuffled simulated neural activity for different values of the signal-noise angle (γ) and population-wise correlations (ν). For all panels, black solid line: boundary between a regime with information-limiting correlations and information-enhancing correlations. b, The difference between correlated and shuffled activity in the fraction of trials in which the two neural features encode consistent stimulus information is higher in the information-limiting regime and increases with population-wise correlations strength. Panels c-e refer to the consistency-independent readout. c-d, Difference in average pairwise correlations (c) and population-wise correlations (d) between trials with correct and incorrect predicted task performance for different combinations of model parameters. e, Difference in task performance predicted by applying the consistency-independent readout to correlated and shuffled simulated neural activity for different combinations of model parameters. For panels c-i, dashed black line: boundary between a regime where task performance is higher for correlated responses and a regime where performance is higher for shuffled responses. The overlap between the continuous and dashed black line indicates that correlations that limits information are also detrimental for behavior. Panel f-h refer to the enhanced-by-consistency readout (consistency modulation index η = 0.85). f-g, Same as in c-d. With the enhanced-by-consistency readout correlations are higher in correct trials. h, Same as in e. The area between the dashed and the continuous black line indicates a regime where correlations are information-limiting but task performance is higher for correlated responses. Thus, in the parameter range between the two lines, the readout is able overcoming the negative impact of correlations. Dark and light gray dots and ellipses: mean values and range between the 25th and the 75th percentile of the signal-noise angles and population-wise correlations for PPC data from the sound localization task and evidence accumulation task, respectively. i, Difference in task performance predicted by applying the enhanced-by-consistency readout or the consistency-independent readout with matched readout efficacy for different combinations of model parameters. The enhanced-by-consistency readout yields increased task performance with respect to the consistency-independent readout. Panels represent the mean over n = 100 simulations with 300,000 trials each.

Extended Data Fig. 5 The effect of neural correlations on the mouse’s single trial choices cannot be explained by higher stimulus information associated to consistent neural representations.

a, Schematic example showing response distributions along two neural features (r1, r2) to two stimuli (s = −1: blue, s = 1: orange). Black dashed line: optimal decoding boundary of a linear decoder trained on the simulated neural responses. The background color represents the linear decoder posterior probability that stimulus s=1 has occurred given the observation of the neural response r = (r1, r2). Intuitively, the farther neural response r is from the decoding boundary, the farther p(s = 1|r) is from 0.5, and the more ‘informative’ r is about the stimulus. Note that, in the example shown, consistent trials have on average higher posterior probability than inconsistent trials, which might represent a confounder for the effect of consistency on mouse’s choices. To control for potential confounders due to differences in the levels of stimulus information between trials with consistent and inconsistent stimulus information, we fitted to the data a readout model that predicted choice using the posterior probability of the stimulus and posterior probabilities consistency given the neural responses, rather than just the decoded stimulus identity (b, f). We further repeated the analyses of Fig. 4 on trials partitioned into those with low (\(\left| {p\left( {s = 1|{\boldsymbol{r}}} \right) - 0.5} \right| < 0.16\)), medium (\(\left| {p\left( {s = 1|{\boldsymbol{r}}} \right) - 0.5} \right| > 0.16 \wedge \left| {p\left( {s = 1|{\boldsymbol{r}}} \right) - 0.5} \right| < 0.32\)), or high (\(\left| {p\left( {s = 1|{\boldsymbol{r}}} \right) - 0.5} \right| > 0.32\)) ‘stimulus information’ (c-e, g-i). Panels b-e refer to PPC data during the sound localization task. b, Performance (fraction of deviance explained) in explaining single-trial choice of models using neural predictors based on posterior probabilities. Full model includes all predictors values, comprising stimulus posterior probability and posterior probability consistency. No Cons model neglects neural consistency by shuffling consistency values across trials. c-e, Left (purple dots). Task performance in trials with correctly decoded stimulus is higher when information is encoded consistently than inconsistently. Right (orange dots). The opposite happens for trials with incorrectly decoded stimulus. Thus, stimulus information in neural activity has a larger impact on choices when it is encoded consistently across time, even when subsets of trials having approximately the same posterior are used. For all comparisons in b-e, P = 10−4, two-sided permutation test. Errorbars represent mean ± SEM across n = 6 sessions and all time point pairs within a 1 s lag. Panels f-i refer to PPC data during the evidence accumulation task. f, Same as in b. P = 6 × 10−4, two-sided permutation test g, Same as in c. h, Same as in d. i, Same as in j. In panels f-i, consistency and mouse choices are computed from the activity of two pools of neurons. For all comparisons in g-i, P = 10−4, two-sided permutation test. In f-i, errorbars represent mean ± SEM across n = 11 sessions, Early and Late Delay epochs and 100 pairs of neuronal pools. From b-i, the fact that information in neural activity informs choice more effectively when it is consistent cannot be explained by differences in overall stimulus information level. Rather, for a given amount of sensory information, more information can be extracted to guide behavioral choices if it is distributed redundantly across neurons or across time.

Extended Data Fig. 6 The role of neural consistency in the readout of PPC activity is not due to the consistency of measured behavioral parameters.

Panels a-e refer to PPC neural activity during the sound localization task. To rule out the concern that the impact of across-time consistency of PPC activity on the mouse’s choice does not only reflect the effect of running related parameters (whose temporal consistency may correlate with both the mouse’s choice and the temporal consistency of neural activity), we developed and fit to PPC data a more sophisticated readout model that explicitly includes such contributions in predicting choices. a, The temporal evolution of the decoder posterior probability of left stimulus presentation given the recorded PPC population activity is shown along with the corresponding temporal evolution of a selection of three concurrently-measured behavioral parameters (lateral position, lateral velocity, view angle), for an example left (orange) and right (blue) cue trial. Colored dots indicate two example time point pairs with consistent (t1 − t2, dark purple) or inconsistent (t3-t4, light purple) neural information. Colored dots in the first and third row show that neural consistency is not necessarily associated to behavioral consistency (when considering lateral running speed, t1-t2 are behaviorally inconsistent while t3 − t4 are behaviorally consistent). b, Schematic representation of the virtual T-maze with corresponding x-y coordinates labelling and mouse’s view angle (for a mouse oriented along the y axis). c-e, Performance (fraction of deviance explained) in explaining (using two population vectors at different points) single-trial mouse choice of models that use both neural and behavioral consistency (c: lateral position, d: lateral velocity, e: view angle). Full model includes all predictors values, comprising neural and behavioral consistency. No Cons model neglects neural consistency by shuffling consistency values across trials. c, P = 10−4. d, P = 2 × 10−4. e, P = 10−4, two-sided permutation test. Errorbars report mean ± SEM across n = 6 sessions and all pairs of time point within a 1 s lag. Results in c-e show that neural consistency still contributed to predicting choices when we added the consistency of running-related variables to the choice regression. This suggests that consistency of the instantaneous PPC population activity across time genuinely influences the behavioral readout of the stimulus information, above and beyond what can be predicted about choice from the consistency of measured behavioral variables.

Extended Data Fig. 7 Across-time correlations in AC do not benefit task performance as they do in PPC.

Panels a-h refer to PPC neural activity during the sound localization task. a-h, Summary of the main results of the analysis of across-time correlations in PPC activity (from Fig. 1, Fig. 4 and Fig. 5), useful for the comparison with AC data. Panels i-p refer to AC neural activity during the sound localization task. i, Pairwise (left) and population-wise (right) noise correlations in time-lagged activity, for correct and error trials. Overall, noise correlations strength is lower in AC than in PPC. j, Distribution of the signal-noise angle γ (over n = 6 sessions and all time point pairs within a 2 s lag). Boxplots show the median (line), quartiles (box) and whiskers extend to ±1.5*interquartile range. Red dotted line: analytically computed bound between the information-limiting and information-enhancing regime. k, Accuracy of a linear decoder of the stimulus applied to joint population activity at two different time points, for real recorded (black) or trial-shuffled (gray) data. The decoder accuracy is higher in AC than in PPC (fraction correct: 0.676 ± 0.003 in AC, 0.602 ± 0.001 in PPC, P = 10−4, two-sided permutation test), compatible with the view that AC is involved in the encoding of sound information. Across-time correlations limit the encoding of stimulus information also in AC, but with a smaller effect than in PPC (average increase in decoder accuracy by shuffling: 0.018 ± 0.001 in AC, 0.026 ± 0.001 in PPC. P = 10−4, two-sided permutation test. Equivalent percentage increase of above-chance (that is above 50%) decoding performance: 10.5% in AC, 25.5% in PPC). l, Fraction of trials in which stimulus information is encoded consistently across time, for real recorded (black) or trial-shuffled (gray) data. The increase in consistency due to across-time correlations is smaller in AC than in PPC (−0.025 ± 0.001 in AC, −0.064 ± 0.01 in PPC, P < 10−4, two-sided permutation test). m, Performance (fraction of deviance explained) in explaining single-trial choices of several readout models (see Methods). Full model uses all predictors (neural and non-neural). ‘No Cons’ model neglects neural consistency. ‘No Neural’ model neglects stimulus decoded from neural activity and neural consistency. A linear SVM is used to decode the stimulus from neural activity. Across-time consistency in AC provides negligible improvements in behavioral choice predictions when compared to PPC (increase in fraction of deviance explained when comparing the Full with the ‘No Cons’ model: 0.0045 ± 0.0001 in AC, 0.0083 ± 0.0012 in PPC, P = 10−4, two-sided permutation test). n, Best-fit coefficients of the Full readout model. AC neural predictors are characterized by low weights. o, Task performance predicted by applying the best-fit readout model to real recorded (black) or trial-shuffled (gray) data. Task performance attributable to recorded neurons is much lower in AC than in PPC (~1% in AC, ~3.5% in PPC). Correlations in AC activity enhance task performance, but the effect is small. p, Task performance predicted by applying to real recorded population vectors the best-fit enhanced-by-consistency (black) and the consistency-independent readout model (green). Task performance attributable to the recorded AC neural activity would not be substantially different if the behavioral readout was consistency-independent. In i, k–p, errorbars report mean ± SEM across all cell pairs (only b-left) and all time point pairs within the specified lag range or within a 1 s lag from n = 6 sessions. For i, left, P = 10−4 for all comparisons, right, P = 0.0016 for lag 0-1 s, P = 0.001 for lag 1–2 s. For k, l, P = 10−4. For m, ***P = 10−4, *P = 0.0324. For o, P = 0.0191. For p, P = 0.0690. All comparisons, two-sided permutation test.

Extended Data Fig. 8 Exploration of the parameters of the biophysical model for the enhanced-by-consistency readout.

a, Normalized coefficient of variation (CV) computed for different values of the membrane time constant τm of the readout neuron and EPSP strength w (connection strength from the input to the readout neuron). The mean input rate was set to Rin = 6 Hz. The red parameter region corresponds to the region where the standard deviation of the readout firing rate increases less than the readout mean firing rate with the value of spatial correlations. b, Contour lines corresponding to the parameter values (τm, w) where the normalized CV is equal to 1, for different values of the input firing rate. c, Contour lines for which the normalized CV is equal to unity, in the parameter space defined by the membrane time constant τm and the mean EPSP input in a window τm normalized by the voltage gap \({\Delta}V = V_{threshold} - V_r\), that is \(K = wR_{in}\tau _m/{\Delta}V\). Regions of parameters on the left of the contour lines correspond to the parameter values where the standard deviation of the readout neuron increases less than its mean with spatial correlations. d–f, Same as a-c for temporal correlations.

Extended Data Fig. 9 Encoding model internally generating correlated activity through recurrent dynamics.

a, Schematic illustrating the basic setup of the encoding recurrent model. Two neurons receive stimulus-dependent feedforward input (which determines the signal correlations) and input noise, and are connected through recurrent synapses with strength w. b, Noise correlations are generated through recurrent connectivity, and depend on the sign of w (for w = 0 responses are uncorrelated). Top: for positive signal correlations, positive (resp. negative) values of the connectivity generate information-limiting (resp. information-enhancing) noise correlations. Bottom: for negative signal correlations, positive (resp. negative) values of the connectivity generate information-enhancing (resp. information-limiting) noise correlations. c-f, Average pairwise noise correlation (over n = 10000 random pairs of neurons) (c), decoding accuracy for correlated and shuffled responses (d), difference in decoding accuracy between correlated and shuffled responses for different values of shared noise (e) and average consistency (f) as a function of connectivity strength w. In c,d,f the external input noise is uncorrelated across the two neurons. g, Schematic illustrating the 2N-dimensional encoding recurrent model. Two N-dimensional neuronal groups with opposite stimulus selectivity receive stimulus-dependent feedforward input and input noise. The connectivity strength is excitatory, sparse and takes the value w > 0 between neurons belonging to the same group, and η > 0 between neurons belonging to different groups. h, Example of a connectivity matrix adopted in these analyses. All matrix entries are positive (excitatory synapses) and sparse with connection probability p. i-l Same quantities computed in c-f as a function of the difference between the within-group connectivity and between-groups connectivity strength, w − η. We set N = 50, p = 0.5, η = 0.5. In c-f, i-l data are presented as mean ± SEM over n = 50 simulations.

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Valente, M., Pica, G., Bondanelli, G. et al. Correlations enhance the behavioral readout of neural population activity in association cortex. Nat Neurosci 24, 975–986 (2021). https://doi.org/10.1038/s41593-021-00845-1

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