Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making

Abstract

Recent work has suggested that the prefrontal cortex (PFC) plays a key role in context-dependent perceptual decision-making. In this study, we addressed that role using a new method for identifying task-relevant dimensions of neural population activity. Specifically, we show that the PFC has a multidimensional code for context, decisions and both relevant and irrelevant sensory information. Moreover, these representations evolve in time, with an early linear accumulation phase followed by a phase with rotational dynamics. We identify the dimensions of neural activity associated with these phases and show that they do not arise from distinct populations but from a single population with broad tuning characteristics. Finally, we use model-based decoding to show that the transition from linear to rotational dynamics coincides with a plateau in decoding accuracy, revealing that rotational dynamics in the PFC preserve sensory choice information for the duration of the stimulus integration period.

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Fig. 1: Context-dependent decision-making task and neural responses.
Fig. 2: Schematic illustrating low-dimensional population-level encoding in a binary sensory decision-making task.
Fig. 3: Model fit for monkey A.
Fig. 4: Projections of population PSTHs onto latent encoding subspaces.
Fig. 5: Distribution of variance within and between subspaces.
Fig. 6: Instantaneous decoding of stimulus for monkey A.
Fig. 7: Instantaneous decoding of choice.

Data availability

Data are available for download at https://www.ini.uzh.ch/en/research/groups/mante/data.html.

Code availability

Demo code for the mTDR method is available for MATLAB at http://www.mikioaoi.com/samplecode/RDRdemo.zip

References

  1. 1.

    Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Roy, J. E., Buschman, T. J. & Miller, E. K. PFC neurons reflect categorical decisions about ambiguous stimuli. J. Cogn. Neurosci. 26, 1283–1291 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Siegel, M., Buschman, T. J. & Miller, E. K. Cortical information flow during flexible sensorimotor decisions. Science 348, 1352–1355 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Katz, L. N., Yates, J. L., Pillow, J. W. & Huk, A. C. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535, 285–288 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Erlich, J. C., Brunton, B. W., Duan, C. A., Hanks, T. D. & Brody, C. D. Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat. eLife 4, e05457 (2015).

    PubMed Central  Article  PubMed  Google Scholar 

  8. 8.

    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).

    PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Bruce, C. J. & Goldberg, M. E. Primate frontal eye fields. I. Single neurons discharging before saccades. J. Neurophysiol. 53, 603–635 (1985).

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Kim, J. N. & Shadlen, M. N. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat. Neurosci. 2, 176–185 (1999).

    PubMed  Article  Google Scholar 

  11. 11.

    Ding, L. & Gold, J. I. Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. Cereb. Cortex 22, 1052–1067 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Stokes, M. G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Purcell, B. A. et al. Neurally constrained modeling of perceptual decision making. Psychol. Rev. 117, 1113–1143 (2010).

    PubMed Central  Article  PubMed  Google Scholar 

  15. 15.

    Heitz, R. P. & Schall, J. D. Neural mechanisms of speed-accuracy tradeoff. Neuron 76, 616–628 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Parthasarathy, A. et al. Mixed selectivity morphs population codes in prefrontal cortex. Nat. Neurosci. 20, 1770–1779 (2017).

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Beck, J. M. et al. Probabilistic population codes for Bayesian decision making. Neuron 60, 1142–1152 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Churchland, M. M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 17, 440–448 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Lara, A. H., Cunningham, J. P. & Churchland, M. M. Different population dynamics in the supplementary motor area and motor cortex during reaching. Nat. Commun. 9, 2754 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Akaike, H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974).

    Article  Google Scholar 

  22. 22.

    Aoi, M. & Pillow, J. W. Model-based targeted dimensionality reduction for neuronal population data. Adv. Neural Inform. Process. Syst. 31, 6690–6699 (2018).

    Google Scholar 

  23. 23.

    Elsayed, G. F. & Cunningham, J. P. Structure in neural population recordings: an expected byproduct of simpler phenomena? Nat. Neurosci. 20, 1310–1318 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  24. 24.

    Rossi-Pool, R. et al. Decoding a decision process in the neuronal population of dorsal premotor cortex. Neuron 96, 1432–1446 (2017).

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Williamson, R. C. et al. Scaling properties of dimensionality reduction for neural populations and network models. PLoS Comput. Biol. 12, e1005141 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. 26.

    Cunningham, J. P. & Byron, M. Y. Dimensionality reduction for large-scale neural recordings. Nature Neurosci. 17, 1500–1509 (2014).

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Kobak, D. et al. Demixed principal component analysis of neural population data. eLife 5, e10989 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. 28.

    Machens, C. K. Demixing population activity in higher cortical areas. Front. Comput. Neurosci. 4, 126 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Machens, C. K., Romo, R. & Brody, C. D. Functional, but not anatomical, separation of ‘what’ and ‘when’ in prefrontal cortex. J. Neurosci. 30, 350–360 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Romo, R., Brody, C. D., Hernández, A. & Lemus, L. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399, 470–473 (1999).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Hernández, A., Zainos, A. & Romo, R. Temporal evolution of a decision-making process in medial premotor cortex. Neuron 33, 959–972 (2002).

    PubMed  Article  Google Scholar 

  32. 32.

    Romo, R., Hernández, A., Zainos, A., Lemus, L. & Brody, C. D. Neuronal correlates of decision-making in secondary somatosensory cortex. Nat. Neurosci. 5, 1217–1225 (2002).

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Romo, R., Hernández, A. & Zainos, A. Neuronal correlates of a perceptual decision in ventral premotor cortex. Neuron 41, 165–173 (2004).

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M. & Cunningham, J. P. Reorganization between preparatory and movement population responses in motor cortex. Nat. Commun. 7, 13239 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Gold, J. I. & Shadlen, M. N. The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J. Neurosci. 23, 632–651 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Kiani, R., Hanks, T. D. & Shadlen, M. N. Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment. J. Neurosci. 28, 3017–3029 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Meister, M. L. R., Hennig, J. A. & Huk, A. C. Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making. J. Neurosci. 33, 2254–2267 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Latimer, K. W., Yates, J. L., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science 349, 184–187 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. & Huk, A. C. Functional dissection of signal and noise in mt and lip during decision-making. Nat. Neurosci. 20, 1285–1292 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. 40.

    Vernet, M., Quentin, R., Chanes, L., Mitsumasu, A. & Valero-Cabré, A. Frontal eye field, where art thou? anatomy, function, and non-invasive manipulation of frontal regions involved in eye movements and associated cognitive operations. Front. Integr. Neurosci. 8, 1–24 (2014).

    Google Scholar 

  41. 41.

    Bruce, C. J., Goldberg, M. E., Bushnell, M. C. & Stanton, G. B. Primate frontal eye fields. II. Physiological and anatomical correlates of electrically evoked eye movements. J. Neurophysiol. 54, 714–734 (1985).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Rizzolatti, G., Riggio, L., Dascola, I. & Umiltá, C. Reorienting attention across the horizontal and vertical meridians: evidence in favor of a premotor theory of attention. Neuropsychologia 25, 31–40 (1987).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Thompson, K. G., Biscoe, K. L. & Sato, T. R. Neuronal basis of covert spatial attention in the frontal eye field. J. Neurosci. 25, 9479–9487 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Schall, J. D. On the role of frontal eye field in guiding attention and saccades. Vision Res. 44, 1453–1467 (2004).

    PubMed  Article  Google Scholar 

  45. 45.

    Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Series B Stat. Methodol. 64, 479–498 (2002).

    Article  Google Scholar 

  46. 46.

    Brody, C. D., Hernández, A., Zainos, A. & Romo, R. Timing and neural encoding of somatosensory parametric working memory in macaque prefrontal cortex. Cereb. Cortex 13, 1196–1207 (2003).

    PubMed  Article  Google Scholar 

  47. 47.

    Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).

    PubMed Central  Article  PubMed  Google Scholar 

  48. 48.

    Lawrence, N. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Mach. Learn. Res. 6, 1783–1816 (2005).

    Google Scholar 

  49. 49.

    Cunningham, J. P. & Ghahramani, Z. Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16, 2859–2900 (2015).

  50. 50.

    Mackevicius, E. L. et al. Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. eLife 8, e38471 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

This work was supported by grants from the McKnight Foundation, the Simons Collaboration on the Global Brain (SCGB AWD543027 to M.C.A. and J.W.P.), the National Institutes of Health BRAIN Initiative (R01EB026946 and NS104899 to J.W.P.), the National Science Foundation CAREER Award (IIS-1150186 to J.W.P.) and a U19 NIH-NINDS BRAIN Initiative Award (5U19NS104648 to M.C.A. and J.W.P.). V.M. was supported by the Swiss National Science Foundation (SNSF Professorship PP00P3-157539), the Simons Foundation (to W. T. Newsome and V.M., award 328189), the Swiss Primate Competence Center in Research, the Howard Hughes Medical Institute (through W. T. Newsome, investigator) and the DOD USAF AFMC Air Force Research Laboratory: W. T. Newsome, agreement number FA9550-07-1-0537.

Author information

Affiliations

Authors

Contributions

M.C.A and J.W.P. developed the model and performed data analysis. V.M. conceived and conducted the experiments and collected the data. All authors helped with the interpretation of analysis and writing of the paper.

Corresponding author

Correspondence to Mikio C. Aoi.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks John Cunningham, Camillo Padoa-Schioppa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Projections of population PSTH’s onto the first, second, and third PC-axes for monkey A.

a, The abs(motion) and b, abs(color) subspaces. Subspaces have been orthogonalized with respect to the first dimension of the choice subspace. The monkey gave the correct response for all trials used. Colored axes indicate dominant axes in the early, middle, and late periods of the stimulus epoch, as determined by the methods described in Supplementary section 9. Purple vertical lines indicate transition from the early to middle epochs. Yellow vertical lines indicate transition from the middle to late epochs as in Figure Figure 4. Plotting colors are the same as those in Figure 4. Units of the ordinate are arbitrary but all axes are on the same scale.

Extended Data Fig. 2 Projections of population PSTH’s onto jPCA axes for monkey A.

Projections are onto the first two jPCA axes identified by the trajectories shown in Figure 4. The jPCA axes reveal strongly rotational dynamics for motion, color, choice, and context subspaces.

Extended Data Fig. 3 Projections of population PSTH’s for monkey F onto the first, second, and third PC-axes of all task variables subspaces.

Plotting conventions and analyses are the same as those for Figure 4. Projected data is averaged over 2-folds of cross validated projections where a random sampling of half of the data was used to estimate parameters and the remaining half used to make projections.

Extended Data Fig. 4 Encoding strength of population pseudosamples for monkey F onto the first three axes of all task variables subspaces.

Plotting conventions and analyses are the same as those for Figure 4. Projected data is averaged over 2-folds of cross validated projections where pseudosamples were drawn from held-out trials. Grey bars at y = 0 indicate time points where the mTDR projections had significantly stronger encoding across all stimulus levels than the 1D projections (left-tailed Wilcoxon signed-rank test, pFDR45 controlled at .01).

Extended Data Fig. 5 Distribution of variance among seqPCA axes. Monkey F.

Plotting conventions are the same as for Figure 5. a, Proportion of variance among seqPCA axes. Each marker corresponds to one neuron. The position of each neuron indicates the distribution of variance from PSTHs across corresponding early, middle, and late axes. e.g. a point that lies closer to the ‘early’ vertex of the motion plot has more of its motion-specific variance explained by the early axis while a point in the middle of the simplex has variance equally distributed across all axes. Darker regions indicate higher density of points. Colored dots correspond to cells displayed in Figure 3. b, Weights of the top (in terms of variance explained) 3 axes for all cells for motion, color, and choice subspaces. Cell indexes are sorted according to the choice weights from most positive to most negative. c, Magnitude of the Pearson correlation between top 3 subspace axes. The magnitude is used because the axes are only identifiable up to a sign. Markers indicate significant correlations controlled by the positive false discovery rate45)(* Q < .01, +Q < .01). Null distribution is based on the positive half-Gaussian with zero-mean and standard deviation σ0 = 1/n, where n = 640 is the number of neurons. Significant correlations are most consistent between color-choice and motion-choice pairs.

Extended Data Fig. 6 Rotational dynamics of subspace projections for Monkey F.

a, Projections of population PSTH’s for monkey F onto the jPC-axes of all task variables subspaces. Plotting conventions and analyses are the same as those for Figure 4. Projected data is averaged over 2-folds of cross validated pro- jections where a random sampling of half of the data was used to estimate parameters and the remaining half used to make projections. b, Angle of rotation over time for low-D trajectories of monkey F. Rotation angle tra- versed through rotational projection using jPCA. Angle was calculated starting from time when the projection transitions between the early and middle epochs. Coherent traversal across stimulus strengths that is con- sistent and monotonically increasing is an indication of rotation. Shaded areas are 95% confidence regions calculated using a maximum entropy method 23 (n = 100 samples) under the null hypothesis of no population structure other than the empirical means and covariances across time, neurons, and task conditions.

Extended Data Fig. 7 Instantaneous decoding of stimulus for monkey F.

Plotting conventions and analyses are the same as for Figure 6 a, Top: Decoded motion coherence by mTDR model in both contexts. Bottom: Mean squared error (MSE) over time of motion coherence decoding across stimulus levels and context. MSE decreases precipitously, and then stabilize around the time of the first transition. b, Same as a) for color coherence decoding. Shaded regions indicate 50% confidence intervals. Dashed lines indicate error trials from the corresponding context for the lowest stimulus strengths. 100 pseudotrials for each of 2-fold cross validation used for each analysis. Solid vertical lines indicate the time of early/middle axis transition for the corresponding stimulus subspace projections. Dashed vertical lines indicate the time of middle/late transition.

Extended Data Fig. 8 Instantaneous decoding of decision for monkey F.

Plotting conventions and analyses are the same as for Figure 6 a, Log-likelihood ratios (LLRs) in favor of a preferred choice using single pseu- dotrials from color - context (gold-blue, sorted by color coherence) and motion - context (red-violet, sorted by motion coherence) trials. Shaded regions indicate 95% quantile intervals for each stimulus strength. Solid lines indicate the median of correct trials. Dashed lines indicate median of error trials. b, Probability of a preferred choice based on corresponding LLRs combined over all stimulus strengths (see section 6.3 for details). Solid lines indicate median of correct trials. Dashed lines indicate median of error trials. Shaded regions indicate quantile coverage intervals of correct trials (light-to-dark: 95%,75%,50%). 100 pseudotrials for each of 2-fold cross validation folds used for all analyses. c, LLRs for in favor of a preferred choice where the choice subspace has been restricted to only the early, middle, or late axes. d, Probability of a preferred choice based on LLRs from (c).

Extended Data Fig. 9 Instantaneous decoding of context for monkey A.

a, LLRs for monkey A in favor of the motion context using single pseudotrials, sorted by color coherence. Shaded regions indicate 95% quantile intervals for each stimulus strength. Solid lines indicate the median over correct trials. Dashed lines indicate median of error trials. b, Probability of the motion context based on corresponding LLRs combined over all stimulus strengths. Solid lines indicate median of correct trials. Dashed lines indicate median of error trials. Shaded regions indicate quantile intervals of correct trials (light-to-dark: 50%, 75%, 95%). Color conventions are the same as in Figure 4. 100 pseudotrials for each of 4-fold cross validation folds used for all analyses.

Extended Data Fig. 10 Instantaneous decoding of context for monkey F.

Plotting conventions are the same as in Extended Data 9. 100 pseudotrials for each of 2-fold cross validation folds used for all analyses.

Supplementary information

Supplementary Information

Supplementary Math Note, Supplementary Table and Supplementary Figs. 1–3.

Reporting Summary

Video 1

Latent trajectories in motion subspace for monkey A.

Video 2

jPCA projection and angle of rotation for trajectories in motion subspace of monkey A.

Video 3

Latent trajectories in color subspace for monkey A.

Video 4

jPCA projection and angle of rotation for trajectories in color subspace of monkey A.

Video 5

Latent trajectories in choice subspace for monkey A.

Video 6

jPCA projection and angle of rotation for trajectories in choice subspace of monkey A.

Video 7

Latent trajectories in context subspace for monkey A.

Video 8

jPCA projection and angle of rotation for trajectories in context subspace of monkey A.

Video 9

Latent trajectories in motion subspace for monkey F.

Video 10

jPCA projection and angle of rotation for trajectories in motion subspace of monkey F.

Video 11

Latent trajectories in color subspace for monkey F.

Video 12

jPCA projection and angle of rotation for trajectories in color subspace of monkey F.

Video 13

Latent trajectories in choice subspace for monkey F.

Video 14

jPCA projection and angle of rotation for trajectories in choice subspace of monkey F.

Video 15

Latent trajectories in context subspace for monkey F.

Video 16

jPCA projection and angle of rotation for trajectories in context subspace of monkey F.

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Aoi, M.C., Mante, V. & Pillow, J.W. Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making. Nat Neurosci (2020). https://doi.org/10.1038/s41593-020-0696-5

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