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.

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, pFDR 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 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 rate)(* 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 projections 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 traversed 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 consistent and monotonically increasing is an indication of rotation. Shaded areas are 95% confidence regions calculated using a maximum entropy method (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 (LLR’s) in favor of a preferred choice using single pseudotrials 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.

Figure 1. Context-dependent decision-making task and neural responses.

a) On each trial, the animal was presented with a context cue (yellow dot or blue cross) indicating which dimension of the stimulus the animal is to attend to, followed by a stimulus of colored, moving dots. On motion context trials the animal is cued to respond to the dominant dot motion direction. In color context trials the animal is cued to respond to the dominant color of the dots. b) The strength of both the color (red / green) and motion (left / right) stimulus was displayed with one of six possible degrees of coherence, making for many possible task contingencies (2 choices × 2 contexts × 6 motion strengths × 6 color strengths = 144 possible combinations). c) PSTHs of representative neurons for monkey A. Motion context PSTHs were sorted by motion coherence and averaged over color coherence. Color context PSTH’s were sorted by color coherence and averaged over motion coherence. Red–indigo color scale indicates motion coherence where red indicates the preferred motion direction. Gold–blue color scale indicates color coherence where gold indicates the preferred color direction. Bolder colors indicate stronger coherence.

Figure 2. Schematic illustrating low-dimensional population-level encoding in a binary sensory decision-making task.

(a). Conditional PSTHs for three neurons that exhibit mixed selectivity to a stimulus variable (taking on six different values) and a choice variable (taking on two values). (b) Modulations of the PSTHs by the task variables span a 2-dimensional “encoding subspace”, which is low-dimensional relative to the 3-dimensional space of firing rates. In this case, a 1D stimulus-encoding subspace (blue arrow) captures all information about the stimulus value, while a 1D choice-encoding subspace (red arrow) captures all information about the decision. Note, for example, that the neuron 2 firing rate axis is nearly orthogonal to the choice axis, meaning that neuron 2 carries almost no information about choice. (c). Projections onto the stimulus and choice subspaces reveal the time-course of information about stimulus and choice, respectively. These timecourses can be seen as temporal basis functions for the single-neuron PSTHs shown in (a). mTDR aims to recover these encoding subspaces even in the presence of additional components that take neural activity outside the plane spanned by these two axes, and is not restricted to 1D subspaces.

Figure 3. Model fit for monkey A.

a) Example of a neuron’s fitted responses composed of a set of weighted basis functions (same as neuron 1 from Figure 1c). These basis functions are shared by the whole population but are weighted differently for each neuron. Weighted basis functions are summed to form the neuron’s response to each task variable. The responses for each task variable are then added together to give the model reconstructed PSTHs (model PSTH). The conditional PSTHs of this neuron are shown for comparison. b) Summed responses for three additional example neurons (same as neurons 2–4, from Figure 1c) which display a diversity of dynamics. c) Dimension estimation based on 5x 4-fold (20 estimates) cross validation. Dimensionality is slightly smaller than estimated using all data but is tightly distributed around a single estimate. d) R ² of the model reconstructions for the PSTHs as a function of mean firing rate for each neuron. e) Percent variance explained for PSTHs of each neuron (n = 762) by projection onto each subspace dimension. Red horizontal bars indicate the median. Box edges indicate 25th and 75th percentiles. Whiskers indicate positions of furthest points from median not considered outliers. Red dots indicate outliers with respect to a normal distribution. Dots have been horizontally jittered to aid with visualization. Results have been averaged for each neuron over 4 CV folds. Colors in title text for (a) and (b) correspond to colors of markers in Figure 5.

Figure 4. Projections of population PSTH’s onto latent encoding subspaces.

Projections onto the first, second, and third principle-axes of the (a) motion, (b) color, (c) choice, and (d) context subspaces. Motion, color, and context subspaces have been orthogonalized with respect to the first dimension of the choice subspace. The choice subspace has been orthogonalized with respect to the context subspace. The context subspace has also been orthogonalized with respect to the motion and color subspaces. Details of orthogonalization are presented in Supplementary note 4.2. Color conventions are the same as those described in Figure Figure 1. Red dots indicate the origin. Projected PSTH’s made from held-out data not used during parameter estimation. a) Projections of PSTHs onto the motion subspace, sorted by motion coherence and averaged over color coherence for trials where the motion stimulus was the active context. b) Projections onto the color subspace sorted by color coherence and averaged over motion coherence for trials where the color stimulus was the active context. c) Projections onto the choice subspace. Motion context trials are displayed with the same sorting and color conventions as displayed in (a). Color context trials are displayed with the same sorting and color conventions as displayed in (b). Only correct trials are displayed. d) Projections onto the context subspace using the same conventions as displayed in (c). Only correct trials are displayed. Colored axes in 3D plots indicate seqPCA axes. Solid vertical lines accompanying time traces indicate the time points where middle-axis variance starts to increase. Dashed vertical lines indicate the time points where late-axis variance starts to increase. Units of the ordinate are arbitrary but all time-trace axes are on the same scale. PSTHs were generated with ≈ 13 ms time bins and smoothed with a Gaussian window with standard deviation of ≈ 50 ms. e) Median encoding strength of pseudotrials onto the first three encoding axes of mTDR compared with the 1D subspace estimated by the max-norm method used by Mante et al. (see Supplementary note 10 for details). For clarity, only trials with the strongest stimulus strengths are shown. 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, pFDR controlled at .01). Multidimensional mTDR projections are larger than 1D projections at nearly all times for all task variables. f) Rotation angle traversed 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 consistent and monotonically increasing is an indication of rotation. Shaded areas are 95% confidence regions calculated using a maximum entropy method (n = 100 samples) under the null hypothesis of no population structure other than the empirical means and covariances across time, neurons, and task conditions.

Figure 5. Distribution of variance within and between subspaces.

(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 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 rate)(* Q < .01, +Q < .01). Null distribution is based on the positive half-Gaussian with zero-mean and standard deviation σ ₀ = 1/n, where n = 762 is the number of neurons. Significant correlations are most consistent between color-choice and motion-choice pairs. All tests were 1-sided.

Figure 6. Instantaneous decoding of stimulus for monkey A.

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. Color conventions are the same as in Figure 4. 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 4-fold cross validation (n = 400) used for all analyses. 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.

Figure 7. Instantaneous decoding of choice.

a) Log-likelihood ratios (LLR’s) for monkey A in favor of a preferred choice using single pseudotrials 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%). Color conventions are the same as in Figure Figure 4. 100 pseudotrials for each of 4-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).