Neuronal stability in medial frontal cortex sets individual variability in decision-making


In the brain, decision making is instantiated in dedicated neural circuits. However, there is considerable individual variability in decision-making behavior, particularly under uncertainty. The origins of decision variability within these conserved neural circuits are not known. Here we demonstrate in the rat medial frontal cortex (MFC) that individual variability is a consequence of altered stability in neuronal populations. In a sensory-guided choice task, rats trained on familiar stimuli were exposed to unfamiliar stimuli, resulting in variable choice responses across individuals. We created a recurrent network model to examine the source of variability in MFC neurons, and found that the landscape of neural population trajectories explained choice variability across different unfamiliar stimuli. We experimentally confirmed model predictions showing that trial-by-trial variability in neuronal activity indexes the landscape and predicts individual variation. These results show that neural stability is a critical component of the MFC neural dynamics that underpins individual variation in decision-making.

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Fig. 1: Individual differences in probabilistic choices across rats.
Fig. 2: Responses of network models reproduce individual differences.
Fig. 3: Neural dynamics in MFC and in the reservoir.
Fig. 4: Predictions of choice behavior by MFC and model.
Fig. 5: Characterization of neural dynamics for different choice behaviors.
Fig. 6: Trial-by-trial variability predicts individual difference.

Data availability

The experimental data that support the findings in this study are available from the corresponding author upon reasonable request.


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We thank J. Johansen and C. Yakoyama for critical comments on the manuscript, and M. Tatsuno and K. Watanabe for discussions about the analysis of correlation. This work was partly supported by KAKENHI (grants 16H01289 and 17H06036 to T.F.) from MEXT.

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T.F. and T.K. designed the work and constructed the model. T.K. conducted numerical simulations and experimental and numerical data analyses. T. Handa performed behavioral and electrophysiological experiments and analyzed experimental data. T. Haga performed behavioral and lesion experiments, and R.H. performed behavioral training, surgery and histological staining. T.F, T.K., T. Handa and T. Haga wrote the manuscript.

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Correspondence to Tomoki Fukai.

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Integrated supplementary information

Supplementary Fig. 1 Relationship between behavioral and neural metrics.

| A crucial hypothesis for the relationship between behavioral metric and neural metric is schematically illustrated. Left, Definition of behavioral sensitivity S. Fitting a tangent function to psychometric curves in rats and models gives behavioral sensitivity, which quantitatively characterizes individual differences. Higher the value of S, the more graded psychometric curve (decision-making is more sensitive to tone). Right, Definition of neural responsiveness χ of the model. Neural responsiveness is the averaged Euclidean distance between the non-perturbed trajectory and neural states generated by random perturbative stimuli applied to a given state of the network (Methods). The larger the value of χ is, the more sensitive to perturbation the neural dynamics are.

Supplementary Fig. 2 Robustness of parameter fitting for sensitivity.

| a, We resampled choices 50 times to generate 50 new psychometric curves (gray lines) for the sensitive network shown in Fig. 2e. The original psychometric curve is shown in orange. b, We fitted the 50 resampled sensitive and insensitive psychometric curves shown in Fig. 2e with a different fitting function P(fn)= b’tan(afn) +(1-b’). Here, fn=0 and 1 represent FL and FH, respectively (Methods). The center values and error bars indicate the average and s.d. c, The new sensitivity values S’=a’b’ are plotted for 31 successful learners, which are sorted in the abscissa in an increasing order of the original sensitivity values S. d, The original and new sensitivity values S and S’ are well correlated across all successful learners. In the x-axis and y-axis, error bars show 25 and 75 percentiles of 50 resampled sensitivity values S and S’ of each successful learner, respectively.

Supplementary Fig. 3 Familiar neural population activities projected onto PCs in a rat.

| To reduce the dimensionality, we applied PCA to neural population activities evoked by FH (green) and FL (red) stimuli in left- (solid) and right- (dashed) choice trials (Methods). From the top, neural activities were projected onto PC1, PC2 and PC3. Fig. 3C shows the same neural trajectories in the two-dimensional space spanned by PC2 and PC3. These results were obtained from 379 FH→left, 309 FL→right successful trials, 101 FH→right and 67 FL→left unsuccessful trials.

Supplementary Fig. 4 Reduced neural dynamics of rats and models.

| We approximated neural dynamics with a two-factor (choice and stimuli) regression model for typical three network models (top) and six rats (middle and bottom). We plotted neural trajectories evoked by FH (green) and FL (red). In addition, we plotted trajectories for left (solid) and right (dotted) choices. Gray and colored (green, red) circles indicate stimulus onset and mean reaction time (RT). Out of the 31 successful networks, we selected three such networks that showed sufficiently many (> 10) error trials for both choices to generate robust trajectories. We excluded two rats from the eight rats because these rats did not give sufficiently many neurons due to the shortage of spikes. Shaded areas indicate the period of stimulus application, and black bars above the curves indicate the epochs of significant separation between FH-left and FL-right trajectories according to Cohen’s d (see the subsection "Linear regression analysis" in the Methods). The number of experiments in rat#879, #880, #897, #940, #941 and #949 are: 263, 347, 379, 433, 422 and 347 FH→left and 286, 338, 309, 403, 433 and 333 FL→right successful trials; 50, 61, 67, 0, 6 and 30 FH→right and 24, 50, 101, 24, 43 and 44 FL→left unsuccessful trials. The number of simulations in net #11, #36 and #39 are: 44, 44 and 43 FH → left and 45, 44 and 48 FL → right success trials; 6, 6 and 7 FH → right and 6, 5 and 2 FL → left unsuccess trials.

Supplementary Fig. 5 Neural dynamics without feedback connections.

| Simulations of the model were performed with cutting off feedback connections from the readout units to the reservoirs just after stimulus onset. Neural trajectories mapped onto the choice axis (top) and stimulus axis (middle) are shown in different trial types (FH: green, FL: red, left: solid, right: dotted) with (thin) and without (thick) feedback connections. As in the rats, continuous increases in the separation of neural trajectories are terminated after the removal of familiar cues. The axes were identical as those used in Fig. 3f from neural dynamics in the presence of the feedback. Dynamics of L- (green) and R- (red) readout neurons without feedback indicates that decision-making is possible even without the feedback (bottom). Shaded areas represent the standard deviations of the trajectories over trials. These results were obtained from 44 FH→left, 44 FL→right successful trials and 6 FH→right and 6 FL→left unsuccessful simulation trials in a network model.

Supplementary Fig. 6 Effects of sensitivity on learning process.

| a, Neural responsiveness χ and sensitivity S of various networks that failed to learn association between stimuli and choices (black circles) are shown. Those of successful networks are shown in gray (the same successful networks were previously shown in Fig. 5d). b, c, Learning performance is plotted for the rats (b) and models (c) against sensitivity S. Here, performance is defined as the fraction of correct trials during the entire recording session (that is, a few hundred trials) for each rat or the fraction of correct trials in 100 (50 FH + 50 FL) test trials for each model. d, e, Learning step at which the success rate (the fraction of correct trials during 20 successive learning steps) of each model firstly exceeded 80% is plotted against sensitivity (d) and responsiveness (e). f, The first day of training session on which each rat achieved the criterion percent correct is plotted against sensitivity.

Supplementary Fig. 7 Relationship between reaction times and the sensitivity in psychometric curves.

| a, e, Medians of RT for two familiar tones are plotted against sensitivity in rats (a) and models (e). Error bars show the first and third quartiles of RT distributions. Typical sample sizes are about 100 trials per familiar tone per rat and 50 trials per familiar tone per model network. b, f, Medians of RT for unfamiliar tones are plotted similarly to a and e. Typical sample sizes are about 10 trials per unfamiliar tone per rat and 50 trials per unfamiliar tone per model network. c, g, Differences in the median RTs between familiar (upper) and unfamiliar (lower) tones are plotted similarly to a and e. Pearson’s correlations and p values of two-sided t-test are shown through panels a to g. d, h, RT distributions in a rat and a model, respectively. The results were obtained from about 100 × 2 familiar and 10 × 5 unfamiliar trials for rat (d) and 50 × 2 familiar and 50 × 5 unfamiliar trials for model (h).

Supplementary Fig. 8 Changes in reaction time during training.

| a, b, The histograms of RT are compared between the last day of training session (a) and the first day of recording session (b) for the eight rats. The trial numbers of the eight rats #807, #879, #880, #897, #902, #940, #941 and #949 are (706, 670), (803, 640), (735, 822), (966, 886), (364, 838), (1102, 937), (955, 998) and (798, 795), respectively, where the first and second numbers in each parenthesis refer to trial numbers in the last training session (6429 trials in total) and recording session (6586 trials), respectively. (c), The medians of RT were significantly changed in six rats (filled squares: Mann-Whitney U-test, rat#807, p = 7.8x10−13; #880, p = 2.3x10−34; #897, p = 2.4x10−23; #902, p = 1.9x10−12; #940, p = 5.0x10−5; #941, p = 1.8x10−18), but not in two rats (empty squares: Mann-Whitney U-test, rat#879,p = 0.72; #949,p = 0.34). Error bars show the first and third quartiles.

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Kurikawa, T., Haga, T., Handa, T. et al. Neuronal stability in medial frontal cortex sets individual variability in decision-making. Nat Neurosci 21, 1764–1773 (2018).

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