Abstract
Many models of cognition and of neural computations posit the use and estimation of prior stimulus statistics^{1,2,3,4}: it has long been known that working memory and perception are strongly impacted by previous sensory experience, even when that sensory history is not relevant to the current task at hand. Nevertheless, the neural mechanisms and regions of the brain that are necessary for computing and using such prior experience are unknown. Here we report that the posterior parietal cortex (PPC) is a critical locus for the representation and use of prior stimulus information. We trained rats in an auditory parametric working memory task, and found that they displayed substantial and readily quantifiable behavioural effects of sensorystimulus history, similar to those observed in humans^{5,6} and monkeys^{7}. Earlier proposals that the PPC supports working memory^{8,9} predict that optogenetic silencing of this region would impair behaviour in our working memory task. Contrary to this prediction, we found that silencing the PPC significantly improved performance. Quantitative analyses of behaviour revealed that this improvement was due to the selective reduction of the effects of prior sensory stimuli. Electrophysiological recordings showed that PPC neurons carried far more information about the sensory stimuli of previous trials than about the stimuli of the current trial. Furthermore, for a given rat, the more information about previous trial sensory history in the neural firing rates of the PPC, the greater the behavioural effect of sensory history, suggesting a tight link between behaviour and PPC representations of stimulus history. Our results indicate that the PPC is a central component in the processing of sensorystimulus history, and could enable further neurobiological investigation of longstanding questions regarding how perception and working memory are affected by prior sensory information.
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Acknowledgements
We thank C. Duan, R. Low, A. Piet, L. Pinto, B. Scott and I. Witten for their comments on the manuscript. We thank K. Osorio and J. Teran for animal and laboratory support.
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A.A. and C.D.B. conceived the project. A.A. carried out all experiments and analysed the data, with the optogenetic inactivations carried out with assistance from C.D.K. A.A. gathered human tactile data in M.E.D.’s laboratory. A.A. and C.D.B. wrote the manuscript, based on a first draft by A.A., with extensive comments from C.D.K. and M.E.D.
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Reviewer Information Nature thanks L. Busse and J. de la Rocha for their contribution to the peer review of this work.
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Extended data figures and tables
Extended Data Figure 1 Full stimulus set, learning curves, mean performance and reward bias.
a, Each stimulus is composed of a series of SPL values sampled from a zeromean normal distribution, and standard deviation of s. For each trial, SPL values are randomly drawn and therefore, owing to sampling statistics, the actual standard deviation value of the stimulus always differed slightly from its designated value. The coordinates of each small box represent the actual joint values of (s_{a}, s_{b}) for one sample training session. b, Individual grey lines show learning curves presented as the change in percentage correct over months of training, for n = 25 rats. An average rat (black line) reaches 70% of performance after 90 sessions. c, Learning curve presented as the ratio of the best fit weights for the second stimulus, s_{b}, to the first stimulus, s_{a}, using the model described in Fig. 2e (threeparameter, nohistory version). d, Rat auditory working memory performance, data from 21 rat subjects (total of 468,165 trials) are grouped according to (s_{a}, s_{b}) pair but averaged across subjects and over different delay durations (2–8 s). e, Human auditory working memory performance. For humans, the interstimulus delay varied randomly from 2 s to 6 s. (11 subjects, 12,623 trials). f, Human tactile working memory performance; similar to e but for humans engaged in the tactile version of the task. In this task, the interstimulus delay varied randomly from 2 s to 8 s. Data from 14 human subjects (total of 4,694 trials) are pooled together. g, Reward history bias. Left, the y axis shows, for turnleft trials and as a function of k, the percentage of subjects that went left when the k^{th} trial back was rewarded on the left, minus the percentage that went left when the k^{th} trial back was rewarded on the right. Right, the complementary plot for turnright trials: the percentage that went right when the k^{th} trial back was rewarded on the right, minus the percentage that went right when the k^{th} trial back was rewarded on the left. Data from n = 21 rats. Each point shows the mean value of the bias over subjects. Error bars show 95% confidence intervals. h, i, Similar to g for human auditory (h, n = 11 subjects) and tactile (i, n = 14 subjects) PWM tasks.
Extended Data Figure 2 Contraction bias grows as a function of the working memory delay interval of the current trial.
a, Slopes from linear fits to the percentage leftward bias (as in Fig. 2a), for rats that were each trained on delay intervals of 2, 4, and 6 s (n = 21). The plot on the left shows the behavioural bias (percentage that went left minus the average) as a function of working memory delay interval of the current trial. The plot on the right shows the behavioural bias as a function of working memory delay interval from one trial back. Each dot represents a rat; lines connect the different delay intervals for each rat. Left: from a onesided paired ttest, 2 versus 4 s: P = 0.012, 2 versus 6 s: P < 0.001; 4 versus 6 s: P < 0.001 *P < 0.001, onesided paired ttest. Right: 2 versus 4 s: P = 0.76, 2 versus 6 s: P = 0.37; 4 versus 6 s: P = 0.65. The behavioural bias increases with greater current working memory delay period, but no significant dependence on the working memory delay period of the previous trial is found^{26}. b, Percentage correct averaged across all bias^{+} trials or all bias^{−} trials, relative to overall average performance, as a function of working memory delay interval on the current trial. Data are pooled from a dataset in which different rats were trained on different sets of delay intervals; data for each delay interval may therefore contain different rats than data for other delay intervals (n = 25 rats total). Error bars show s.d. As in a, behavioural effect grows as a function of the current working memory delay period. *P < 0.001, onesided ttest. c, Schematics of stimuli used for three different psychometric curves: high s_{b}, in which contraction bias would lead all the s_{a} stimuli to be treated as lower than they actually were (indicated by the leftward arrows), producing a rightward shift of the psychometric curve; mid s_{b}, in which contraction bias would lead all the s_{a} stimuli to be treated as closer to s_{b} than they actually were, producing a flattening of the psychometric curve; and low s_{b}, in which contraction bias would lead all the s_{a} stimuli to be treated as higher than they actually were, producing a leftward shift of the psychometric curve. d, Psychometric curves for lows_{b} trials, averaged across rats and separately for each individual rat, for trials with a 2s working memory delay interval, and for trials with a 6s working memory delay interval. Curves are fits to a fourparameter logistic function (see Methods). As the working memory delay interval grows, the leftward shift predicted by contraction bias shift is more pronounced. For each individual rat, n = 120 sessions of data were used. Error bars show the s.e.m. over sessions. e, as in d but for the mids_{b} trials. As the working memory delay interval grows, the flattening predicted by contraction bias is more pronounced. f, as in d but for the highs_{b} trials. As the working memory delay interval grows, the rightward shift predicted by contraction bias is more pronounced.
Extended Data Figure 3 Sensoryhistory matrix, from one to five trials back.
a, Stimulushistory matrix, as described in Fig. 2a, when percentage left is shown given any combination of the stimuli in the current trial (x axis) and ntrials back (y axis), n = 1, 2, 3, 4, 5. Trial numbers indicate pairs of (s_{a}, s_{b}), values in dB. 1: (68, 60); 2: (76, 68); 3: (84, 76); 4: (92, 84); 5: (60, 68); 6: (68, 76); 7: (76, 84); 8: (92, 84). Data from n = 21 rats, comprising a total of 468,165 trials used in this analysis. b, Similar to a, for the human auditory task. Trial numbers, with values in dB: 1: (62.7, 60); 2: (65.4, 62.7); 3: (68.1, 65.4); 4: (70.8, 68.1); 5: (73.5, 70.8); 6: (60, 62.7); 7: (62.7, 65.4); 8: (65.4, 68.1); 9: (68.1, 70.8); 10: (70.8, 73.5). c, Similar to a, for the human tactile task. Trial numbers, in mm s^{−1}: 1: (33, 23); 2: (46, 33); 3: (64, 46); 4: (90, 64); 5: (125, 90); 6: (175, 125); 7: (245, 175); 8: (23, 33); 9: (33, 46); 10: (46, 64); 11: (64, 90); 12: (90, 125); 13: (125, 175); 14: (245, 175).
Extended Data Figure 4 Sensoryhistory matrix, controlled for reward and choice.
Similar to Extended Data Fig. 2, except that in this plot only trials for which the previous trial resulted in the same action and reward status are included. Therefore, modulation by previous trial cannot be due to action history or reward history. Trial numbers are similar to those in Extended Data Fig. 3.
Extended Data Figure 5 Shortterm and longterm sensory history, and estimating the optimal window of 〈s〉.
a, Slopes from linear fits to the percentage leftward bias from nback trials (n = 1–7, as in Fig. 2a where n = 1 was used), and also 〈s〉 which is a window of 17 trials, from n = 4 to n = 20, in grey. Each point shows the mean of the slope values over n = 25 rats. Error bars show 95% confidence intervals. b, For each rat the optimal exponential window over the past trials was estimated such that it would maximize the crossvalidation bit/trial measurement. Two models are compared here: green shows the distribution of τ values from a model that has five regressors to account for the sensory history—the first and second stimulus from the two trials back and a separate exponential window over the remaining past trials (Fig. 2d). The results shown in orange are from a model containing only one regressor: a single exponential window over all the past trials accounts for the sensory history. In the singleexponential model, the bestfit value of τ is very small, practically as if only past one or two trials back are inducing most of the effect. c, The fiveparameter model of sensory history outperforms the singleexponential model. Two hundred iterations of fivefold cross validation were used to calculate the crossvalidated bit/trial (see Methods). Accordingly, each bar shows the mean of n = 1,000 data points. Error bars denote s.d.
Extended Data Figure 6 Model comparison.
a, Model comparisons, 200 runs of fivefold cross validation were performed, on data from each rat, in order to find the best fit parameters and to compare different model fits using the crossvalidated bit/trial quantity defined as the relative value of the log likelihood of each model, to the null log likelihood, normalized in log2. Removing one parameter by constraining the regression weights on the s_{a} stimulus of the current trial plus the weights on previous sensory stimuli to add to 1 (constrained model, in red) improved performance on crossvalidated data compared to the unconstrained model (in black). A total of 12 different variants of the model are compared. Regressors are described in the box. b, Mean value of crossvalidated bit/trial for different variants of the model as in a, over n = 20 rats. Error bars show s.e.m. Unconstrained models are shown in black, constrained models are shown in red. c, Top, raster plots of versus (t = 1, 2, from model 9). Each dot represents a subject. Pearson correlation values (r), and corresponding twosided P values are shown for each plot. Bottom, median value of and (t = 1, 2), across rats. Error bars show median absolute deviation. d, Similar to c, for human subjects (auditory and tactile tasks are pooled together). Similar to rat subjects, model 9 shows the best performance for human subjects as well (data not shown). e, To compare the sensoryhistory matrix from the real data to the ones predicted from the best model fits (Fig. 2f, g), Frobenious distance norm was used, defined as the square root of the sum of the absolute squares of the difference between elements of two matrices. Frobenious distance is a measure of similarity, and the smaller the value, the more similar the two matrices. Frobenious distance is calculated separately for individual rats and each bar shows its mean value over n = 20 rats. Error bars show s.e.m. Models are models A–F from Fig. 2e. f, Scatter plot of slopes from linear fits to percentage leftward bias (Fig. 2a) versus shortterm sensory history (that is, sum of weights for , , and ) from model 9. This plot shows significant correlation between the two measurements (Pearson correlation, r = −0.66, twosided P = 0.0084, n = 17 rats), suggesting that when our logistic fit coefficients are particularly large, the subjects also have a particularly large contraction bias. g, Examining the weights in regression model 9, which is determined to be the best model, shows that the weights for sensoryhistory terms are significantly larger than those for the correctside history term (pairedsample ttest, P < 0.0001, n = 22 rats). Data from individual rats are used to fit the model and bars show the mean value of sensoryhistory weights (in blue), and correctside history weight (in green), over fit values from n = 22 rats. Error bars show s.e.m. Moreover, the sensory history regressor term, that is, sum of sensoryhistory weights × regressors produces larger variance over trials (0.38) compared to the correctside regressor (0.11), indicating a bigger impact on trialbytrial behaviour.
Extended Data Figure 7 Physiological and histological confirmations.
a, Physiological confirmation of optogenetic inactivation effect in an anesthetized rat. Left, single trace of acute extracellular activity of an example cell in the PPC, expressing eNpHR3.0, is shown in response to light stimulation. Laser illumination period (8 s) is marked by the light green bar. Right, rasterplot for 32 trials, for variable durations of light stimulation. The green vertical dashed line indicates the start of the laser illumination. The laser was on for variable durations of 750, 1,500, 3,000, 6,000 or 8,000 ms. The laser turning off is indicated by the vertical red dashed line. Recordings continued for 2 s after the laser was turned off. b, Histological localization of electrodes targeting the PPC. The inset shows an example of electrode locations in a coronal slice at anteroposterior = 3.48 from the bregma. In all cases, the electrode and fibre placements in the PPC were within between 2.8 and 4 mm posterior the bregma and between 2 and 3.5 mm lateral to the midline. Atlas panel is taken from Paxinos and Watson, 2004 (ref. 31).
Extended Data Figure 8 Optogenetics: PPC inhibition reduces leftward bias owing to past sensory stimuli.
a, Sensoryhistory matrix and leftward biases due to past sensory stimuli, similar to Fig. 2a–c, but now for three types of trials: laseroff trials (two leftmost panels) that consist of trials with no PPC inactivation on either the current or the previous trial; laseron trials (two middle panels) that consist of trials with PPC inactivation on the current trial; and laseroffafterlaseron trials (two rightmost panels) that consist of trials immediately after the laseron trials. This last set controls for number of trials, as it contains equal numbers of trials to the laseron condition. Modulation along the vertical indicates a previous trial effect behavioural bias as a function of the stimuli of the previous trial, for trials for which rats went left, and were rewarded, therefore history of reward and choice is held fixed. Grey lines are different current trial (s_{a}, s_{b}) pairs, the black line is the average over pairs. b, Similar to a, for trials for which rats went right and were rewarded. c, Similar to a for all combinations of current and previous stimuli.
Extended Data Figure 9 Optogenetics: impact on contraction bias on the full stimulus set, individual data points and best fit parameters for nonsensoryhistory weights.
a, Stimulus set and performance during optogenetic inhibition sessions, averaged over 37 sessions from 3 rats (delay interval of 2 s). Trials are grouped based on laseroff (left) and laseron (right) conditions. The boxes represent the set of (s_{a}, s_{b}) pairs used in a session, with the colour representing the percentage that went left and the numbers above each box indicating the percentage correct. The plot in the bottom shows the difference between laseroff and laseron conditions, with positive values indicating improved performance in laseron conditions and negative values indicating impaired performance. b, c, Similar to Fig. 3d–f, with all data points overlaid on the bar plots. For b, n = 37 for each bar plot (equal to the total number of inactivation sessions); for c, n = 600, from 200 iterations of threefold crossvalidation data; *P < 0.01 from one sided ttest. d, Bestfit parameter values for all weights from the nineparameter model (shortterm sensoryhistory model, constrained version, Fig. 2d, e). Values are plotted as their mean once the average value from the laseroff condition is subtracted. Except for the sensory history, none of the other weights were significantly affected by optogenetic inactivation of the PPC. Error bars show s.d. (n = 600, 200 iterations of threefold crossvalidation; *P < 0.01 from one sided ttest). e, Similar to d, for periodselective optogenetic inhibitions, in which the PPC is selectively inhibited during the first stimulus s_{a} (left), delay interval (middle) or second stimulus s_{b} (right).
Extended Data Figure 10 Mutual information.
a, Sensoryhistory coding, one trial back, population analysis, each row represents the time course of significant values of mutual information between the firing rate of a cell and the stimulus pair (s_{a}, s_{b}) presented on the previous trial. Data from all trials with variable delay duration (minimum of 2 s) were pooled and plots are aligned to the beginning of s_{a}. Data from n = 5 rats, and only cells with significant values of mutual information values are included. When estimating the mutual information, spurious information values can be attributed to the inherent correlations between task parameters, such as sensory stimuli and choice. To overcome this, conditional mutual information was calculated only when trials with same previous choice and reward status were considered, and sensory inputs were the only variable. Left, on the previous trial rats went right and were rewarded. Right, on the previous trial rats went left and were rewarded. b, Sensoryhistory coding, one trial back, percentage of cells with significant coding of stimuli presented on the previous trial (trial i − 1), aligned to the start of trial i. Only trials with a delay interval larger than 4 s are included in this analysis. c, Sensoryhistory coding, two trials back, percentage of cells with significant coding of stimuli presented two trials in the past (trial i − 2), aligned to the start of trial i. Shaded horizontal areas show the mean ± s.d. of the percentage of neurons with significant mutual information (MI), calculated from random sets built by shuffling the firing rates of neurons and conditions. d, Percentage of cells with significant coding of a rat’s choice and reward status, on both the current trial (solid lines) and previous trial (dashed lines), when time is aligned to the current trial, either s_{a} (left), or s_{b} (right). Shaded horizontal areas show the mean ± s.d. of the percentage of neurons with significant mutual information, calculated from random sets built by shuffling the firing rates of neurons and conditions. e, In the standard stimulus set (Fig. 1b, (s_{a}, s_{b}) pairs along the diagonal lines), knowledge of the rat’s choice of side, whether it was rewarded or not, and one of either s_{a} or s_{b} enables unique identification of the other stimulus (s_{b} or s_{a}). Therefore, in order to probe whether neurons carried information for different values of s_{a} itself (as opposed to a combination of choice, reward and s_{b}), we ran recording sessions with psychometric stimuli added to the standard stimulus set (top left). In this way, three different values of s_{a} are assigned to one fixed value of s_{b} and one fixed action (left in different shades of red, and right in different shades of blue). The firing rate of an example neuron is shown in response to different values of s_{a}, only for trials in which the rat responded by going left (middle graph) or right (right graph) after the ‘go’ cue, was rewarded, and the delay interval was 4 s. Even though choice, reward and s_{b} are fixed, firing rates clearly differentiate values of s_{a}. The bottom graph shows a summary of population analysis from psychometric recording sessions (as in the examples in the graphs above), showing the percentage of cells with significant coding of s_{a} from trial i (red) or trial i − 1 (blue, n = 142 cells). Shaded horizontal areas show the mean ± s.d. of the percentage of neurons with significant mutual information, calculated from random sets built by shuffling the firing rates of neurons and conditions.
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Akrami, A., Kopec, C., Diamond, M. et al. Posterior parietal cortex represents sensory history and mediates its effects on behaviour. Nature 554, 368–372 (2018). https://doi.org/10.1038/nature25510
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