Affective memory rehearsal with temporal sequences in amygdala neurons

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Abstract

Affective learning and memory are essential for daily behavior, with both adaptive and maladaptive learning depending on stimulus-evoked activity in the amygdala circuitry. Behavioral studies further suggest that post-association offline processing contributes to memory formation. Here we investigated spike sequences across simultaneously recorded neurons while monkeys learned to discriminate between aversive and pleasant tone–odor associations. We show that triplets of neurons exhibit consistent temporal sequences of spiking activity that differed from firing patterns of individual neurons and pairwise correlations. These sequences occurred throughout the long post-trial period, contained valence-related information, declined as learning progressed and were selectively present in activity evoked by the recent pairing of a conditioned stimulus with an unconditioned stimulus. Our findings reveal that temporal sequences across neurons in the primate amygdala serve as a coding mechanism and might aid memory formation through the rehearsal of the recently experienced association.

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Fig. 1: Experimental setup and structure of spatiotemporal sequences in the amygdala.
Fig. 2: Consistent amygdala sequences throughout time and within valence.
Fig. 3: Sequence-based decoding and information in the post-trial epoch.
Fig. 4: Valence-specific post-trial sequences are repetitions of sequences that occurred during CS–US presentations.
Fig. 5: ME models support structure, consistency, coding and rehearsal in triplets.

Data availability

All data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Custom code for behavioral and electrophysiological tests is available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thank Y. Kfir, A. Taub, U. Livneh, Y. Cohen, K. Aberg, E. Schneidman and E. Karpas for scientific consultations. The authors thank Y. Shohat for animal training, experiments and welfare, E. Kahana for medical and surgical procedures, and E. Furman-Haran and F. Attar for MRI procedures. This work was supported by ISF no. 2352/19 and ERC-2016-CoG no. 724910 grants to R.P.

Author information

T.R.-S. and R.P. conceived and designed the experiments. T.R.-S. planned and performed the analyses. T.R.-S. and R.P. wrote the manuscript.

Correspondence to Rony Paz.

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The authors declare no competing interests.

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Extended data

Extended data Fig. 1 Structure in triplets (sequences of three-spikes from three neurons).

(A) Circular shuffling illustration. Shuffling was performed on 10s activity segments. Activity of two randomly chosen units (to control for single neuron activity) was circularly shifted by a random lag between 150–300 ms, one left and the other right. The spiking structure of each neuron, and firing rate correlations beyond 300–600 ms, are preserved, yet the precise spike time correlation between the neurons is destroyed. (B) Structure analysis illustration. Right top: Triplet 177 sequence probability distribution (log scale). Right bottom: Examples of triplet 177 shuffled data sequence probability distributions (two shuffled units). Left: data distribution (front) and 30 examples of shuffled data distributions. Structure was examined by calculating (1) the mean Jensen-Shannon-divergence (JSD) dissimilarity between sequences of the data and each of the shuffled data (\(\bar D_{data}\), blue arrow, comparing right top and left distributions), and (2) the mean JSD dissimilarity between sequences of each shuffled data instance and all other instances (\(\bar D_{shuffle}^{\left\{ {1, \ldots n} \right\}}\), green arrow, comparing right bottom and left distributions). If the dissimilarity between the data and the shuffled data is larger than the dissimilarities between the shuffled data sets, it suggests that the structure of the sequence distribution is different than expected from single neuron activity. Shaded area: s.e.m.

Extended data Fig. 2 Locality of sequence activity.

(A) Average maximal physical distance between the three electrodes (taking only triplets recorded from three different electrodes) for triplets exhibiting sequence structure (n=24) and triplets with no structure (n=80). Dots represent maximal distance between electrodes of a single triplet. Structured triplets were physically closer than non-structured triplets (one tailed Wilcoxon rank sum: Z=−2.47, p=0.007, rrb=0.24). (B) Average maximal physical distance between the three electrodes (taking only triplets recorded from three different electrodes) for consistent triplets (n=16) and triplets with no significant consistency (n=88). Dots represent maximal distance between electrodes of a single triplet. Consistent triplets were physically closer than non-consistent triplets (one tailed Wilcoxon rank sum test: Z=−3.1, p < 0.001, rrb = 0.3). Error bars: s.e.m. **p<0.01.

Extended data Fig. 3 Consistency in sequences.

Top: Triplet 177 estimated sequence probability distribution in two subdivisions of the time period (left and right, sequence order is as in Fig. 2a left). Bottom: Shuffled data sequence probability distributions of the same subdivisions. Consistency was examined by calculating (1) the mean JSD dissimilarity between sequences of the data in the two subdivisions, for 100 subdivisions (\(\bar C_{data}\), blue arrow, comparing left and right top panels), and (2) the mean JSD dissimilarity between sequences of the data of one half and all shuffled instance of the other half (\(\bar C_{shuffle}^{\left\{ {1, \ldots n} \right\}}\), green arrows, comparing (a) left top and right bottom panels and (b) right top and left bottom panels), for 100 subdivisions. If the dissimilarity between the data subdivisions is smaller than the dissimilarities between the data and the shuffled data sets, it suggests that the structure of sequences is consistent beyond expected from single neuron activity. Shaded area: s.e.m.

Extended data Fig. 4 Comparing Amygdala to ACC sequences.

(A) proportion of significantly structured (Benjamini-Hochberg [BH] corrected for multiple comparisons) amygdala (blue, n=355) and dACC (green, n=564) triplets. The proportion was higher in amygdala than in dACC triplets (χ2 test for independence:\({\mathrm{\chi }}_{{\mathrm{df}} = 1}^2 = 20.5,\,\mathrm{p} < 10^{-\mathrm{5}}\)). (B) Violin plots of the structure scores of individual triplets (black dots, only significant triplets). The mean score (black dashed line) of amygdala triplets (blue, n=173) was higher than that of dACC (green, n=189, independent two-tailed t-test, t360 = 4.9, p < 10−5, d=0.51). The colored surface marks the kernel density estimate of the corresponding probability distribution, the thick gray line marks the interquartile range and the white dot marks the median. (C-D) Consistency analysis—arranged as A,B. Higher proportion in amygdala (n=355) than in dACC (n=564): \({\mathrm{\chi }}_{\mathrm{df} = 1}^2 = 30.7,\,{\mathrm{p}} < 10^{ - 7}\); Higher score in amygdala (n=116) than in dACC (n=94): t208=5.9, p < 10−7, d=0.83. Error bars: s.e.m. ***p<0.001.

Extended data Fig. 5 Decoding performance and controls.

(A) The proportion of triplets with significant decoding hit rate (BH corrected, false discovery rate [FDR]≤0.05, blue line) was significantly higher than that of trial-shuffle control (n=250 trial shuffle data set, MC p-value: p=0.024). Inset: triplets from different recording electrodes (p=0.11). (B) The mean decoding hit rate (n=355, 0.56±0.01) across all triplets (blue line) was significantly higher than that of independent trial-shuffle control (turquoise histogram, n=250 trial-shuffle data set, MC p-value: p=0.004). Inset: triplets from different recording electrodes (p=0.004). Note that x axes begin at chance level (0.5). (C) Decoding hit rate for decoding pleasant trials from pre-task activity (purple) and decoding aversive trials from pre-task activity (red) for all triplets that were FDR significant for decoding pleasant from aversive trials (Fig. 3a, n=101). The hit rate for simultaneous recorded activity (data, x-axis) was higher than the hit rate for independent shuffle trial control (shuffle trials, y-axis) for both aversive (one tailed t-test: t70=1.71, p=0.046, d=0.13) and pleasant decoding (t70=3.12, p=0.001, d=0.19). Left top inset: mean hit rate for the data (blue) and trial shuffled data (turquoise). Note that the y axis begins at chance level (0.5). Right bottom inset: Histogram of the difference between data hit rate and trial shuffle hit rate in individual triplets. For both pleasant (purple) and aversive (red) trials, the majority of triplets showed higher hit rate for the data, so that the difference is larger than 0 (dashed black line). (D) Histograms of decoding hit rates of higher-than-chance triplets for sequences (blue, N=76) and for ISI (green, N=130, one tailed independent t-test: t204=1.69, p=0.046, d=0.24). Note that the axes begin at chance level (0.5). In all panels error bars mark s.e.m, *p<0.05;**p<0.01.

Extended data Fig. 6 Maximum entropy (ME) models.

(A) Spatial-ME model fitted to quadruplets of neurons. Spike trains were binned to 50ms non-overlapping bins (top), creating four letters spatial words (right bottom). The full model was trained on spatial correlations and the coefficients of independent rates (hi), pairwise correlations (jij) and triple-wise correlations (mijk) were learned. This model captures a lower dimension of sequence activity, where the temporal proximity of spikes from different neurons is preserved yet the order of spikes is lost due to binning. (B) Sequence-ME model: Spatiotemporal ME model fitted to sequence activity matrices in triplets. Bins with no spiking activity were removed and spike trains were kept at 1ms resolution (top). The full model was trained on two steps temporal activity of three neurons, creating a 3X3 (9 letters) spatiotemporal word (right bottom). The coefficients of independent rates (hi), spatial pairwise correlations (hij), pairwise spatiotemporal correlations \(\left( {j_{i\left( t \right)j\left( {t + 1} \right)},j_{i\left( t \right)j\left( {t + 2} \right)}} \right)\) and triple-wise spatiotemporal correlations \(m_{i\left( t \right)j\left( {t + 1} \right)k\left( {t + 2} \right)}\) were learned. This model captures the order of spikes between triplets of neurons, neglecting time lags between spikes and is similar to the sequence activity defined originally (as in Fig. 1c).

Extended data Fig. 7 Decoding CS-US activity using post-trial activity with ME models.

(A) Hit rate of spatial-ME decoding trained on the post-trial activity and tested on CS-US activity (CS-US hit rate), taking all quadruplets with better-than-chance decoding for the post-trial activity in either model (Fig. 5e, p<0.05, n=198). Main panels: violin plot of the normalized (Z-score) CS-US hit rate of individual quadruplets (black dots) for each model. The colored surface marks the kernel density estimate of the corresponding probability distribution, the thick gray line marks the interquartile range and the dashed black line marks the mean of the normalized CS-US hit rate. The pairwise and triple-wise models (middle and right) showed higher rates than that of the independent (one way repeated measures ANOVA, \(F_{194}^2 = 7.9,\,p = 0.001\), one tailed Tukey HSD, independent-pairwise: \(p < 10^{ - 3},\,d = 0.63\), independent-triple-wise: \(p = 0.02,\,d = 0.49\)), as apparent from the higher means and larger densities in the top part of the two right distributions compared to the left. Left inset: proportion of highest hit rate in quadruplets using decoding based on the independent, pairwise and triple-wise model. Right insets: CS-US hit rate (mean±s.e.m). Note that the y axis begins at chance level (0.5). *p<0.05;**p<0.01; n.s p>0.05. (B) Same as (A) for the sequence-ME, taking triplets with better-than-chance decoding for the post-trial activity in either model (Fig. 5f, p<0.05, n=129). CS-US activity was averaged over 15 trials to ensure sufficient sampling. The triple-wise models (right) showed higher rates than that of the independent (left, \(F_{194}^2 = 3.68,\,p = 0.03\), one tailed Tukey HSD, p=0.03, d=0.44) and pairwise (middle, one tailed t-test t89=1.83, p=0.035, d=0.25), apparent from the higher density in the lower rates of the independent and the higher density in the higher rates of the triple-wise model. Violin elements are as in A.

Extended data Fig. 8 Negligible effect of non-stationary firing rates (FR).

Stationary and non-stationary units were separated by two methods: 1. Two tailed t-test comparing the average firing rate in the first and last 150 seconds of the pre-task activity (FR t-test). 2. Runs-test examining if inter-spike-intervals along the pre-task activity were drawn randomly from a single distribution. Units with significant results (p<0.05) were classified as non-stationary and all other units were classified as stationary. Only triplets with three stationary units were classified as stationary triplets (FR t-test: n=156; runs-test: n=38). (A) There was no significant difference between the proportion of structured triplets in stationary (pink) and non-stationary (blue) triplets as classified by either the FR t-test (left bars, χ2=2.91, p=0.09) or the runs-test (right bars, χ2=0.75, p=0.39), suggesting a negligible effect of stationarity on structure. (B) There was no significant difference between the proportion of consistent triplets in stationary (pink) and non-stationary (blue) triplets as classified by either the FR t-test (left bars, χ2=3.35, p=0.07) or the runs-test (right bars, χ2=0.27, p=0.6), suggesting a negligible effect of stationarity on consistency. (C) Violin plots of the proportion of reduction in entropy by the third-order correlation of the sequence-ME model, normalized by the proportion in pairwise surrogate data (see Fig. 5d and methods). The colored surface marks the kernel density estimate of the corresponding probability distribution, the thick gray line marks the interquartile range and the white dot marks the median. The proportion of reduction in entropy in stationary triplets (pink) was significantly larger than that of non-stationary triplets (blue) as classified by the FR t-test (left violins, two tailed rank sum test, Z=2.36, p=0.02) and there was no difference between these groups as classified by the runs-test (right violins, Z=−1.03, p=0.3), suggesting a negligible (if at all) effect of stationarity on sequence-ME results. (D, E) Reduction in entropy due to the triple-wise correlations of the Sequence-ME model (see main Fig. 5d) in stationary triplets. (D) FR t-test, n=156; (E) runs test, n=38. The proportion of reduction in entropy due to the triple-wise correlations (I3/IN) in the real data (x-axis) was larger than in surrogate data sampled from the pairwise ME distribution (pairwise-surrogate control, y-axis). Black dashed line: identity line. Inset: means and SEM over all stationary triplet. Paired t test between medians across trials; FR t-test for stationarity: t121=18.08, p<10−30, d=1.05; runs test: t20=6.11, p<10−4, d=0.68. In all panels, error bars and shaded area mark s.e.m., *p<0.05; ***p<0.001; n.s p>0.05.

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Reitich-Stolero, T., Paz, R. Affective memory rehearsal with temporal sequences in amygdala neurons. Nat Neurosci 22, 2050–2059 (2019) doi:10.1038/s41593-019-0542-9

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