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Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation

Nature Neuroscience (2024)Cite this article

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Abstract

Reliable execution of precise behaviors requires that brain circuits are resilient to variations in neuronal dynamics. Genetic perturbation of the majority of excitatory neurons in HVC, a brain region involved in song production, in adult songbirds with stereotypical songs triggered severe degradation of the song. The song fully recovered within 2 weeks, and substantial improvement occurred even when animals were prevented from singing during the recovery period, indicating that offline mechanisms enable recovery in an unsupervised manner. Song restoration was accompanied by increased excitatory synaptic input to neighboring, unmanipulated neurons in the same brain region. A model inspired by the behavioral and electrophysiological findings suggests that unsupervised single-cell and population-level homeostatic plasticity rules can support the functional restoration after large-scale disruption of networks that implement sequential dynamics. These observations suggest the existence of cellular and systems-level restorative mechanisms that ensure behavioral resilience.

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Fig. 1: Song degradation and recovery after selective large-scale perturbation of projection neurons.
Fig. 2: Dynamics of song degradation and recovery after large-scale perturbation of HVC projection neurons.
Fig. 3: Substantial song recovery without practice.
Fig. 4: Songs continue to recover rapidly during the postprevention period.
Fig. 5: Population-level homeostatic plasticity and recruitment of silent neurons in a network model contribute to the recovery of sequential activity.
Fig. 6: Saltatory recovery of syllable duration.

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Data availability

All derived data in this study are included in this article. Raw datasets are publicly available online at https://doi.org/10.22002/dvhsa-h5s72 (ref. 72) or by contacting the corresponding authors. Source data are provided with this paper.

Code availability

Custom codes associated with this study are publicly available (for behavior analysis, https://doi.org/10.5281/zenodo.10823142 (ref. 73); for modeling, code is available on GitHub at https://github.com/davidgbe/unsupervised_restoration_modeling (ref. 74) and on Zenodo at https://doi.org/10.5281/zenodo.10823218 (ref. 75)).

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Acknowledgements

This work was supported by National Institutes of Health grant R01 NS104925-01 (C.L. and A.L.F.). We thank R. Pang for contributing to the code used to run simulations. We thank F. Lagzi for valuable discussions. This work was facilitated through the use of advanced computational, storage and networking infrastructure provided by the Hyak supercomputer system and funded by the Student Technology Fee at the University of Washington.

Author information

Author notes

  1. These authors contributed equally: Bo Wang, Zsofia Torok, Alison Duffy.

  2. These authors jointly supervised this work: Tarciso A. F. Velho, Adrienne L. Fairhall, Carlos Lois.

Authors and Affiliations

  1. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Bo Wang, Zsofia Torok, Shelyn Wongso, Tarciso A. F. Velho & Carlos Lois

  2. Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA

    Alison Duffy & Adrienne L. Fairhall

  3. Computational Neuroscience Center, University of Washington, Seattle, WA, USA

    Alison Duffy, David G. Bell & Adrienne L. Fairhall

  4. Department of Physics, University of Washington, Seattle, WA, USA

    David G. Bell & Adrienne L. Fairhall

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Contributions

All authors contributed to the conceptualization, methodology, and writing of the paper. Experimental investigations were undertaken by B.W., Z.T., S.W., T.A.F.V. and C.L. Computational investigations were carried out by A.D. and D.G.B. Visualization was performed by B.W., A.D. and D.G.B. Funding acquisition and supervision was done by A.L.F. and C.L.

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Correspondence to Bo Wang or Carlos Lois.

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Extended data

Extended Data Fig. 1 Specific infection of HVC projection neurons by LVs.

(a) Confocal image of a brain slice from a bird injected with LVs, showing the expression of the transgene (tagged with GFP) in HVC neurons. HVC(RA) neurons are labeled by a fluorescent retrograde tracer (cholera toxin b - alexa 555) injected into RA; (b) Confocal images of a brain slice showing that LV selectively target projection neurons, where only 1/1000 of the cells labeled by LV transgene (tagged with GFP, seen in green) overlap the immunofluorescent signal of pooled antibodies against some of the standard markers of inhibitory neurons (PV, parvalbumin/CB, calbindin/CR, calretinin, seen in red and blue), N = 2 animals; (c) Example of serial sagittal slices of HVC, marked by their distance relative to the most lateral side of HVC, showing the efficient labeling of HVC neurons by LV, except for the most medial posterior corner (as demonstrated by the +1300 and +1400 μm images); (d) Confocal images showing the expression of the viral-delivered transgene (tagged with GFP) in the majority of projection neurons (retrogradely labeled with fluorescent tracer, FluoroGold/cholera toxin b, injected into RA and X, respectively); (e) Histogram indicating the percentage of GFP-labeled cells among all projection neurons in HVC. Each pair of bars is generated from one animal. N = 3 animals. Error bars indicate 95% confidence interval. Numbers in each bar are number of identified GFP positive cells divided by total number of cells counted.

Extended Data Fig. 2 Expression of NaChBac in HVC(RA) neurons.

(a) Schematic drawing showing whole-cell patch clamp recordings made in HVC(RA) neurons infected with LV-NaChBac or naive control; (b) Example traces of whole-cell NaChBac currents evoked by depolarizing voltage steps (from −80 to +30 mV. Increment, 10 mV) recorded from HVC(RA) neurons; (c) I-V curve of the NaChBac current, illustrating the peak amplitude of whole-cell current at different step voltages; (d) Comparison of the maximal peak amplitude of whole-cell NaChBac currents recorded at 5 dpi (3.6 ± 0.6 nA) vs. 21–28 dpi (3.0 ± 0.3 nA). Student’s t-test. Sample size is shared between panels b and c, N = 10 cells / 3 animals (control), 15 / 2 (5 dpi), and 19 / 3 (21–28 dpi); (e) Example of current traces with mIPSC events recorded in HVC(RA) neurons expressing NaChBac at different times after injection. The bar called ‘Degraded’ illustrates currents at 5 dpi. The bar called ‘Recovered’ illustrates currents at 25–35 dpi; (f) Group data of the frequency and amplitude of mIPSCs in HVC(RA) NaChBac+ cells. mIPSC frequency: Control, 2.7 ± 0.2 s−1, N = 23 / 4; Degraded, 6.1 ± 0.7 s−1, N = 17 / 3; Recovered, 7.2 ± 0.7 s−1, N = 16 / 5; (g) Example of current traces with mEPSC events recorded in HVC(RA) neurons expressing NaChBac at different time points; (h) Group data of the frequency and amplitude of mEPSCs in HVC(RA) NaChBac+ cells. mEPSC frequency: Control, 9.7 ± 1.6 min−1, N = 22 / 4; Degraded, 6.8 ± 0.9 min−1, N = 14 / 5; Recovered, 5.9 ± 0.9 min−1, N = 17 / 4. mEPSC amplitude: Control, 17.2 ± 0.9 pA; Degraded, 18.5 ± 0.9 pA; Recovered, 16.1 ± 0.5 pA. One-way ANOVA & student’s t-test; (i) Confocal images of an HVC slice stained with antibodies against GFP and EGR-1, an immediate early gene whose expression is induced by singing related activity. Scale bar, 25 μm. Scatter plot in the bottom right represents the percentage of GFP-positive cells that are co-expressing EGR-1. Each dot represents data from one bird. N = 3 (4 dpi) and 4 (28 dpi) animals. Student’s t-test. Error bars represent SEM.

Source data

Extended Data Fig. 3 Expressing TeNT in HVC(RA) cells blocked their synaptic output.

(a) Example traces from a dual patch clamp recording in control animals, showing that when action potentials were evoked in HVC(RA) neurons (upper), excitatory postsynaptic currents (EPSCs) can be reliably detected in the connected interneuron (below); (b) Example traces similar to those in panel a, showing that no EPSC could be detected in the interneuron when the HVC(RA) neuron was expressing TeNT; (c) Summary of dual patch clamp results. The amplitude of EPSCs are shown in the scatter plot. The block of synaptic transmission by TeNT expression is long-lasting, as it could be detected even >3 weeks after viral injection, after the song had already recovered; (d) Confocal image showing axon bundles from HVC and their terminal branches within RA labeled by LV-TeNT (tagged by GFP); (e) To evoke post-synaptic responses in RA neurons, a bipolar stimulation electrode was placed in the vicinity of the axon bundles going into RA. Under DIC microscopy, RA is clearly distinguishable by strong phase contrast against the surrounding region. Scale bar, 100 μm; (f) Whole-cell patch clamp recordings were made in RA neurons. Example membrane potential traces in response to depolarizing and hyperpolarizing current steps recorded from one projection neuron in RA (RA(PN)); (g) In RA(PN) neurons, inward post-synaptic current transients were evoked by briefly stimulating axon bundles from HVC with varying intensity, measured by amplitude of stimulus current. Compared with naïve animals (black traces on top), the responses were much smaller in RA(PN) neurons recorded in birds with LV-TeNT in HVC (green traces below); (h) Summarized data of the synaptic responses in RA(PN) neurons. Stimulus – response curves were composed with data obtained at three different time points. The central marks of box plots indicate the medians, the bottom and top edges indicate the 1st and 3rd quartiles, and the whiskers indicate the most extreme data points that are still within the 1.5 times of the inter-quartile-region beyond the lower and upper quartiles. Outliers are individually marked. N = 11 cells / 3 animals (Control), 15 / 3 (2 dpi), and 12 / 1 (21–28 dpi).

Source data

Extended Data Fig. 4 Songs degradation and recovery was not due to mechanical lesion or inflammation.

(a) Example spectrograms of songs from a bird injected with LV-NaChBac(EtoK), a dead-pore mutant of NaChBac; (b) Distribution of syllable durations per day of the same bird shown in panel a; (c) Plots of scaled acoustic distance to original syllables. Data was from the same bird as in panels a and b. The insets are UMAP visualizations of songs at selected time points. Dashed lines are generated from the syllables of the bird injected with LV-NaChBac shown in Fig. 1d, and for comparison here they were not normalized to maximum. Experiments with LV-NaChBac(EtoK) yielded consistent results, N = 4 animals; (d) Example spectrograms of songs from a bird injected with half of the volume of LV-NaChBac used for animals shown in Fig. 1; (e) Distribution of syllable durations per day of the same bird shown in panel d; (f) Plots of scaled acoustic distance to original syllables. Data was from the same bird as in panels d and e. Each line/color represents one syllable. The insets are UMAP visualizations of songs at selected time points. Experiments with reduced volume of LV-NaChBac were repeated, N = 2.

Extended Data Fig. 5 Recovery of fine intra-syllable structure without practice.

(a-d) Individual curves of time-varying acoustic features, including mean frequency (a), entropy (b), pitch (c), and goodness of pitch (d), generated from the same syllable as shown in Fig. 3g&h. Each black curve represents one rendition of the syllable, and the red curve represents the mean. The population of renditions shown are the 2.5% of renditions closest to the original syllable cluster in a given day. (e-j) More example syllables to show intra-syllable acoustic structures on the first day after song prevention was lifted. Four time points were selected for comparison, pre-perturbation, pre-prevention, first day postprevention, and when songs were fully recovered. Spectrograms (on the left in each panel) and averaged time-varying acoustic features (right) of each corresponding syllable were plotted in the same way as Fig. 3e–h. All spectrograms share the same time scale. Note that, on the first day after song-prevention (postprevention), clear syllable renditions were sung and the intra-syllable timing of acoustic feature fluctuations were very similar to pre-perturbation syllables, though differences in acoustic feature magnitudes were still evident and the rendition-to-rendition variability was higher. Shading represents standard error of mean.

Extended Data Fig. 6 Recovery of syllable durations without practice.

(a) The distribution of durations of one example syllable, same as the one shown in Fig. 3g&h, before perturbation and right after song prevention (bin size, 10 ms). Note that they both peak at the same value. Two syllable-selection methods were used (see the Methods), with or without dynamic time warping, in order to test whether the k-nearest neighbor measure was missing dilated or compressed syllable renditions. The two syllable-selection methods found highly similar duration distributions; (b) Comparison of the mode of syllable durations before perturbation and post-prevention. Most syllables recovered their duration modes with high precision without song practice. Dashed lines indicate second peak of dual peak distribution; (c) Comparison of the standard deviation of duration distributions demonstrates that the variability of syllable durations was still elevated the first day post prevention. Comparable results were obtained with either type of syllable-selection method. In a-c, we performed this analysis on 2/3 birds in the song prevention experiment. In the third bird, the motif was not identifiable the first day post-song prevention, making it not feasible to construct individual syllable duration distributions on this day. One potential reason the song recovery trajectory differed is that this bird’s song was abnormally variable before the viral perturbation and song prevention experiment. We include this bird (V449) in the data and analyses presented in Fig. 4b–e where the pre-perturbation variability and the post-prevention lag in recovery are clear. N = 8 syllables / 2 animals for both panel b and c.

Extended Data Fig. 7 Unperturbed cells did not change their intrinsic excitability or inhibitory synaptic inputs.

(a) (Left) Whole-cell patch clamp recordings were made in GFP-negative HVC(RA) neurons in naive control birds or birds injected with LV-TeNT. (Right) Membrane potential and firing pattern of HVC(RA) neurons in response to current steps; (b) Group data showing that no significant difference was found in the resting membrane potential (−75.2 ± 0.8 vs. −74.8 ± 0.8 mV), input resistance (414.8 ± 30.0 vs. 416.2 ± 33.1 MOhm), or initial F-I slope (437.9 ± 35.5 vs. 406.2 ± 37.3 Hz/A) between neurons in naive control (N = 22 cells / 3 animals) and in birds with LV-TeNT for more than 25 days (N = 30 / 4). Student’s t-test; (c) F-I curves obtained from HVC(RA) neurons showed no difference between control or animals recovered from LV-TeNT. Sample size is the same as that in panel b; (d) Group data of mIPSC recorded in HVC(RA) neurons, showing no significant difference at any time during the experiment. ‘Degraded’ indicates the time when the song was degraded, at 5 dpi. ‘Recovered’ indicates the time after the song had fully recovered, at 25 dpi. mIPSC frequency: Control, 2.7 ± 0.2 s−1, N = 23 cells / 4 animals; ‘Degraded’ GFP-negative cell, 2.6 ± 0.4 s−1, N = 16 / 3; ‘Degraded’ GFP-positive, 2.3 ± 0.4 s−1, N = 16 / 3; ‘Recovered’ GFP-, 2.6 ± 0.5 s−1, N = 9 / 3; ‘Recovered’ GFP+, 3.1 ± 0.8 s−1, N = 9 / 3. mIPSC amplitude: Control, 37.9 ± 2.0 pA; ‘Degraded’ GFP-negative cell, 40.6 ± 1.7 pA; ‘Degraded’ GFP-positive cell, 43.2 ± 1.6 pA; ‘Recovered’ GFP- negative cell, 39.0 ± 3.7 pA; ‘Recovered’ GFP- positive cell, 41.4 ± 5.4 pA. One-way ANOVA. Error bars represent SEM.

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Extended Data Fig. 8 Synaptic changes in unmanipulated neurons only occurred in the injected hemisphere.

(a) Example song spectrograms of a bird with LV-TeNT in one side of the two HVCs; (b) Acoustic distance to each original syllable when LV-TeNT was injected bilaterally (left) or unilaterally (right). Curves in the same color are from the same bird. Bilateral trajectories are similar to those shown in Fig. 2f but without normalization; (c) Comparison of the peak distortion, measured by the maximal distance from each original syllable, between unilateral or bilateral LV-TeNT perturbations. Each column represents syllables from the same bird. N = 24 syllables (nested) / 4 animals for Bilateral and 20 (nested) / 3 for Unilateral. Nested two-way ANOVA; (d) After unilateral LV-TeNT perturbation, the day when each syllable reached peak distortion compared with bilateral LV-TeNT (triangles. On average, 2.75 ± 0.46 dpi. Nested two-way ANOVA) and the day when each syllable achieved more than 90% recovery (round dots. on average, 17.82 ± 2.49 dpi, later than that for bilateral LV-TeNT, 13.71 ± 0.72 dpi, nested two-way ANOVA). Sample size is the same as that in panel c; (e) Whole-cell recordings were made in GFP-negative HVC(RA) (‘unmanipulated’) neurons and interneurons in the injected and unperturbed hemispheres of birds with unilateral LV-TeNT injection; (f) Group data of mEPSC in naive control birds and birds with unilateral LV-TeNT. (Middle) Cumulative curve of inter-event intervals of mEPSCs. Dashed line represents data from GFP- neurons from animals with bilateral LV-TeNT injection for comparison and was adapted from Fig. 5c. mEPSC frequency: Naive control, 9.7 ± 1.6 min−1, N = 23 cells / 4 animals; Contralateral (uninjected HVC), 9.8 ± 2.3 min−1, N = 16 / 4; Ipsilateral (injected), 22.8 ± 3.3 min−1, N = 18 / 3. mEPSC amplitude: Naive control, 17.2 ± 0.9 pA; Contralateral, 18.2 ± 0.9 pA; Ipsilateral, 22.3 ± 1.0 pA. One-way ANOVA followed by t-test (without adjustment); (g) Group data of mEPSC recorded in interneurons. (Left) The frequency of mEPSC in interneurons decreased after virus injection, but eventually recovered to a level comparable to that of controls. (Middle) Cumulative curve of inter-event intervals of mEPSCs. mEPSC frequency: Naive control, 1.47 ± 0.14 s−1, N = 19 / 2; Degraded contralateral, 1.56 ± 0.16 s−1, N = 13 / 3; Degraded ipsilateral, 0.63 ± 0.14 s−1; N = 20 / 3; Recovered contralateral, 1.34 ± 0.19 s−1, N = 18 / 4; Recovered ipsilateral, 1.45 ± 0.15 s−1, N = 20 / 4. mEPSC amplitude: Naive control, 38.3 ± 1.2 pA; Degraded contralateral, 38.4 ± 2.6 pA; Degraded ipsilateral, 35.5 ± 1.9 pA; Recovered contralateral, 37.3 ± 2.1 pA; Recovered ipsilateral, 33.2 ± 2.0 pA.One-way ANOVA & student’s t-test. Error bars represent SEM.

Source data

Extended Data Fig. 9 Recovery of modeled sequences with STDP.

(a) Schematic showing triplet STDP implemented both in E→E and E→I synapses. Potentiation is mediated by triplets of spikes; depression is dictated by a pairwise rule. See Methods for details; (b) Spike raster plots showing the sequential dynamics generated by HVC neurons before and after perturbation with only STDP and downward firing rate homeostasis implemented (see Eq. 2 in Methods). Colors represent the firing timing of each cell before perturbation. Note that the sequence regenerates serially and all cells recapture their original firing timing; (c-e) Percentage of syllables completed prior to (pre-perturbation), just after (perturbed), and 3000 renditions after (recovered) perturbation for networks with (c) STDP alone, (d) STDP plus Poisson inputs, (e) or firing rate homeostatic plasticity, for 30% to 70% perturbations in increments of 10%. Note that networks with firing rate homeostatic mechanisms possessed larger recoverable regimes.

Extended Data Fig. 10 Recovery of modeled sequences with population-level homeostatic plasticity.

(a-d) are generated from networks with STDP and a downward, single-cell, firing rate homeostasis rule implemented; (e-h) are generated from networks with STDP and 2-sided, single-cell, firing rate homeostasis; (i-l) are generated from networks with silent cells and population-level homeostasis added to rules in (a-d); (a,e,i) Percentage of modeled syllables that successfully completed following >80% of activations in three time windows (before perturbation, perturbed and recovered) in response to different degrees of perturbation (10, 25, and 50%), when different sets of plasticity rules were implemented. Each point represents a network. Lines represent averages over all networks (N = 15); (b,f,j) The normalized total firing activity of all functional HVC(RA) (left) or interneurons (right) plotted against renditions. Solid curves show the average of multiple networks (N = 15) and shading represents SEM. RPP, renditions post perturbation; (c,g,k) Normalized total excitatory synaptic input into each unmanipulated HVC(RA) neuron after perturbations. Note that with population-level plasticity and silent neurons, an increase in E → E weight over its pre-perturbation value was introduced; (d,h,l) Distributions of single-cell firing rates per rendition for HVC(RA) (top) and interneurons (bottom) before and following 50% perturbation. Pre-perturbation distributions in black or gray. Lines represent mean and shadings represent standard error of mean, N = 15.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7.

Reporting Summary

Supplementary Audio 1

Example song of bird V287 before perturbation. Spectrogram is shown in Fig. 1d, preperturbation.

Supplementary Audio 2

Example song of bird V287 with LV-NaChBac. Spectrogram is shown in Fig. 1d, at 6 dpi.

Supplementary Audio 3

Example song of bird V287 after recovery. Spectrogram is shown in Fig. 1d, at 25 dpi.

Supplementary Audio 4

Example song of bird V405 before perturbation. Spectrogram is shown in Fig. 1f, preperturbation.

Supplementary Audio 5

Example song of bird V405 with LV-TeNT. Spectrogram is shown in Fig. 1f, at 4 dpi.

Supplementary Audio 6

Example song of bird V405 after recovery. Spectrogram is shown in Fig. 1f, at 25 dpi.

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Source Data Extended Data Fig. 2/Table 2

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Source Data Extended Data Fig. 3/Table 3

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Source Data Extended Data Fig. 7/Table 7

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Wang, B., Torok, Z., Duffy, A. et al. Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation. Nat Neurosci (2024). https://doi.org/10.1038/s41593-024-01630-6

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