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Nature Neuroscience | Article

Basal ganglia subcircuits distinctively encode the parsing and concatenation of action sequences

Journal name:
Nature Neuroscience
Volume:
17,
Pages:
423–430
Year published:
DOI:
doi:10.1038/nn.3632
Received
Accepted
Published online
Corrected online

Abstract

Chunking allows the brain to efficiently organize memories and actions. Although basal ganglia circuits have been implicated in action chunking, little is known about how individual elements are concatenated into a behavioral sequence at the neural level. Using a task in which mice learned rapid action sequences, we uncovered neuronal activity encoding entire sequences as single actions in basal ganglia circuits. In addition to neurons with activity related to the start/stop activity signaling sequence parsing, we found neurons displaying inhibited or sustained activity throughout the execution of an entire sequence. This sustained activity covaried with the rate of execution of individual sequence elements, consistent with motor concatenation. Direct and indirect pathways of basal ganglia were concomitantly active during sequence initiation, but behaved differently during sequence performance, revealing a more complex functional organization of these circuits than previously postulated. These results have important implications for understanding the functional organization of basal ganglia during the learning and execution of action sequences.

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  1. Behavioral learning of rapid action sequences in mice.
    Figure 1: Behavioral learning of rapid action sequences in mice.

    (a) Return map of IPIs showing the behavior of the same mouse across the different schedules. Every two consecutive IPIs contribute to one dot in the map. (b) An example of single-session IPI dynamics of the mouse shown in a under the FR4/0.5s schedule. Each dot indicates an IPI. The short (black dots), intermediate (pink dots) and long (red dots) IPIs represent two lever presses performed in a chunk, two lever presses spaced by magazine checking without reward (that is, head entry, no licks followed) and two lever presses spaced by reward consummation (that is, licks), respectively. Note that the first peak of the IPI distribution falls below 167 ms (red dashed line) under the FR4/0.5s schedule. (c) Behavioral microstructure of the same mouse performing under the FR4/0.5s schedule. Each dot indicates a lever press, with the red and black dots representing the first and final press in a sequence, and the blue dots representing intermediate presses. The vertical black dash lines imply the timing of reward. Black and red bars at the bottom indicate the timing of head entries and licks, respectively. Inset shows a rewarded lever press sequence. (d) First, second and third IPI in a sequence changed across different schedules. (e) Coefficient of variance for the first, second and third IPI across different training schedules. (f) Sequence length during training under different schedules. (g) Percentage of ultrafast sequences (FR4/0.5s) throughout training. Error bars denote s.e.m.

  2. Neuronal activity in the dorsal striatum during learning and performance of rapid action sequences.
    Figure 2: Neuronal activity in the dorsal striatum during learning and performance of rapid action sequences.

    (a) PETH of a MSN related to each lever press in each rewarded action sequence under the FR4/1s schedule. Top, each black dot indicates a spike and the orange and red triangle markers indicate lever pressing and reward timing, respectively (same markers are used for all PETHs unless otherwise stated). Bottom, average firing activity of the cell in relation to lever pressing; time zero indicates the time of lever pressing. Left and right blocks of five panels are PETHs from the same cell; the right PETHs are zoomed in to show the fine temporal profile of the cell's activity and the four orange bars on top mark the average timing for each press in sequence. This MSN showed a phasic increase in firing activity selectively before the first lever press of each action sequence. (b) PETH of a MSN showing phasic firing rate increase selectively after the final lever press of each action sequence. (c) MSN showing a decrease in firing rate throughout the whole action sequence. (d) MSN showing sustained firing activity throughout the whole action sequence. (e–g) Statistic results of percentage of MSNs showing start/stop (e), inhibited (f) or sustained (g) sequence-related activity in the striatum across different schedules. (h) Lever press histogram (top) and PETH for an MSN showing sustained activity (bottom), both referenced to the first lever press. The lever press histogram (bottom, red line) was temporally shifted to calculate the correlation with PETH (Online Methods). (i) Percentage of sequence-related MSNs with sustained activity that showed a significant correlation between the PETH and the average lever press rate. Error bars denote s.e.m.

  3. Neuronal activity in the SNr and GPe during learning and performance of rapid action sequences.
    Figure 3: Neuronal activity in the SNr and GPe during learning and performance of rapid action sequences.

    (a) A SNr neuron showing a phasic firing rate increase selectively before the first lever press of each action sequence. (b) A SNr neuron showing inhibited firing activity throughout the whole action sequence. (c) A GPe neuron displaying sustained firing activity throughout the whole action sequence. (d–f) Percentage of SNr (black) and GPe (red) neurons showing start/stop (d), inhibited (e) or sustained (f) sequence-related activity during the performance of action sequences under different schedules. (g) The lever press histogram (top) and the sustained GPe neuron PETH (bottom) both aligned to the first lever press in action sequences. (h,i) Percentage of neurons in SNr (h) and GPe (i) displaying sequence-related sustained activity with a significant correlation between PETH and average lever press rate. Error bars denote s.e.m.

  4. Action- versus speed-specific sequence-related activity in the basal ganglia circuits.
    Figure 4: Action- versus speed-specific sequence-related activity in the basal ganglia circuits.

    (a–c) Percentage of action-specific (black bars) and speed-specific (red bars) start/stop, inhibited and sustained activity in striatum (a), SNr (b) and GPe (c), respectively. Error bars denote s.e.m.

  5. Subcircuit-specific neuronal activity in the basal ganglia during learning and performance of rapid action sequences.
    Figure 5: Subcircuit-specific neuronal activity in the basal ganglia during learning and performance of rapid action sequences.

    (a) A coronal section of dorsal striatum from a D1 Cre mouse with viral driven expression of ChR2-YFP; note axons targeting GPm and SNr. Scale bar represents 1 mm. (b) A coronal section of dorsal striatum from a D2 Cre mouse with viral driven expression of ChR2-YFP; note axons targeting GPe. Scale bar represents 1 mm. (c) Illustration of electrode array and cannula design allowing for adjustable fiber optic stimulation for cell identification. (d) PETH of a MSN recorded in a D1-ChR2 mouse showing sequence start–related activity. (e) The neuron presented in d, as evidenced by identical waveform (black trace during action sequences versus red trace during light stimulations, same for below), showed reliable, short-latency response to blue light stimulation. (f) PETH of a MSN recorded in a D2-ChR2 mouse showing sequence-related inhibited activity. (g) The neuron presented in f showed reliable, short-latency response to blue light stimulation at the end of session. Inset, neuronal response to light stimulation at a fine timescale. (h,i) PETH of a MSN recorded in a D1-ChR2 mouse showing sequence-related sustained activity (h) and its response to light stimulation at the end of the session (i). (j) Distribution of light to response latencies for D1- and D2-MSNs. (k,l) Proportions of striatal D1- and D2-MSNs (k) and SNr and GPe neurons (l) displaying different types of sequence-related activity under FR4/0.5s. (m) Percentage of striatal D1- or D2-MSNs displaying sequence start, stop or boundary-related activity. Error bars denote s.e.m.

  6. The relation between standard deviation (STD) and mean of inter-press intervals (IPIs) under different schedules.
    Supplementary Fig. 1: The relation between standard deviation (STD) and mean of inter-press intervals (IPIs) under different schedules.

    a-c, Disproportionally faster decrease of the standard deviation compared to the mean for IPI1 (a), IPI2 (b) and IPI3 (c) as training progressed. Note that the linear fit line (red, appears distorted under logarithmic scale) deviated from the 1:1 line (black) in all three cases.

  7. Impaired sequence learning, decreased action efficiency and increased pressing speed in RGS9L-Cre/Nr1f/f mutants.
    Supplementary Fig. 2: Impaired sequence learning, decreased action efficiency and increased pressing speed in RGS9L-Cre/Nr1f/f mutants.

    a, b, Example of the behavioral microstructure of of a RGS9L-Cre/Nr1f/f mutant (KO) and littermate control (CT) mouse under the schedule FR4/8s. This mutant (b) exhibited longer lever-press sequences than its littermate controls (a). Each dot indicates a lever press, with red and black indicating the first and final press of each individual sequence. The black and red solid lines on the X axis represent magazine entries and licks, respectively. The black dashed lines indicate the time of reward and corresponding lever press2. c, d, Another example of microstructure of behavior of a RGS9L-Cre/Nr1f/f mutant (d), who exhibited shorter lever-press sequences than littermate controls (c). e-h, Sequence length difference to four (e), action efficiency (f, percentage of lever presses actually rewarded), within-sequence press rate (g) and inter-press interval (h) for RGS9L-Cre/Nr1f/f mutants and controls under FR4/8s. For data and statistics: (e) CT 0.79 ± 0.10 vs. KO 1.50 ± 0.15, t44 = 3.94, P < 0.001; (f) CT 78.1 ± 2.2 % vs. KO 57.7 ± 4.0 %, t44 = 4.82, P < 0.001; (g) CT 109.8 ± 10.2 times/min vs. KO 278.3 ± 27.7 times/min, t44 = 6.76, P < 0.0001; (h) IPI1: CT 1581.1 ± 86.4 ms vs. KO 976.0 ± 135.2 ms, t44 = 3.98, P < 0.001; IPI2: CT 1453.4 ± 88.2 ms vs. KO 1002.3 ± 156.9 ms, t44 = 2.72, P < 0.01; IPI3: CT 1387.0 ± 77.9 ms vs. KO 872.5 ± 107.9 ms, t44 = 3.93, P < 0.001; See Methods for details.

  8. Depiction of electrode placement confirmed by cresyl violet staining.
    Supplementary Fig. 3: Depiction of electrode placement confirmed by cresyl violet staining.

    a-f, The electrode array and placement in dorsal striatum (a), SNr (b) and GPe (c), which were further confirmed by post experimental staining (d-f). Atlas adapted from Paxinos & Franklin 50.

  9. Striatal neurons classification.
    Supplementary Fig. 4: Striatal neurons classification.

    a, The 3-D plot of striatal units, and each dot indicates a single unit. Putative parvalbumin-expressing fast-spiking interneurons (FSIs, red), medium-spiny neurons (MSNs. black) and choline acetyltransferase-expressing tonically active interneurons (TANs, blue) were separated based on cell waveform width, peak-to-trough ratio and firing rate. Optogenetically identified D1-MSNs (dark green) and D2-MSNs (dark red) overlapped with the cluster of putative MSNs recorded from C57BL/6J mice. There were no significant differences between D1-MSNs and D2-MSNs in terms of spike amplitude (t261 = 0.07, P = 0.94), spike half-width (t261 = 1.30, P = 0.20), spike peak-to-trough ratio (t261 = 0.19, P = 0.85) or baseline firing rate (D1-MSNs 4.5 ± 0.4 Hz, D2-MSNs 4.4 ± 0.5 Hz; t261 = 0.45, P = 0.63). b, Example waveforms of a FSI (red, left panel), a MSN (black, left second panel) and a TAN (blue, right panel) recorded from C57BL/6J mice, and a D1-MSN (dark green, middle panel) and a D2-MSN (dark red, right second panel) recorded from D1-ChR2 and D2-ChR2 mice respectively. Among the total striatal units recorded from all training sessions of C57BL/6J mice, 3.4% were classified as putative FSIs, 5.0% were classified as TANs and 91.6% were classified as MSNs (also see detailed data in Supplementary Tables 2 -4).

  10. Procedure for classification of different types of sequence-related neuronal activity.
    Supplementary Fig. 5: Procedure for classification of different types of sequence-related neuronal activity.

    a, Decision tree for determining sequence-related start/stop, sustained and inhibited types of neuronal activity, as well as potential subtypes with overlapping response, based on the firing rate modulation during each lever press within action sequence. b-c, A typical example of 3-D classification and isolation of different response types based on the principle component analysis (PCA) of individual-lever-press related firing rate modulation vector [r1, r2, r3, r4] (see Methods for more details). The sequence-related start, stop and boundary activity were clearly classified as individual clusters (b). The sequence-related inhibited and sustained activity were further separated and highlighted under a different view angle for better visualization (c).

  11. Statistics for neurons with complex activity in Striatum, SNr and GPe across learning.
    Supplementary Fig. 6: Statistics for neurons with complex activity in Striatum, SNr and GPe across learning.

    a, b, Percentage of general task-related MSNs (a) and SNr/GPe neurons (b). c, d, Percentage of sequence-related neurons displaying both start/stop and inhibited activity for MSNs (c) and SNr/GPe neurons (d). e, f, Percentage of sequence-related neurons showing both start/stop and sustained activity for MSNs (e) and SNr/GPe neurons (f). g-i, Percentage of sequence-related start only, stop only and boundary (i.e. both start and stop) types of neurons for MSNs (g), SNr (h) and GPe neurons (i).

  12. Spatial distribution of neurons recorded on different electrodes exhibiting various types of sequence-related activity across regions.
    Supplementary Fig. 7: Spatial distribution of neurons recorded on different electrodes exhibiting various types of sequence-related activity across regions.

    a-c, Proportion of sequence-related start, stop, boundary, inhibited and sustained activity in dorsal striatum (a), SNr (b) and GPe (c) roughly across electrodes 1 more lateral to 8 more medial (2x8 array for DS and GPe or 1 – 4, 4x4 array, for GPe), corresponding to the lateral – medial gradient. GPe appears to have a more even distribution across electrodes than SNr and DS, where there is a trend toward more lateral electrodes to have higher proportion of sequence-related activity. Note that the most lateral electrode of SNr is probably at the border of SNr as few cells are recorded.

  13. (a-b) Percentage of sequence-related activity in putative D1- and D2-MSNs using identification criteria of light response latency of less than or equal to 10ms.
    Supplementary Fig. 8: (a-b) Percentage of sequence-related activity in putative D1- and D2-MSNs using identification criteria of light response latency of less than or equal to 10ms.

    a, Sequence-related activity in D1- vs. D2-MSNs. The results consistently showed that sequence-related start/stop activity was similarly observed in D1- vs. D2-MSNs (t23 = 1.02, P = 0.32), but that sequence-related inhibited activity was dominant in D2- over D1-MSNs (t23 = 3.89, P < 0.001), while sequence-related sustained activity was preferentially implemented in D1- rather than D2-MSNs (t23 = 2.24, P < 0.05). b, Within start/stop activity in D1- vs. D2-MSNs. While similar percentage of D1-MSNs signaled sequence start vs. stop (t15 = 0.68, P = 0.50), there was higher proportion of D2-MSNs signaling sequence start than sequence stop (t8 = 3.18, P < 0.01). (c-d) Positive and negative firing rate modulation in D1-/D2-MSNs for sequence-related start/stop activity. The percentage of start/stop neurons showing positive vs. negative firing rate modulation, for D1-MSNs (c) and D2-MSNs (d). (e) Timing of sequence-related start activity in D1-/D2-MSNs in relation to sequence initiation. The percentage of timing distribution of start neurons related to sequence initiation for D1-MSNs (red bars, on average -185.8 ± 43.7 ms) and D2-MSNs (blue bars, on average -215.9 ± 36.3 ms). There was no significant difference in the timing of start activity between the two neuronal population (t70 = 0.52, P = 0.60).

  14. Action sequence-related activity in putative striatal interneurons.
    Supplementary Fig. 9: Action sequence-related activity in putative striatal interneurons.

    a, PETH of a fast-spiking interneuron - putative striatal parvalbumin-expressing neuron (FSI) showing sustained firing activity throughout the whole action sequence. The FSI exhibited high baseline firing rate with narrow waveform (downright panel). b-d, Statistics of percentage of striatal FSIs shows general task-related activity (b), or sequence-related activity (c), or sequence-related start only, stop only and both start and stop activity (d). e, PETH of a tonically-active interneurons - putative striatal choline acetyltransferase-expressing (TAN) showing inhibited firing activity throughout the whole action sequence. The TANs exhibited moderate baseline firing with very wide waveform (downright panel). f-h, Proportion of striatal TANs showing general task-related activity (f), sequence-related activity (g), and sequence-related start only, stop only and both start and stop activity (h). Different types of putative striatal interneurons showed distinct sequence-related activity; while a significant proportion of both FSIs and TANs exhibited sequence-related start/stop activity, FSIs showed mainly sustained activity and TANs mostly displayed inhibited activity throughout the whole sequence.

  15. (a-b) Sequence-related neuronal activity in primary motor cortex (M1).
    Supplementary Fig. 10: (a-b) Sequence-related neuronal activity in primary motor cortex (M1).

    a, Percentage of task-related M1 neurons showing sequence-related start/stop, inhibited or sustained activity. b, Distribution of percentage of start only, stop only and boundary (both start and stop) subtypes of activity in M1 sequence-related start/stop neurons. (c-d) Diagram of two different operation modes of basal ganglia direct/indirect pathways during sequence initiation/termination vs. sequence execution. c, Phasic co-activation of D1- and D2-MSNs during sequence initiation/termination. d, Sustained activity dominated in D1-MSNs and inhibited activity dominated in D2-MSNs during sequence execution. The downstream nuclei SNr and GPe demonstrated corresponding activity in a consistent way.

Videos

  1. Mice can learn to perform very rapid action sequences.
    Video 1: Mice can learn to perform very rapid action sequences.
    An example of electrode-implanted mouse performing rapid action sequence under FR4/1s with simultaneous neural recording.

Change history

Corrected online 02 February 2014
In the version of this article initially published online, gray and black were reversed in the key to Figure 2i. The error has been corrected for the print, PDF and HTML versions of this article.

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Author information

Affiliations

  1. Laboratory for Integrative Neuroscience, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland, USA.

    • Xin Jin &
    • Rui M Costa

  2. Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, USA.

    • Xin Jin

  3. Champalimaud Neuroscience Programme at Instituto Gulbenkian de Ciência and Champalimaud Center for the Unknown, Lisbon, Portugal.

    • Fatuel Tecuapetla &
    • Rui M Costa

Contributions

X.J. performed the experiments and analyzed the data. F.T. conducted part of the D2-Cre optogenetics experiment. X.J. and R.M.C. designed the experiments and wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

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Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: The relation between standard deviation (STD) and mean of inter-press intervals (IPIs) under different schedules. (159 KB)

    a-c, Disproportionally faster decrease of the standard deviation compared to the mean for IPI1 (a), IPI2 (b) and IPI3 (c) as training progressed. Note that the linear fit line (red, appears distorted under logarithmic scale) deviated from the 1:1 line (black) in all three cases.

  2. Supplementary Figure 2: Impaired sequence learning, decreased action efficiency and increased pressing speed in RGS9L-Cre/Nr1f/f mutants. (351 KB)

    a, b, Example of the behavioral microstructure of of a RGS9L-Cre/Nr1f/f mutant (KO) and littermate control (CT) mouse under the schedule FR4/8s. This mutant (b) exhibited longer lever-press sequences than its littermate controls (a). Each dot indicates a lever press, with red and black indicating the first and final press of each individual sequence. The black and red solid lines on the X axis represent magazine entries and licks, respectively. The black dashed lines indicate the time of reward and corresponding lever press2. c, d, Another example of microstructure of behavior of a RGS9L-Cre/Nr1f/f mutant (d), who exhibited shorter lever-press sequences than littermate controls (c). e-h, Sequence length difference to four (e), action efficiency (f, percentage of lever presses actually rewarded), within-sequence press rate (g) and inter-press interval (h) for RGS9L-Cre/Nr1f/f mutants and controls under FR4/8s. For data and statistics: (e) CT 0.79 ± 0.10 vs. KO 1.50 ± 0.15, t44 = 3.94, P < 0.001; (f) CT 78.1 ± 2.2 % vs. KO 57.7 ± 4.0 %, t44 = 4.82, P < 0.001; (g) CT 109.8 ± 10.2 times/min vs. KO 278.3 ± 27.7 times/min, t44 = 6.76, P < 0.0001; (h) IPI1: CT 1581.1 ± 86.4 ms vs. KO 976.0 ± 135.2 ms, t44 = 3.98, P < 0.001; IPI2: CT 1453.4 ± 88.2 ms vs. KO 1002.3 ± 156.9 ms, t44 = 2.72, P < 0.01; IPI3: CT 1387.0 ± 77.9 ms vs. KO 872.5 ± 107.9 ms, t44 = 3.93, P < 0.001; See Methods for details.

  3. Supplementary Figure 3: Depiction of electrode placement confirmed by cresyl violet staining. (371 KB)

    a-f, The electrode array and placement in dorsal striatum (a), SNr (b) and GPe (c), which were further confirmed by post experimental staining (d-f). Atlas adapted from Paxinos & Franklin 50.

  4. Supplementary Figure 4: Striatal neurons classification. (153 KB)

    a, The 3-D plot of striatal units, and each dot indicates a single unit. Putative parvalbumin-expressing fast-spiking interneurons (FSIs, red), medium-spiny neurons (MSNs. black) and choline acetyltransferase-expressing tonically active interneurons (TANs, blue) were separated based on cell waveform width, peak-to-trough ratio and firing rate. Optogenetically identified D1-MSNs (dark green) and D2-MSNs (dark red) overlapped with the cluster of putative MSNs recorded from C57BL/6J mice. There were no significant differences between D1-MSNs and D2-MSNs in terms of spike amplitude (t261 = 0.07, P = 0.94), spike half-width (t261 = 1.30, P = 0.20), spike peak-to-trough ratio (t261 = 0.19, P = 0.85) or baseline firing rate (D1-MSNs 4.5 ± 0.4 Hz, D2-MSNs 4.4 ± 0.5 Hz; t261 = 0.45, P = 0.63). b, Example waveforms of a FSI (red, left panel), a MSN (black, left second panel) and a TAN (blue, right panel) recorded from C57BL/6J mice, and a D1-MSN (dark green, middle panel) and a D2-MSN (dark red, right second panel) recorded from D1-ChR2 and D2-ChR2 mice respectively. Among the total striatal units recorded from all training sessions of C57BL/6J mice, 3.4% were classified as putative FSIs, 5.0% were classified as TANs and 91.6% were classified as MSNs (also see detailed data in Supplementary Tables 2 -4).

  5. Supplementary Figure 5: Procedure for classification of different types of sequence-related neuronal activity. (225 KB)

    a, Decision tree for determining sequence-related start/stop, sustained and inhibited types of neuronal activity, as well as potential subtypes with overlapping response, based on the firing rate modulation during each lever press within action sequence. b-c, A typical example of 3-D classification and isolation of different response types based on the principle component analysis (PCA) of individual-lever-press related firing rate modulation vector [r1, r2, r3, r4] (see Methods for more details). The sequence-related start, stop and boundary activity were clearly classified as individual clusters (b). The sequence-related inhibited and sustained activity were further separated and highlighted under a different view angle for better visualization (c).

  6. Supplementary Figure 6: Statistics for neurons with complex activity in Striatum, SNr and GPe across learning. (376 KB)

    a, b, Percentage of general task-related MSNs (a) and SNr/GPe neurons (b). c, d, Percentage of sequence-related neurons displaying both start/stop and inhibited activity for MSNs (c) and SNr/GPe neurons (d). e, f, Percentage of sequence-related neurons showing both start/stop and sustained activity for MSNs (e) and SNr/GPe neurons (f). g-i, Percentage of sequence-related start only, stop only and boundary (i.e. both start and stop) types of neurons for MSNs (g), SNr (h) and GPe neurons (i).

  7. Supplementary Figure 7: Spatial distribution of neurons recorded on different electrodes exhibiting various types of sequence-related activity across regions. (344 KB)

    a-c, Proportion of sequence-related start, stop, boundary, inhibited and sustained activity in dorsal striatum (a), SNr (b) and GPe (c) roughly across electrodes 1 more lateral to 8 more medial (2x8 array for DS and GPe or 1 – 4, 4x4 array, for GPe), corresponding to the lateral – medial gradient. GPe appears to have a more even distribution across electrodes than SNr and DS, where there is a trend toward more lateral electrodes to have higher proportion of sequence-related activity. Note that the most lateral electrode of SNr is probably at the border of SNr as few cells are recorded.

  8. Supplementary Figure 8: (a-b) Percentage of sequence-related activity in putative D1- and D2-MSNs using identification criteria of light response latency of less than or equal to 10ms. (277 KB)

    a, Sequence-related activity in D1- vs. D2-MSNs. The results consistently showed that sequence-related start/stop activity was similarly observed in D1- vs. D2-MSNs (t23 = 1.02, P = 0.32), but that sequence-related inhibited activity was dominant in D2- over D1-MSNs (t23 = 3.89, P < 0.001), while sequence-related sustained activity was preferentially implemented in D1- rather than D2-MSNs (t23 = 2.24, P < 0.05). b, Within start/stop activity in D1- vs. D2-MSNs. While similar percentage of D1-MSNs signaled sequence start vs. stop (t15 = 0.68, P = 0.50), there was higher proportion of D2-MSNs signaling sequence start than sequence stop (t8 = 3.18, P < 0.01). (c-d) Positive and negative firing rate modulation in D1-/D2-MSNs for sequence-related start/stop activity. The percentage of start/stop neurons showing positive vs. negative firing rate modulation, for D1-MSNs (c) and D2-MSNs (d). (e) Timing of sequence-related start activity in D1-/D2-MSNs in relation to sequence initiation. The percentage of timing distribution of start neurons related to sequence initiation for D1-MSNs (red bars, on average -185.8 ± 43.7 ms) and D2-MSNs (blue bars, on average -215.9 ± 36.3 ms). There was no significant difference in the timing of start activity between the two neuronal population (t70 = 0.52, P = 0.60).

  9. Supplementary Figure 9: Action sequence-related activity in putative striatal interneurons. (699 KB)

    a, PETH of a fast-spiking interneuron - putative striatal parvalbumin-expressing neuron (FSI) showing sustained firing activity throughout the whole action sequence. The FSI exhibited high baseline firing rate with narrow waveform (downright panel). b-d, Statistics of percentage of striatal FSIs shows general task-related activity (b), or sequence-related activity (c), or sequence-related start only, stop only and both start and stop activity (d). e, PETH of a tonically-active interneurons - putative striatal choline acetyltransferase-expressing (TAN) showing inhibited firing activity throughout the whole action sequence. The TANs exhibited moderate baseline firing with very wide waveform (downright panel). f-h, Proportion of striatal TANs showing general task-related activity (f), sequence-related activity (g), and sequence-related start only, stop only and both start and stop activity (h). Different types of putative striatal interneurons showed distinct sequence-related activity; while a significant proportion of both FSIs and TANs exhibited sequence-related start/stop activity, FSIs showed mainly sustained activity and TANs mostly displayed inhibited activity throughout the whole sequence.

  10. Supplementary Figure 10: (a-b) Sequence-related neuronal activity in primary motor cortex (M1). (161 KB)

    a, Percentage of task-related M1 neurons showing sequence-related start/stop, inhibited or sustained activity. b, Distribution of percentage of start only, stop only and boundary (both start and stop) subtypes of activity in M1 sequence-related start/stop neurons. (c-d) Diagram of two different operation modes of basal ganglia direct/indirect pathways during sequence initiation/termination vs. sequence execution. c, Phasic co-activation of D1- and D2-MSNs during sequence initiation/termination. d, Sustained activity dominated in D1-MSNs and inhibited activity dominated in D2-MSNs during sequence execution. The downstream nuclei SNr and GPe demonstrated corresponding activity in a consistent way.

Video

  1. Video 1: Mice can learn to perform very rapid action sequences. (1.55 MB, Download)
    An example of electrode-implanted mouse performing rapid action sequence under FR4/1s with simultaneous neural recording.

PDF files

  1. Supplementary Text and Figures (2,943 KB)

    Supplementary Figures 1–10 and Supplementary Table 1

Additional data