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



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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Figure 1: Behavioral learning of rapid action sequences in mice.
Figure 2: Neuronal activity in the dorsal striatum 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.
Figure 4: Action- versus speed-specific sequence-related activity in the basal ganglia circuits.
Figure 5: Subcircuit-specific neuronal activity in the basal ganglia during learning and performance of rapid action sequences.

Change history

  • 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.


  1. 1

    Lashley, K.S. The problem of serial order in behavior. in Cerebral Mechanisms in Behavior (ed. Jeffress, L.A.) (John Wiley Press, New York, 1951).

  2. 2

    Miller, G.A. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956).

    CAS  Article  Google Scholar 

  3. 3

    Gallistel, C.R. The Organization of Action: A New Synthesis (Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1980).

  4. 4

    Doupe, A.J. & Kuhl, P.K. Birdsong and human speech: common themes and mechanisms. Annu. Rev. Neurosci. 22, 567–631 (1999).

    CAS  Article  Google Scholar 

  5. 5

    Sakai, K., Kitaguchi, K. & Hikosaka, O. Chunking during human visuomotor sequence learning. Exp. Brain Res. 152, 229–242 (2003).

    Article  Google Scholar 

  6. 6

    Hikosaka, O., Miyashita, K., Miyachi, S., Sakai, K. & Lu, X. Differential roles of the frontal cortex, basal ganglia and cerebellum in visuomotor sequence learning. Neurobiol. Learn. Mem. 70, 137–149 (1998).

    CAS  Article  Google Scholar 

  7. 7

    Graybiel, A.M. The basal ganglia and chunking of action repertoires. Neurobiol. Learn. Mem. 70, 119–136 (1998).

    CAS  Article  Google Scholar 

  8. 8

    Doupe, A.J., Perkel, D.J., Reiner, A. & Stern, E.A. Birdbrains could teach basal ganglia research a new song. Trends Neurosci. 28, 353–363 (2005).

    CAS  Article  Google Scholar 

  9. 9

    Jin, X. & Costa, R.M. Start/stop signals emerge in nigrostriatal circuits during sequence learning. Nature 466, 457–462 (2010).

    CAS  Article  Google Scholar 

  10. 10

    Miyachi, S., Hikosaka, O., Miyashita, K., Karadi, Z. & Rand, M.K. Differential roles of monkey striatum in learning of sequential hand movement. Exp. Brain Res. 115, 1–5 (1997).

    CAS  Article  Google Scholar 

  11. 11

    Benecke, R., Rothwell, J.C., Dick, J.P., Day, B.L. & Marsden, C.D. Disturbance of sequential movements in patients with Parkinson's disease. Brain 110, 361–379 (1987).

    Article  Google Scholar 

  12. 12

    Phillips, J.G., Chiu, E., Bradshaw, J.L. & Iansek, R. Impaired movement sequencing in patients with Huntington's disease: a kinematic analysis. Neuropsychologia 33, 365–369 (1995).

    CAS  Article  Google Scholar 

  13. 13

    Boyd, L.A. et al. Motor sequence chunking is impaired by basal ganglia stroke. Neurobiol. Learn. Mem. 92, 35–44 (2009).

    CAS  Article  Google Scholar 

  14. 14

    Jog, M.S., Kubota, Y., Connolly, C.I., Hillegaart, V. & Graybiel, A.M. Building neural representations of habits. Science 286, 1745–1749 (1999).

    CAS  Article  Google Scholar 

  15. 15

    Wymbs, N.F., Bassett, D.S., Mucha, P.J., Porter, M.A. & Grafton, S.T. Differential recruitment of the sensorimotor putamen and frontoparietal cortex during motor chunking in humans. Neuron 74, 936–946 (2012).

    CAS  Article  Google Scholar 

  16. 16

    Pellegrino, F., Coupé, C. & Marsico, E. A cross-language perspective on speech information rate. Language 87, 539–558 (2011).

    Article  Google Scholar 

  17. 17

    Gerfen, C.R. et al. D1 and D2 dopamine receptor-regulated gene expression of striatonigral and striatopallidal neurons. Science 250, 1429–1432 (1990).

    CAS  Article  Google Scholar 

  18. 18

    Gong, S. et al. Targeting Cre recombinase to specific neuron populations with bacterial artificial chromosome constructs. J. Neurosci. 27, 9817–9823 (2007).

    CAS  Article  Google Scholar 

  19. 19

    Albin, R.L., Young, A.B. & Penney, J.B. The functional anatomy of basal ganglia disorders. Trends Neurosci. 12, 366–375 (1989).

    CAS  Article  Google Scholar 

  20. 20

    DeLong, M.R. Primate models of movement disorders of basal ganglia origin. Trends Neurosci. 13, 281–285 (1990).

    CAS  Article  Google Scholar 

  21. 21

    Graybiel, A.M. The basal ganglia. Curr. Biol. 10, R509–R511 (2000).

    CAS  Article  Google Scholar 

  22. 22

    Kravitz, A.V. et al. Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry. Nature 466, 622–626 (2010).

    CAS  Article  Google Scholar 

  23. 23

    Hikosaka, O., Takikawa, Y. & Kawagoe, R. Role of the basal ganglia in the control of purposive saccadic eye movements. Physiol. Rev. 80, 953–978 (2000).

    CAS  Article  Google Scholar 

  24. 24

    Mink, J.W. The Basal Ganglia and involuntary movements: impaired inhibition of competing motor patterns. Arch. Neurol. 60, 1365–1368 (2003).

    Article  Google Scholar 

  25. 25

    Hampton, C.M., Sakata, J.T. & Brainard, M.S. An avian basal ganglia-forebrain circuit contributes differentially to syllable versus sequence variability of adult Bengalese finch song. J. Neurophysiol. 101, 3235–3245 (2009).

    Article  Google Scholar 

  26. 26

    Aldridge, J.W. & Berridge, K.C. Coding of serial order by neostriatal neurons: a “natural action” approach to movement sequence. J. Neurosci. 18, 2777–2787 (1998).

    CAS  Article  Google Scholar 

  27. 27

    Watanabe, M. & Munoz, D.P. Probing basal ganglia functions by saccade eye movements. Eur. J. Neurosci. 33, 2070–2090 (2011).

    Article  Google Scholar 

  28. 28

    Lima, S.Q., Hromadka, T., Znamenskiy, P. & Zador, A.M. PINP: a new method of tagging neuronal populations for identification during in vivo electrophysiological recording. PLoS ONE 4, e6099 (2009).

    Article  Google Scholar 

  29. 29

    Boyden, E.S., Zhang, F., Bamberg, E., Nagel, G. & Deisseroth, K. Millisecond timescale, genetically targeted optical control of neural activity. Nat. Neurosci. 8, 1263–1268 (2005).

    CAS  Article  Google Scholar 

  30. 30

    Fujii, N. & Graybiel, A.M. Representation of action sequence boundaries by macaque prefrontal cortical neurons. Science 301, 1246–1249 (2003).

    CAS  Article  Google Scholar 

  31. 31

    Barnes, T.D., Kubota, Y., Hu, D., Jin, D.Z. & Graybiel, A.M. Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories. Nature 437, 1158–1161 (2005).

    CAS  Article  Google Scholar 

  32. 32

    Kimura, M., Kato, M., Shimazaki, H., Watanabe, K. & Matsumoto, N. Neural information transferred from the putamen to the globus pallidus during learned movement in the monkey. J. Neurophysiol. 76, 3771–3786 (1996).

    CAS  Article  Google Scholar 

  33. 33

    Tanji, J. Sequential organization of multiple movements: involvement of cortical motor areas. Annu. Rev. Neurosci. 24, 631–651 (2001).

    CAS  Article  Google Scholar 

  34. 34

    Hikosaka, O. GABAergic output of the basal ganglia. Prog. Brain Res. 160, 209–226 (2007).

    CAS  Article  Google Scholar 

  35. 35

    McHaffie, J.G., Stanford, T.R., Stein, B.E., Coizet, V. & Redgrave, P. Subcortical loops through the basal ganglia. Trends Neurosci. 28, 401–407 (2005).

    CAS  Article  Google Scholar 

  36. 36

    Harvey, C.D., Coen, P. & Tank, D.W. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012).

    CAS  Article  Google Scholar 

  37. 37

    Shima, K. & Tanji, J. Binary-coded monitoring of a behavioral sequence by cells in the pre-supplementary motor area. J. Neurosci. 26, 2579–2582 (2006).

    CAS  Article  Google Scholar 

  38. 38

    Kao, M.H., Doupe, A.J. & Brainard, M.S. Contributions of an avian basal ganglia-forebrain circuit to real-time modulation of song. Nature 433, 638–643 (2005).

    CAS  Article  Google Scholar 

  39. 39

    Ölveczky, B.P., Andalman, A.S. & Fee, M.S. Vocal experimentation in the juvenile songbird requires a basal ganglia circuit. PLoS Biol. 3, e153 (2005).

    Article  Google Scholar 

  40. 40

    Graybiel, A.M. & Rauch, S.L. Toward a neurobiology of obsessive-compulsive disorder. Neuron 28, 343–347 (2000).

    CAS  Article  Google Scholar 

  41. 41

    Vargha-Khadem, F., Gadian, D.G., Copp, A. & Mishkin, M. FOXP2 and the neuroanatomy of speech and language. Nat. Rev. Neurosci. 6, 131–138 (2005).

    CAS  Article  Google Scholar 

  42. 42

    Dang, M.T. et al. Disrupted motor learning and long-term synaptic plasticity in mice lacking NMDAR1 in the striatum. Proc. Natl. Acad. Sci. USA 103, 15254–15259 (2006).

    CAS  Article  Google Scholar 

  43. 43

    Burkhardt, J.M., Jin, X. & Costa, R.M. Dissociable effects of dopamine on neuronal firing rate and synchrony in the dorsal striatum. Front. Integr. Neurosci. 3, 28 (2009).

    Article  Google Scholar 

  44. 44

    Gulley, R.L. & Wood, R.L. The fine structure of the neurons in the rat substantia nigra. Tissue Cell 3, 675–690 (1971).

    CAS  Article  Google Scholar 

  45. 45

    Juraska, J.M., Wilson, C.J. & Groves, P.M. The substantia nigra of the rat: a Golgi study. J. Comp. Neurol. 172, 585–600 (1977).

    CAS  Article  Google Scholar 

  46. 46

    Millhouse, O.E. Pallidal neurons in the rat. J. Comp. Neurol. 254, 209–227 (1986).

    CAS  Article  Google Scholar 

  47. 47

    Nambu, A. & Llinas, R. Morphology of globus pallidus neurons: its correlation with electrophysiology in guinea pig brain slices. J. Comp. Neurol. 377, 85–94 (1997).

    CAS  Article  Google Scholar 

  48. 48

    Gerfen, C.R. Indirect-pathway neurons lose their spines in Parkinson disease. Nat. Neurosci. 9, 157–158 (2006).

    CAS  Article  Google Scholar 

  49. 49

    Belova, M.A., Paton, J.J., Morrison, S.E. & Salzman, C.D. Expectation modulates neural responses to pleasant and aversive stimuli in primate amygdala. Neuron 55, 970–984 (2007).

    CAS  Article  Google Scholar 

  50. 50

    Paxinos, G. & Franklin, K.B. The Mouse Brain in Stereotaxic Coordinates, 2nd edn. (Academic Press, 2001).

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We thank C. Gerfen for the D1-Cre and D2-Cre mice (US National Institutes of Health), Y. Li for the Rgs9-cre mice (University of Florida), K. Nakazawa for the Grin1loxP/loxP mice (US National Institutes of Health), G. Luo and A. Vaz for genotyping, and G. Cui for comments on the manuscript. This research was supported by the National Institute on Alcohol Abuse and Alcoholism Division of Intramural Clinical and Biological Research, the Champalimaud Neuroscience Programme, European Research Council grant 243393 and Howard Hughes Medical Institute International Early Careers Scientist Grant to R.M.C., the Whitehall Foundation, and US National Institutes of Health grant NS083815 to X.J.

Author information




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.

Corresponding authors

Correspondence to Xin Jin or Rui M Costa.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 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.

Supplementary Figure 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.

Supplementary Figure 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.

Supplementary Figure 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).

Supplementary Figure 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).

Supplementary Figure 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).

Supplementary Figure 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.

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.

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).

Supplementary Figure 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.

Supplementary Figure 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.

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Supplementary Text and Figures

Supplementary Figures 1–10 and Supplementary Table 1 (PDF 2874 kb)

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. (MOV 1588 kb)

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Jin, X., Tecuapetla, F. & Costa, R. Basal ganglia subcircuits distinctively encode the parsing and concatenation of action sequences. Nat Neurosci 17, 423–430 (2014).

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