Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Differences in the emergent coding properties of cortical and striatal ensembles


The function of a given brain region is often defined by the coding properties of its individual neurons, yet how this information is combined at the ensemble level is an equally important consideration. We recorded multiple neurons from the anterior cingulate cortex (ACC) and the dorsal striatum (DS) simultaneously as rats performed different sequences of the same three actions. Sequence and lever decoding was markedly similar on a per-neuron basis in the two regions. At the ensemble level, sequence-specific representations in the DS appeared synchronously, but transiently, along with the representation of lever location, whereas these two streams of information appeared independently and asynchronously in the ACC. As a result, the ACC achieved superior ensemble decoding accuracy overall. Thus, the manner in which information was combined across neurons in an ensemble determined the functional separation of the ACC and DS on this task.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Behavior was similar for common segment lever presses in different sequence blocks.
Figure 2: No regional differences in the way individual neurons responded to presses in different sequence blocks or on different physical levers.
Figure 3: Comparison of the signal detection characteristics of single neurons versus ensembles.
Figure 4: Sequence information is represented as differences in ensemble activity state patterns in both the ACC and DS.
Figure 5: Consistency of sequence encoding in the ACC and DS within behavioral epochs.
Figure 6: Comparison of the timing of maximal sequence and lever differentiation in ACC and DS ensembles.


  1. Duncan, J. An adaptive coding model of neural function in prefrontal cortex. Nat. Rev. Neurosci. 2, 820–829 (2001).

    Article  CAS  Google Scholar 

  2. Jung, M.W., Qin, Y., McNaughton, B.L. & Barnes, C.A. Firing characteristics of deep layer neurons in prefrontal cortex in rats performing spatial working memory tasks. Cereb. Cortex 8, 437–450 (1998).

    Article  CAS  Google Scholar 

  3. Fino, E. & Yuste, R. Dense inhibitory connectivity in neocortex. Neuron 69, 1188–1203 (2011).

    Article  CAS  Google Scholar 

  4. Packer, A.M. et al. Two-photon optogenetics of dendritic spines and neural circuits. Nat. Methods 9, 1202–1205 (2012).

    Article  CAS  Google Scholar 

  5. Durstewitz, D., Vittoz, N.M., Floresco, S.B. & Seamans, J.K. Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning. Neuron 66, 438–448 (2010).

    Article  CAS  Google Scholar 

  6. Rich, E.L. & Shapiro, M. Rat prefrontal cortical neurons selectively code strategy switches. J. Neurosci. 29, 7208–7219 (2009).

    Article  CAS  Google Scholar 

  7. Parthasarathy, H.B. & Graybiel, A.M. Cortically driven immediate-early gene expression reflects modular influence of sensorimotor cortex on identified striatal neurons in the squirrel monkey. J. Neurosci. 17, 2477–2491 (1997).

    Article  CAS  Google Scholar 

  8. Koós, T. & Tepper, J.M. Inhibitory control of neostriatal projection neurons by GABAergic interneurons. Nat. Neurosci. 2, 467–472 (1999).

    Article  Google Scholar 

  9. Gage, G.J., Stoetzner, C.R., Wiltschko, A.B. & Berke, J.D. Selective activation of striatal fast-spiking interneurons during choice execution. Neuron 67, 466–479 (2010).

    Article  CAS  Google Scholar 

  10. Berke, J.D., Okatan, M., Skurski, J. & Eichenbaum, H.B. Oscillatory entrainment of striatal neurons in freely moving rats. Neuron 43, 883–896 (2004).

    Article  CAS  Google Scholar 

  11. Graybiel, A.M., Aosaki, T., Flaherty, A.W. & Kimura, M. The basal ganglia and adaptive motor control. Science 265, 1826–1831 (1994).

    Article  CAS  Google Scholar 

  12. Graybiel, A.M. Building action repertoires: memory and learning functions of the basal ganglia. Curr. Opin. Neurobiol. 5, 733–741 (1995).

    Article  CAS  Google Scholar 

  13. Averbeck, B.B. & Lee, D. Effects of noise correlations on information encoding and decoding. J. Neurophysiol. 95, 3633–3644 (2006).

    Article  Google Scholar 

  14. Sesack, S.R., Deutch, A.Y., Roth, R.H. & Bunney, B.S. Topographical organization of the efferent projections of the medial prefrontal cortex in the rat: an anterograde tract-tracing study with Phaseolus vulgaris leucoagglutinin. J. Comp. Neurol. 290, 213–242 (1989).

    Article  CAS  Google Scholar 

  15. Zheng, T. & Wilson, C.J. Corticostriatal combinatorics: the implications of corticostriatal axonal arborizations. J. Neurophysiol. 87, 1007–1017 (2002).

    Article  CAS  Google Scholar 

  16. Mushiake, H., Saito, N., Sakamoto, K., Itoyama, Y. & Tanji, J. Activity in the lateral prefrontal cortex reflects multiple steps of future events in action plans. Neuron 50, 631–641 (2006).

    Article  CAS  Google Scholar 

  17. Averbeck, B.B. & Lee, D. Prefrontal neural correlates of memory for sequences. J. Neurosci. 27, 2204–2211 (2007).

    Article  CAS  Google Scholar 

  18. Shima, K., Isoda, M., Mushiake, H. & Tanji, J. Categorization of behavioural sequences in the prefrontal cortex. Nature 445, 315–318 (2007).

    Article  CAS  Google Scholar 

  19. Procyk, E., Tanaka, Y.L. & Joseph, J.P. Anterior cingulate activity during routine and non-routine sequential behaviors in macaques. Nat. Neurosci. 3, 502–508 (2000).

    Article  CAS  Google Scholar 

  20. Barone, P. & Joseph, J.P. Prefrontal cortex and spatial sequencing in macaque monkey. Exp. Brain Res. 78, 447–464 (1989).

    Article  CAS  Google Scholar 

  21. Nakamura, K., Sakai, K. & Hikosaka, O. Neuronal activity in medial frontal cortex during learning of sequential procedures. J. Neurophysiol. 80, 2671–2687 (1998).

    Article  CAS  Google Scholar 

  22. Ninokura, Y., Mushiake, H. & Tanji, J. Integration of temporal order and object information in the monkey lateral prefrontal cortex. J. Neurophysiol. 91, 555–560 (2004).

    Article  Google Scholar 

  23. Shidara, M. & Richmond, B.J. Anterior cingulate: single neuronal signals related to degree of reward expectancy. Science 296, 1709–1711 (2002).

    Article  Google Scholar 

  24. Ryou, J.W. & Wilson, F.A. Making your next move: dorsolateral prefrontal cortex and planning a sequence of actions in freely moving monkeys. Cogn. Affect. Behav. Neurosci. 4, 430–443 (2004).

    Article  Google Scholar 

  25. Lu, X. & Ashe, J. Anticipatory activity in primary motor cortex codes memorized movement sequences. Neuron 45, 967–973 (2005).

    Article  CAS  Google Scholar 

  26. Schmitzer-Torbert, N. & Redish, A.D. Neuronal activity in the rodent dorsal striatum in sequential navigation: separation of spatial and reward responses on the multiple T task. J. Neurophysiol. 91, 2259–2272 (2004).

    Article  Google Scholar 

  27. Fujii, N. & Graybiel, A.M. Time-varying covariance of neural activities recorded in striatum and frontal cortex as monkeys perform sequential-saccade tasks. Proc. Natl. Acad. Sci. USA 102, 9032–9037 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  29. 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).

    Article  CAS  Google Scholar 

  30. Seo, M., Lee, E. & Averbeck, B.B. Action selection and action value in frontal-striatal circuits. Neuron 74, 947–960 (2012).

    Article  CAS  Google Scholar 

  31. Cowen, S.L. & McNaughton, B.L. Selective delay activity in the medial prefrontal cortex of the rat: contribution of sensorimotor information and contingency. J. Neurophysiol. 98, 303–316 (2007).

    Article  Google Scholar 

  32. Euston, D.R. & McNaughton, B.L. Apparent encoding of sequential context in rat medial prefrontal cortex is accounted for by behavioral variability. J. Neurosci. 26, 13143–13155 (2006).

    Article  CAS  Google Scholar 

  33. Krzanowski, W.J. Principles of Multivariate Analysis: a User's Perspective (Oxford University Press, 2000).

  34. Berdyyeva, T.K. & Olson, C.R. Rank signals in four areas of macaque frontal cortex during selection of actions and objects in serial order. J. Neurophysiol. 104, 141–159 (2010).

    Article  Google Scholar 

  35. Clower, W.T. & Alexander, G.E. Movement sequence-related activity reflecting numerical order of components in supplementary and presupplementary motor areas. J. Neurophysiol. 80, 1562–1566 (1998).

    Article  CAS  Google Scholar 

  36. Grillner, S., Hellgren, J., Menard, A., Saitoh, K. & Wikstrom, M.A. Mechanisms for selection of basic motor programs—roles for the striatum and pallidum. Trends Neurosci. 28, 364–370 (2005).

    Article  CAS  Google Scholar 

  37. Carrillo-Reid, L. et al. Encoding network states by striatal cell assemblies. J. Neurophysiol. 99, 1435–1450 (2008).

    Article  Google Scholar 

  38. Calabresi, P., Misgeld, U. & Dodt, H.U. Intrinsic membrane properties of neostriatal neurons can account for their low level of spontaneous activity. Neuroscience 20, 293–303 (1987).

    Article  CAS  Google Scholar 

  39. Wilson, C.J. & Kawaguchi, Y. The origins of two-state spontaneous membrane potential fluctuations of neostriatal spiny neurons. J. Neurosci. 16, 2397–2410 (1996).

    Article  CAS  Google Scholar 

  40. Hyman, J.M., Ma, L., Balaguer-Ballester, E., Durstewitz, D. & Seamans, J.K. Contextual encoding by ensembles of medial prefrontal cortex neurons. Proc. Natl. Acad. Sci. USA 109, 5086–5091 (2012).

    Article  CAS  Google Scholar 

Download references


This research was supported by Canadian Institutes of Health Research grants (MOP-93784 and MOP-84319).

Author information

Authors and Affiliations



J.K.S. and L.M. designed the study, L.M. conducted the experiments, L.M., J.M.H., A.J.L. and J.K.S. performed data analysis and created the figures, A.G.P. helped interpret the results and write the manuscript.

Corresponding author

Correspondence to Liya Ma.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Task description and performance.

a) The operant chamber contained 3 levers installed on the front panel and a food-cup on the opposing wall. A unique sensory cue was attached to the floor immediately in front of each lever as well as on the surrounding wall. The 1st lever (light gray in schematic) to be pressed in a given sequence block could have Velcro attached, the 2nd lever (dark gray in schematic) cardboard attached and the 3rd lever (black in schematic), soft foam attached. b) Example of a typical test day where the rat had to perform a minimum of 10 trials on each of the 3 sequence blocks, which were given in a pseudorandom order. On this session, sequence block A required the rat to respond on the right lever, followed by the middle lever and then the left lever, before reward pellets were delivered to the food-cup on the opposite wall. The serial order of the 3 sensory cues (velcro, cardboard, foam) remained constant for a given rat but were moved to different levers for each of the sequence blocks.

Supplementary Figure 2 Histology showing recording sites.

a) Histology showing representative electrode track endings (white arrows) in the ACC. b) Schematics showing the location and range of the recording sites in the ACC. Given that not all electrodes left a track, the recording range was inferred based on the visible tracks and the size of the electrode arrays. c) Representative electrode track endings (white arrows) in the DS. d) Schematics showing the location and range of the recording sites in the DS.

Supplementary Figure 3 Firing properties in the ACC and DS.

The iFRs from all bins and neurons were combined for each region, and their probability density functions were plotted on a logarithmic scale. Although both ACC and DS neurons usually have low firing rates, DS neurons exhibited brief periods of high activity more often than did ACC neurons, giving rise to the long-tailed firing rate distribution (DS: gray, ACC: black).

Supplementary information

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ma, L., Hyman, J., Lindsay, A. et al. Differences in the emergent coding properties of cortical and striatal ensembles. Nat Neurosci 17, 1100–1106 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing