Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex

Article metrics


Although short-term plasticity is believed to play a fundamental role in cortical computation, empirical evidence bearing on its role during behavior is scarce. Here we looked for the signature of short-term plasticity in the fine-timescale spiking relationships of a simultaneously recorded population of physiologically identified pyramidal cells and interneurons, in the medial prefrontal cortex of the rat, in a working memory task. On broader timescales, sequentially organized and transiently active neurons reliably differentiated between different trajectories of the rat in the maze. On finer timescales, putative monosynaptic interactions reflected short-term plasticity in their dynamic and predictable modulation across various aspects of the task, beyond a statistical accounting for the effect of the neurons' co-varying firing rates. Seeking potential mechanisms for such effects, we found evidence for both firing pattern–dependent facilitation and depression, as well as for a supralinear effect of presynaptic coincidence on the firing of postsynaptic targets.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Large-scale recording of multiple single units from mPFC in a working memory task.
Figure 2: Behavior- and position-selective firing activity of PFC single neurons.
Figure 3: Physiological identification of pyramidal cells and interneurons.
Figure 4: Task-dependent changes in monosynaptic interactions.
Figure 5: Task-dependent changes of monosynaptic interactions are demonstrable beyond a statistical accounting for firing rate changes.
Figure 6: Spike transmission efficacy depends on the firing pattern of the presynaptic neuron.
Figure 7: Coincident firing of more than one neuron facilitates spike transmission.


  1. 1

    Hebb, D.O. The Organization of Behavior (Wiley, New York, 1949).

  2. 2

    Gupta, A., Wang, Y. & Markram, H. Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex. Science 287, 273–278 (2000).

  3. 3

    Hempel, C.M., Hartman, K.H., Wang, X.J., Turrigiano, G.G. & Nelson, S.B. Multiple forms of short-term plasticity at excitatory synapses in rat medial prefrontal cortex. J. Neurophysiol. 83, 3031–3041 (2000).

  4. 4

    Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat. Neurosci. 9, 534–542 (2006).

  5. 5

    Markram, H. et al. Interneurons of the neocortical inhibitory system. Nat. Rev. Neurosci. 5, 793–807 (2004).

  6. 6

    Thomson, A.M. & Lamy, C. Functional maps of neocortical local circuitry. Frontiers Neurosci. 1, 19–42 (2007).

  7. 7

    Abbott, L.F. & Regehr, W.G. Synaptic computation. Nature 431, 796–803 (2004).

  8. 8

    Zucker, R.S. & Regehr, W.G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).

  9. 9

    Reyes, A. et al. Target-cell-specific facilitation and depression in neocortical circuits. Nat. Neurosci. 1, 279–285 (1998).

  10. 10

    Markram, H., Wang, Y. & Tsodyks, M. Differential signaling via the same axon of neocortical pyramidal neurons. Proc. Natl. Acad. Sci. USA 95, 5323–5328 (1998).

  11. 11

    Holmgren, C., Harkany, T., Svennenfors, B. & Zilberter, Y. Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. J. Physiol. (Lond.) 551, 139–153 (2003).

  12. 12

    Mongillo, G., Barak, O. & Tsodyks, M. Synaptic theory of working memory. Science 319, 1543–1546 (2008).

  13. 13

    Sussillo, D., Toyoizumi, T. & Maass, W. Self-tuning of neural circuits through short-term synaptic plasticity. J. Neurophysiol. 97, 4079–4095 (2007).

  14. 14

    Riehle, A., Grun, S., Diesmann, M. & Aertsen, A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278, 1950–1953 (1997).

  15. 15

    Constantinidis, C., Williams, G.V. & Goldman-Rakic, P.S. A role for inhibition in shaping the temporal flow of information in prefrontal cortex. Nat. Neurosci. 5, 175–180 (2002).

  16. 16

    Hirabayashi, T. & Miyashita, Y. Dynamically modulated spike correlation in monkey inferior temporal cortex depending on the feature configuration within a whole object. J. Neurosci. 25, 10299–10307 (2005).

  17. 17

    Csicsvari, J., Hirase, H., Czurko, A. & Buzsáki, G. Reliability and state dependence of pyramidal cell–interneuron synapses in the hippocampus: an ensemble approach in the behaving rat. Neuron 21, 179–189 (1998).

  18. 18

    Bartho, P. et al. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. 92, 600–608 (2004).

  19. 19

    Marshall, L. et al. Hippocampal pyramidal cell-interneuron spike transmission is frequency dependent and responsible for place modulation of interneuron discharge. J. Neurosci. 22, RC197 (2002).

  20. 20

    Henze, D.A., Wittner, L. & Buzsáki, G. Single granule cells reliably discharge targets in the hippocampal CA3 network in vivo. Nat. Neurosci. 5, 790–795 (2002).

  21. 21

    Cobb, S.R., Buhl, E.H., Halasy, K., Paulsen, O. & Somogyi, P. Synchronization of neuronal activity in hippocampus by individual GABAergic interneurons. Nature 378, 75–78 (1995).

  22. 22

    Gabbott, P.L.A., Warner, T.A., Jays, P.R.L., Salway, P. & Busby, S.J. Prefrontal cortex in the rat: projections to subcortical autonomic, motor, and limbic centers. J. Comp. Neurol. 492, 145–177 (2005).

  23. 23

    Eichenbaum, H., Clegg, R.A. & Feeley, A. Reexamination of functional subdivisions of the rodent prefrontal cortex. Exp. Neurol. 79, 434–451 (1983).

  24. 24

    Brody, C.D. Correlations without synchrony. Neural Comput. 11, 1537–1551 (1999).

  25. 25

    Ventura, V., Cai, C. & Kass, R.E. Trial-to-trial variability and its effect on time-varying dependency between two neurons. J. Neurophysiol. 94, 2928–2939 (2005).

  26. 26

    Hatsopoulos, N., Geman, S., Amarasingham, A. & Bienenstock, E. At what time scale does the nervous system operate? Neurocomputing 52–54, 25–29 (2003).

  27. 27

    Beaulieu, C. Numerical data on neocortical neurons in adult rat, with special reference to the GABA population. Brain Res. 609, 284–292 (1993).

  28. 28

    Silberberg, G. & Markram, H. Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells. Neuron 53, 735–746 (2007).

  29. 29

    Kapfer, C., Glickfield, L.L., Atallah, B.V. & Scanziani, M. Supralinear increase of recurrent inhibition during sparse activity in the somatosensory cortex. Nat. Neurosci. 10, 743–753 (2007).

  30. 30

    Losonczy, A., Makara, J.K. & Magee, J.C. Compartmentalized dendritic plasticity and input feature storage in neurons. Nature 452, 436–441 (2008).

  31. 31

    Alonso, J.M., Usrey, W.M. & Reid, R.C. Precisely correlated firing in cells of the lateral geniculate nucleus. Nature 383, 815–819 (1996).

  32. 32

    Henze, D.A. et al. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol. 84, 390–400 (2000).

  33. 33

    Baeg, E.H. et al. Learning-induced enduring changes in functional connectivity among prefrontal cortical neurons. J. Neurosci. 27, 909–918 (2007).

  34. 34

    Perkel, D.H., Gerstein, G.L. & Moore, G.P. Neuronal spike trains and stochastic point processes. I. The single spike train. Biophys. J. 7, 391–418 (1967).

  35. 35

    Cruikshank, S.J., Lewis, T.J. & Connors, B.W. Synaptic basis for intense thalamocortical activation of feedforward inhibitory cells in neocortex. Nat. Neurosci. 10, 462–468 (2007).

  36. 36

    Pouille, F. & Scanziani, M. Routing of spike series by dynamic circuits in the hippocampus. Nature 429, 717–723 (2004).

  37. 37

    Gabernet, L., Jadhav, S.P., Feldman, D.E., Carandini, M. & Scanziani, M. Somatosensory integration controlled by dynamic thalamocortical feed-forward inhibition. Neuron 48, 315–327 (2005).

  38. 38

    Swadlow, H.A. Thalamocortical control of feed-forward inhibition in awake somatosensory 'barrel' cortex. Phil. Trans. R. Soc. Lond. B 357, 1717–1727 (2002).

  39. 39

    Martina, M., Vida, I. & Jonas, P. Distal initiation and active propagation of action potentials in interneuron dendrites. Science 287, 295–300 (2000).

  40. 40

    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).

  41. 41

    Jung, M.W., Qin, Y.L., 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).

  42. 42

    Baeg, E.H. et al. Dynamics of population code for working memory in the prefrontal cortex. Neuron 40, 177–188 (2003).

  43. 43

    Jones, M.W. & Wilson, M.A. Theta rhythms coordinate hippocampal-prefrontal interactions in a spatial memory task. PLoS Biol. 3, e402 (2005).

  44. 44

    Kargo, W.J., Szatmary, B. & Nitz, D.A. Adaptation of prefrontal cortical firing patterns and their fidelity to changes in action-reward contingencies. J. Neurosci. 27, 3548–3559 (2007).

  45. 45

    Batuev, A.S., Kursina, N.P. & Shutov, A.P. Unit activity of the medial wall of the frontal cortex during delayed performance in rats. Behav. Brain Res. 41, 95–102 (1990).

  46. 46

    Niki, H. & Watanabe, M. Prefrontal and cingulate unit activity during timing behavior in the monkey. Brain Res. 171, 213–224 (1979).

  47. 47

    Funahashi, S., Bruce, C.J. & Goldman-Rakic, P.S. Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349 (1989).

  48. 48

    Good, P. Permutation, Parametric and Bootstrap Tests of Hypotheses (Springer, New York, 2005).

  49. 49

    Westfall, P.H. & Young, S.S. Resampling-Based Multiple Testing: Examples and Methods for P-value Adjustment (Wiley, New York, 1993).

  50. 50

    Romano, J.P. & Wolf, M. Exact and approximate methods for multiple hypothesis testing. J. Am. Stat. Assoc. 100, 94–108 (2005).

Download references


We thank A. Sirota for help with data analysis and D. Robbe, K. Mizuseki, A. Renart, E. Pastalkova, S. Sakata and S. Ozen for comments on earlier versions of this manuscript. Supported by grants from the US National Institutes of Health (NS34994, MH54671), the James S. McDonnell Foundation, a US National Science Foundation Postdoctoral Fellowship in Biological Informatics (A.A.), the Uehara Memorial Foundation, the Naito Foundation, the Japan Society for the Promotion of Science (S.F.) and the US National Science Foundation (DMS-0240019) and US National Institutes of Health (MH064537) (M.T.H.). We dedicate this paper to Jenny Chandra Amarasingham.

Author information

This study was the product of an intensive collaboration between S.F. and A.A. S.F. and G.B. designed the project, S.F. conducted the experiments, A.A. and M.T.H. designed the statistical methods, S.F. and A.A. analyzed the data and A.A., S.F. and G.B. wrote the paper.

Correspondence to György Buzsáki.

Supplementary information

Supplementary Text and Figures

Supplementary Note and Supplementary Figures 1–16 (PDF 4947 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Fujisawa, S., Amarasingham, A., Harrison, M. et al. Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex. Nat Neurosci 11, 823–833 (2008) doi:10.1038/nn.2134

Download citation

Further reading