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

Abstract

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.

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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.

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References

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

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  CAS  Google Scholar 

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Acknowledgements

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.

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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.

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Correspondence to György Buzsáki.

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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). https://doi.org/10.1038/nn.2134

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