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.
Subscribe to Journal
Get full journal access for 1 year
only $18.75 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Hebb, D.O. The Organization of Behavior (Wiley, New York, 1949).
Gupta, A., Wang, Y. & Markram, H. Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex. Science 287, 273–278 (2000).
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).
Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat. Neurosci. 9, 534–542 (2006).
Markram, H. et al. Interneurons of the neocortical inhibitory system. Nat. Rev. Neurosci. 5, 793–807 (2004).
Thomson, A.M. & Lamy, C. Functional maps of neocortical local circuitry. Frontiers Neurosci. 1, 19–42 (2007).
Abbott, L.F. & Regehr, W.G. Synaptic computation. Nature 431, 796–803 (2004).
Zucker, R.S. & Regehr, W.G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).
Reyes, A. et al. Target-cell-specific facilitation and depression in neocortical circuits. Nat. Neurosci. 1, 279–285 (1998).
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).
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).
Mongillo, G., Barak, O. & Tsodyks, M. Synaptic theory of working memory. Science 319, 1543–1546 (2008).
Sussillo, D., Toyoizumi, T. & Maass, W. Self-tuning of neural circuits through short-term synaptic plasticity. J. Neurophysiol. 97, 4079–4095 (2007).
Riehle, A., Grun, S., Diesmann, M. & Aertsen, A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278, 1950–1953 (1997).
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).
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).
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).
Bartho, P. et al. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. 92, 600–608 (2004).
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).
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).
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).
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).
Eichenbaum, H., Clegg, R.A. & Feeley, A. Reexamination of functional subdivisions of the rodent prefrontal cortex. Exp. Neurol. 79, 434–451 (1983).
Brody, C.D. Correlations without synchrony. Neural Comput. 11, 1537–1551 (1999).
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).
Hatsopoulos, N., Geman, S., Amarasingham, A. & Bienenstock, E. At what time scale does the nervous system operate? Neurocomputing 52–54, 25–29 (2003).
Beaulieu, C. Numerical data on neocortical neurons in adult rat, with special reference to the GABA population. Brain Res. 609, 284–292 (1993).
Silberberg, G. & Markram, H. Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells. Neuron 53, 735–746 (2007).
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).
Losonczy, A., Makara, J.K. & Magee, J.C. Compartmentalized dendritic plasticity and input feature storage in neurons. Nature 452, 436–441 (2008).
Alonso, J.M., Usrey, W.M. & Reid, R.C. Precisely correlated firing in cells of the lateral geniculate nucleus. Nature 383, 815–819 (1996).
Henze, D.A. et al. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol. 84, 390–400 (2000).
Baeg, E.H. et al. Learning-induced enduring changes in functional connectivity among prefrontal cortical neurons. J. Neurosci. 27, 909–918 (2007).
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).
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).
Pouille, F. & Scanziani, M. Routing of spike series by dynamic circuits in the hippocampus. Nature 429, 717–723 (2004).
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).
Swadlow, H.A. Thalamocortical control of feed-forward inhibition in awake somatosensory 'barrel' cortex. Phil. Trans. R. Soc. Lond. B 357, 1717–1727 (2002).
Martina, M., Vida, I. & Jonas, P. Distal initiation and active propagation of action potentials in interneuron dendrites. Science 287, 295–300 (2000).
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).
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).
Baeg, E.H. et al. Dynamics of population code for working memory in the prefrontal cortex. Neuron 40, 177–188 (2003).
Jones, M.W. & Wilson, M.A. Theta rhythms coordinate hippocampal-prefrontal interactions in a spatial memory task. PLoS Biol. 3, e402 (2005).
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).
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).
Niki, H. & Watanabe, M. Prefrontal and cingulate unit activity during timing behavior in the monkey. Brain Res. 171, 213–224 (1979).
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).
Good, P. Permutation, Parametric and Bootstrap Tests of Hypotheses (Springer, New York, 2005).
Westfall, P.H. & Young, S.S. Resampling-Based Multiple Testing: Examples and Methods for P-value Adjustment (Wiley, New York, 1993).
Romano, J.P. & Wolf, M. Exact and approximate methods for multiple hypothesis testing. J. Am. Stat. Assoc. 100, 94–108 (2005).
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.
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
Adaptive spike-artifact removal from local field potentials uncovers prominent beta and gamma band neuronal synchronization
Journal of Neuroscience Methods (2020)
Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE)
Neural Networks (2020)
Layer-Specific Physiological Features and Interlaminar Interactions in the Primary Visual Cortex of the Mouse
Probabilistic associative learning suffices for learning the temporal structure of multiple sequences
PLOS ONE (2019)