Journal of Computational Neuroscience

, Volume 15, Issue 1, pp 71–90 | Cite as

Synchronization of Strongly Coupled Excitatory Neurons: Relating Network Behavior to Biophysics

  • Corey D. Acker
  • Nancy Kopell
  • John A. White


Behavior of a network of neurons is closely tied to the properties of the individual neurons. We study this relationship in models of layer II stellate cells (SCs) of the medial entorhinal cortex. SCs are thought to contribute to the mammalian theta rhythm (4–12 Hz), and are notable for the slow ionic conductances that constrain them to fire at rates within this frequency range. We apply “spike time response” (STR) methods, in which the effects of synaptic perturbations on the timing of subsequent spikes are used to predict how these neurons may synchronize at theta frequencies. Predictions from STR methods are verified using network simulations. Slow conductances often make small inputs “effectively large”; we suggest that this is due to reduced attractiveness or stability of the spiking limit cycle. When inputs are (effectively) large, changes in firing times depend nonlinearly on synaptic strength. One consequence of nonlinearityis to make a periodically firing model skip one or more beats, often leading to the elimination of the anti-synchronous state in bistable models. Biologically realistic membrane noise makes such “cycle skipping” more prevalent, and thus can eradicate bistability. Membrane noise also supports “sparse synchrony,” a phenomenon in which subthreshold behavior is uncorrelated, but there are brief periods of synchronous spiking.

synchrony phase response theta rhythm cycle skipping membrane noise 


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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Corey D. Acker
    • 1
  • Nancy Kopell
    • 2
  • John A. White
    • 3
  1. 1.Department of Biomedical Engineering, Center for BioDynamicsBoston UniversityBostonUSA
  2. 2.Department of Mathematics, Center for BioDynamicsBoston UniversityBostonUSA
  3. 3.Department of Biomedical Engineering, Center for BioDynamics, Center for Memory and BrainBoston UniversityBostonUSA

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