Characterizing synaptic conductance fluctuations in cortical neurons and their influence on spike generation

J Neurosci Methods. 2008 Apr 30;169(2):302-22. doi: 10.1016/j.jneumeth.2007.11.010. Epub 2007 Nov 22.


Cortical neurons are subject to sustained and irregular synaptic activity which causes important fluctuations of the membrane potential (V(m)). We review here different methods to characterize this activity and its impact on spike generation. The simplified, fluctuating point-conductance model of synaptic activity provides the starting point of a variety of methods for the analysis of intracellular V(m) recordings. In this model, the synaptic excitatory and inhibitory conductances are described by Gaussian-distributed stochastic variables, or "colored conductance noise". The matching of experimentally recorded V(m) distributions to an invertible theoretical expression derived from the model allows the extraction of parameters characterizing the synaptic conductance distributions. This analysis can be complemented by the matching of experimental V(m) power spectral densities (PSDs) to a theoretical template, even though the unexpected scaling properties of experimental PSDs limit the precision of this latter approach. Building on this stochastic characterization of synaptic activity, we also propose methods to qualitatively and quantitatively evaluate spike-triggered averages of synaptic time-courses preceding spikes. This analysis points to an essential role for synaptic conductance variance in determining spike times. The presented methods are evaluated using controlled conductance injection in cortical neurons in vitro with the dynamic-clamp technique. We review their applications to the analysis of in vivo intracellular recordings in cat association cortex, which suggest a predominant role for inhibition in determining both sub- and supra-threshold dynamics of cortical neurons embedded in active networks.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Cerebral Cortex / cytology
  • Cerebral Cortex / physiology*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Electrophysiology
  • Ferrets
  • In Vitro Techniques
  • Membrane Potentials / physiology
  • Microelectrodes
  • Models, Neurological
  • Neural Conduction / physiology*
  • Neurons / physiology*
  • Synapses / physiology*