Zuzanna Piwkowska | PubMed Health

PubMed2006

Calculating event-triggered average synaptic conductances from the membrane potential.

The optimal patterns of synaptic conductances for spike generation in central neurons is a subject of considerable interest. Ideally such conductance time courses should be extracted from membrane potential (V(m)) activity, but this is difficult because the nonlinear contribution of conductances to the V(m) renders their estimation from the membrane equation extremely sensitive. We outline here a solution to this problem based on a discretization of the time axis. This procedure can extract the time course of excitatory and inhibitory conductances solely from the analysis of V(m) activity. We test this method by calculating spike-triggered averages of synaptic conductances using numerical simulations of the integrate-and-fire model subject to colored conductance noise. The procedure was also tested successfully in biological cortical neurons using conductance noise injected with dynamic clamp. This method should allow the extraction of synaptic conductances from V(m) recordings in vivo.

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PubMed2004

A method to estimate synaptic conductances from membrane potential fluctuations.

In neocortical neurons, network activity can activate a large number of synaptic inputs, resulting in highly irregular subthreshold membrane potential (V(m)) fluctuations, commonly called "synaptic noise." This activity contains information about the underlying network dynamics, but it is not easy to extract network properties from such complex and irregular activity. Here, we propose a method to estimate properties of network activity from intracellular recordings and test this method using theoretical and experimental approaches. The method is based on the analytic expression of the subthreshold V(m) distribution at steady state in conductance-based models. Fitting this analytic expression to V(m) distributions obtained from intracellular recordings provides estimates of the mean and variance of excitatory and inhibitory conductances. We test the accuracy of these estimates against computational models of increasing complexity. We also test the method using dynamic-clamp recordings of neocortical neurons in vitro. By using an on-line analysis procedure, we show that the measured conductances from spontaneous network activity can be used to re-create artificial states equivalent to real network activity. This approach should be applicable to intracellular recordings during different network states in vivo, providing a characterization of the global properties of synaptic conductances and possible insight into the underlying network mechanisms.

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