Extracting synaptic conductances from single membrane potential traces

TL;DR

This paper introduces a novel method to estimate excitatory and inhibitory synaptic conductances from single membrane potential traces, enabling analysis of complex neural activity with minimal data.

Contribution

The paper presents the VmT method, a new approach to extract synaptic conductance parameters from single Vm traces using maximum likelihood estimation.

Findings

Successfully estimates conductances from single Vm traces

Validated with models and in vitro experiments

Potential for in vivo application

Abstract

In awake animals, the activity of the cerebral cortex is highly complex, with neurons firing irregularly with apparent Poisson statistics. One way to characterize this complexity is to take advantage of the high interconnectivity of cerebral cortex and use intracellular recordings of cortical neurons, which contain information about the activity of thousands of other cortical neurons. Identifying the membrane potential (Vm) to a stochastic process enables the extraction of important statistical signatures of this complex synaptic activity. Typically, one estimates the total synaptic conductances (excitatory and inhibitory) but this type of estimation requires at least two Vm levels and therefore cannot be applied to single Vm traces. We propose here a method to extract excitatory and inhibitory conductances (mean and variance) from single Vm traces. This "VmT method" estimates conductance parameters using maximum likelihood criteria, under the assumption are that synaptic conductances are described by Gaussian stochastic processes and are integrated by a passive leaky membrane. The method is illustrated using models and is tested on guinea-pig visual cortex neurons in vitro using dynamic-clamp experiments. The VmT method holds promises for extracting conductances from single-trial measurements, which has a high potential for in vivo applications.