Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations

Abstract

Berg RW, Ditlevsen S. Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations. J Neurophysiol 110: 1021-1034. First published May 1, 2013 doi:10.1152/jn.00006.2013.-When recording the membrane potential, V, of a neuron it is desirable to be able to extract the synaptic input. Critically, the synaptic input is stochastic and nonreproducible so one is therefore often restricted to single-trial data. Here, we introduce means of estimating the inhibition and excitation and their confidence limits from single sweep trials. The estimates are based on the mean membrane potential, V, and the membrane time constant, . The time constant provides the total conductance (G ϭ capacitance/) and is extracted from the autocorrelation of V. The synaptic conductances can then be inferred from V when approximating the neuron as a single compartment. We further employ a stochastic model to establish limits of confidence. The method is verified on models and experimental data, where the synaptic input is manipulated pharmacologically or estimated by an alternative method. The method gives best results if the synaptic input is large compared with other conductances, the intrinsic conductances have little or no time dependence or are comparably small, the ligand-gated kinetics is faster than the membrane time constant, and the majority of synaptic contacts are electrotonically close to soma (recording site). Although our data are in current clamp, the method also works in V-clamp recordings, with some minor adaptations. All custom made procedures are provided in Matlab.

Spiking neurons : interacting Hawkes processes, mean field limits and oscillations

“…where L is given in Assumption 1.1, then there exists a unique stationary Hawkes process (Z i (t)) i∈I,t∈R with intensity given by (4). Moreover, in this case, we dispose of a perfect simulation algorithm of the stationary measure.…”

“…Suppose we observe a large homogeneous population of N neurons evolving according to (4), where I = {1, . .…”

“…The biological motivation for this paper comes from the rhythmic scratch like network activity in the turtle, induced by a mechanical stimulus, and recorded and analyzed by Berg and co-workers [2][3][4]29]. Oscillations in a spinal motoneuron are initiated by the sensory input, and continue by some internal mechanisms for some time after the stimulus is terminated.…”

Estimation of Synaptic Conductance in the Spiking Regime for the McKean Neuron Model

“…These input estimations constitute an important piece of information to understand the organization of activity in the populations of efferent neurons. In particular, they can shed light to the excitation versus inhibition balance along time in the subjacent network (see the Introduction of Berg and Ditlevsen (2013) and Vich and Guillamon (2015) for specific applications).…”

“…Aiming at giving methods as general as possible, the variety of neuron types does not advise to use very specific models, but rather minimal models that capture essential features of neuronal dynamics. This idea has been extensively used in the existing literature (see Borg-Graham et al (1998), Anderson et al (2000), Wehr and Zador (2003), Rudolph et al (2004), Pospischil et al (2009), Bédard et al (2011), Berg and Ditlevsen (2013), among others) assuming that data is following some underlying linear process. Normally, this assumption involves a complementary treatment of the noise present in the data, which is the second main obstacle.…”