Estimating three synaptic conductances in a stochastic neural model

Abstract

We present a method for the reconstruction of three stimulus-evoked time-varying synaptic input conductances from voltage recordings. Our approach is based on exploiting the stochastic nature of synaptic conductances and membrane voltage. Starting with the assumption that the variances of the conductances are known, we use a stochastic differential equation to model dynamics of membrane potential and derive equations for first and second moments that can be solved to find conductances. We successfully apply the new reconstruction method to simulated data. We also explore the robustness of the method as the assumptions of the underlying model are relaxed. We vary the noise levels, the reversal potentials, the number of stimulus repetitions, and the accuracy of conductance variance estimation to quantify the robustness of reconstruction. These studies pave the way for the application of the method to experimental data.

Synaptic Conductances during Interictal Discharges in Pyramidal Neurons of Rat Entorhinal Cortex

“…Algebraic calculations provide estimates for the corresponding conductances if the averaged synaptic responses are recorded at two or more different membrane potentials. In spite of its common use in in vivo and in vitro studies, this method cannot be extended to measure three or more conductances while assuming the linear-voltage dependence of the synaptic currents, because under such conditions, only two input variables control a neuron (Pokrovskii, 1978 ; Odom and Borisyuk, 2012 ; Chizhov et al, 2014 ). In the present study, we estimated three conductances by exploring the non-linearity of NMDAR-mediated current and using direct measurements of current-voltage dependences for each synaptic component and its reversal potential.…”

Estimation of Synaptic Activity during Neuronal Oscillations

“…However, researchers are aware of possible misestimations coming from the measurement noise produced during recordings. Therefore, new methods have been also devised to solve this problem from different approaches: stochastic linear procedures (see [8][9][10][11][12][13]), sophisticated filtering techniques (see [14][15][16][17][18]), and techniques using the I − V relation extracted experimentally (see [19]).…”

Firing clamp: a novel method for single-trial estimation of excitatory and inhibitory synaptic neuronal conductances

“…reduce the precision of estimation. In a recent paper, Odom and Borisyuk (2012) generalized the current-clamp approach to the case of three estimated synaptic conductances with the help of multiplicative noise, but with the assumption that non-linear channels do not contribute to the recorded voltage traces and the variance of estimated conductance is known a priori .…”

“…Nevertheless, with the above stipulation, the control property described above explains that only two linear combinations of input variables such as the total synaptic conductance and total synaptic current are required to control the voltage. An important consequence following from the given assumptions is that only two input conductances may be estimated using the characteristics of the voltage trace (see also in Odom and Borisyuk, 2012). However, it should be noted that extra assumptions on the temporal characteristics of the synaptic conductances may allow further splitting of the input signals as, for example, in the multi-trial variant of (Odom and Borisyuk, 2012) with extra limitations assumed for the conductance fluctuations.…”