Abstract
Subthreshold fluctuations in neuronal membrane potential traces contain nonlinear components, and employing nonlinear models might improve the statistical inference. We propose a new strategy to estimate synaptic conductances, which has been tested using in silico data and applied to in vivo recordings. The model is constructed to capture the nonlinearities caused by subthreshold activated currents, and the estimation procedure can discern between excitatory and inhibitory conductances using only one membrane potential trace. More precisely, we perform second order approximations of biophysical models to capture the subthreshold nonlinearities, resulting in quadratic integrate-and-fire models, and apply approximate maximum likelihood estimation where we only suppose that conductances are stationary in a 50–100 ms time window. The results show an improvement compared to existent procedures for the models tested here.
Highlights
Unveiling the information that a neuron receives from other neurons and distinguishing between excitatory and inhibitory inputs is an important task in neuroscience as it provides valuable information on local connectivity and brain operating conditions
We generate in silico voltage traces from computational neuron models using prescribed synaptic inputs generated from Ornstein-Uhlenbeck processes with sinusoidal drift, or use in vivo data of intracellular recordings of a spinal motoneuron subjected to rhythmic synaptic bombardments from the surrounding network
The results obtained from the stellate neuron model as well as from experimental recordings, are compared to both the oversampling method and the OU method
Summary
Unveiling the information that a neuron receives from other neurons and distinguishing between excitatory and inhibitory inputs is an important task in neuroscience as it provides valuable information on local connectivity and brain operating conditions. From an experimental point of view, this is difficult due to the diversity of synaptic inputs and their unattainable conductances. Inverse methods are sought to retrieve the dynamics of mean synaptic conductances from measurements of the membrane potential of the neuron. It is desirable that such methods do not rely on repeated trials since it is difficult to assume exactly repeated synaptic input from trial to trial. Estimation of excitatory and inhibitory conductances from single time courses of the membrane potential is preferable. One has to take into account the presence of noise as well as potential nonlinearity in the experimental data
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