Title
Estimation of Synaptic Conductances in Presence of Nonlinear Effects Caused by Subthreshold Ionic Currents.
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.
Year
DOI
Venue
2017
10.3389/fncom.2017.00069
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Keywords
Field
DocType
synaptic inhibition and excitation,quadratic integrate-and-fire model,Ornstein-Uhlenbeck process,oversampling method,spinal motoneurons,intracellular recordings of membrane potentials,maximum likelihood estimation,intrinsic currents
Membrane potential,Nonlinear system,Linear model,Excitatory postsynaptic potential,Inhibitory postsynaptic potential,Subthreshold conduction,Statistical inference,Ornstein–Uhlenbeck process,Artificial intelligence,Mathematics,Machine learning
Journal
Volume
ISSN
Citations 
11
1662-5188
2
PageRank 
References 
Authors
0.40
7
4
Name
Order
Citations
PageRank
Catalina Vich141.82
Rune W Berg271.34
Antoni Guillamon3214.51
Susanne Ditlevsen4577.84