- Poster presentation
- Open Access
Estimation of the synaptic conductance in a McKean-model neuron
BMC Neuroscience volume 16, Article number: P251 (2015)
Estimating the synaptic conductances impinging on a single neuron directly from its membrane potential is one of the open problems to be solved in order to understand the flow of information in the brain. Despite the existence of some computational strategies that give circumstantial solutions ([1–3] for instance), they all present the inconvenience that the estimation can only be done in subthreshold activity regimes. The main constraint to provide strategies for the oscillatory regimes is related to the nonlinearity of the input-output curve and the difficulty to compute it. In experimental studies it is hard to obtain these strategies and, moreover, there are no theoretical indications of how to deal with this inverse non-linear problem. In this work, we aim at giving a first proof of concept to address the estimation of synaptic conductances when the neuron is spiking. For this purpose, we use a simplified model of neuronal activity, namely a piecewise linear version of the Fitzhugh-Nagumo model, the McKean model (, among others), which allows an exact knowledge of the nonlinear f-I curve by means of standard techniques of non-smooth dynamical systems. As a first step, we are able to infer a steady synaptic conductance from the cell's oscillatory activity. As shown in Figure 1, the model shows the relative errors of the conductances of order C, where C is the membrane capacitance (C<<1), notably improving the errors obtained using filtering techniques on the membrane potential plus linear estimations, see numerical tests performed in .
Bédard C, Béhuret S, Deleuze C, Bal T, Destexhe A: Oversampling method to extract excitatory and inhibitory conductances from single-trial membrane potential recordings. Journal of Neuroscience Methods. 2012, 210: 3-14.
Lankarany M, Zhu W-P, Swamy S, Toyoizumi T: Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using Gaussian mixture Kalman filtering. Frontiers in Computational Neuroscience. 2013, 7 (109):
Rudolph M, Piwkowska Z, Badoual M, Bal T, Destexhe A: A method to estimate synaptic conductances from membrane potential fluctuations. Journal of Neurophysiology. 2004, 91 (6): 2884-2896.
Coombes S: Neuronal networks with gap junctions: A study of piecewise linear planar neuron models. SIAM Journal of Applied Dynamical Systems. 2008, 7 (3): 1101-1129.
Guillamon A, McLaughlin DW, Rinzel J: Estimation of synaptic conductances. Journal of Physiology-Paris. 2006, 100 (1-3): 31-42.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Guillamon, A., Prohens, R., Teruel, A.E. et al. Estimation of the synaptic conductance in a McKean-model neuron. BMC Neurosci 16 (Suppl 1), P251 (2015). https://doi.org/10.1186/1471-2202-16-S1-P251
- Membrane Potential
- Numerical Test
- Single Neuron
- Computational Strategy
- Activity Regime