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Estimating three synaptic conductances in a stochastic neural model

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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.

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Acknowledgements

This work was supported by National Science Foundation grants NSF-DMS-1022945 and NSF-0354259. The authors also would like to thank Gary Rose and Jeremy Wilkerson for stimulating discussions.

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Authors and Affiliations

  1. Department of Mathematics, University of Utah, 155 S 1400 E, Salt Lake City, UT, 84112, USA

    Stephen E. Odom & Alla Borisyuk

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  1. Stephen E. Odom
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  2. Alla Borisyuk
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Correspondence to Alla Borisyuk.

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Action Editor: Frances K. Skinner

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Odom, S.E., Borisyuk, A. Estimating three synaptic conductances in a stochastic neural model. J Comput Neurosci 33, 191–205 (2012). https://doi.org/10.1007/s10827-012-0382-z

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  • DOI: https://doi.org/10.1007/s10827-012-0382-z

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