Journal Article

Estimating parameters and predicting membrane voltages with conductance-based neuron models

Meliza, C. Daniel, Kostuk, Mark, Huang, Hao, Margoliash, Daniel, Nogaret, Alain, Abarbanel, Henry D. I.

2014

Overview

Recent results demonstrate techniques for fully quantitative, statistical inference of the dynamics of individual neurons under the Hodgkin–Huxley framework of voltage-gated conductances. Using a variational approximation, this approach has been successfully applied to simulated data from model neurons. Here, we use this method to analyze a population of real neurons recorded in a slice preparation of the zebra finch forebrain nucleus HVC. Our results demonstrate that using only 1,500 ms of voltage recorded while injecting a complex current waveform, we can estimate the values of 12 state variables and 72 parameters in a dynamical model, such that the model accurately predicts the responses of the neuron to novel injected currents. A less complex model produced consistently worse predictions, indicating that the additional currents contribute significantly to the dynamics of these neurons. Preliminary results indicate some differences in the channel complement of the models for different classes of HVC neurons, which accords with expectations from the biology. Whereas the model for each cell is incomplete (representing only the somatic compartment, and likely to be missing classes of channels that the real neurons possess), our approach opens the possibility to investigate in modeling the plausibility of additional classes of channels the cell might possess, thus improving the models over time. These results provide an important foundational basis for building biologically realistic network models, such as the one in HVC that contributes to the process of song production and developmental vocal learning in songbirds.

• Publisher: Springer Berlin Heidelberg,Springer,Springer Nature B.V
• Subject: Action Potentials - physiology / Animals / Bioinformatics / Biological and medical sciences / Biological effects / Biomedical and Life Sciences / Biomedicine / Biophysical Phenomena - physiology / Cell membranes. Ionic channels. Membrane pores / Cell structures and functions / Central nervous system / Channels / Complex Systems / Computer Appl. in Life Sciences / Construction / Cybernetics / Dynamics / Electric potential / Electric Stimulation / Electrophysiology / Exact sciences and technology / Fundamental and applied biological sciences. Psychology / Inference from stochastic processes; time series analysis / Ion Channels - physiology / Mathematical models / Mathematics / Membranes / Models, Neurological / Models, Statistical / Molecular and cellular biology / Nerve Net - physiology / Neural Conduction - physiology / Neurobiology / Neurons / Neurons - physiology / Neurosciences / Nonlinear Dynamics / Nonlinear dynamics and nonlinear dynamical systems / Original Paper / Patch-Clamp Techniques / Physics / Predictive Value of Tests / Probability and statistics / Reproducibility of Results / Sciences and techniques of general use / Songbirds / Statistical physics, thermodynamics, and nonlinear dynamical systems / Statistics / Synchronization ; coupled oscillators / Taeniopygia guttata / Vertebrates: nervous system and sense organs / Voltage