Efficient fitting of conductance-based model neurons from somatic current clamp
- 597 Downloads
Estimating biologically realistic model neurons from electrophysiological data is a key issue in neuroscience that is central to understanding neuronal function and network behavior. However, directly fitting detailed Hodgkin–Huxley type model neurons to somatic membrane potential data is a notoriously difficult optimization problem that can require hours/days of supercomputing time. Here we extend an efficient technique that indirectly matches neuronal currents derived from somatic membrane potential data to two-compartment model neurons with passive dendrites. In consequence, this approach can fit semi-realistic detailed model neurons in a few minutes. For validation, fits are obtained to model-derived data for various thalamo-cortical neuron types, including fast/regular spiking and bursting neurons. A key aspect of the validation is sensitivity testing to perturbations arising in experimental data, including sampling rates, inadequately estimated membrane dynamics/channel kinetics and intrinsic noise. We find that maximal conductance estimates and the resulting membrane potential fits diverge smoothly and monotonically from near-perfect matches when unperturbed. Curiously, some perturbations have little effect on the error because they are compensated by the fitted maximal conductances. Therefore, the extended current-based technique applies well under moderately inaccurate model assumptions, as required for application to experimental data. Furthermore, the accompanying perturbation analysis gives insights into neuronal homeostasis, whereby tuning intrinsic neuronal properties can compensate changes from development or neurodegeneration.
KeywordsConductance-based model neuron Hodgkin–Huxley model Model fitting Optimization Neuronal homeostasis Sensitivity Neocortex Pyramidal neuron Fast spiking neuron Genesis Minimal model
The authors thank Darren Hoyland, Mark Humphries and Ric Wood for valuable advice and discussions. This work was supported by EPSRC (CARMEN: EP/E002331/1).
- Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology (London), 117, 500–544.Google Scholar
- LeMasson, G., & Maex, R. (2001). Introduction to equation solving and parameter fitting. In Computational neuroscience: Realistic modelling for experimentalists (pp. 1–25).Google Scholar
- Moore, E. H. (1920). On the reciprocal of the general algebraic matrix. Bulletin of the American Mathematical Society, 26, 394–395.Google Scholar
- Morse, T. M., Davison, A. P., & Hines, M. (2001). Parameter space reduction in neuron model optimization through minimization of residual voltage clamp current. In 31st Annual Meeting of the Society for Neuroscience, San Diego, CA, USA, 10–15 November 2001. Society for Neuroscience Abstracts (Vol. 27).Google Scholar
- Wood, R., & Gurney, K. N. (2002). A new parametric search technique for biophysical models. A study in the rat neostriatal projection neuron. In 32nd annual meeting of the Society for Neuroscience, Orlando, FL, USA, 2–7 November 2002. Society for Neuroscience abstracts (Vol. 28).Google Scholar