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

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• 作者：C. Daniel Meliza (1) (2)
  Mark Kostuk (3)
  Hao Huang (1)
  Alain Nogaret (4)
  Daniel Margoliash (1)
  Henry D. I. Abarbanel (5)
  
• 关键词：Data assimilation ; Neuronal dynamics ; Ion channel properties ; Song system
• 刊名：Biological Cybernetics
• 出版年：2014
• 出版时间：August 2014
• 年：2014
• 卷：108
• 期：4
• 页码：495-516
• 全文大小：3,578 KB
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• 作者单位：C. Daniel Meliza (1) (2)
  Mark Kostuk (3)
  Hao Huang (1)
  Alain Nogaret (4)
  Daniel Margoliash (1)
  Henry D. I. Abarbanel (5)
  
  1. Department of Organismal Biology and Anatomy, University of Chicago, 1027 E 57th Street, Chicago, IL, 60637, USA
  2. Department of Psychology, University of Virginia, PO Box 400400, Charlottesville, VA, 22904, USA
  3. Department of Physics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0402, USA
  4. Department of Physics, University of Bath, Claverton Down, Bath, UK
  5. Department of Physics and Marine Physical Laboratory (Scripps Institution of Oceanography), Center for Theoretical Biological Physics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0374, USA
  
• ISSN：1432-0770

文摘

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.