TY - JOUR
T1 - Estimating parameters and predicting membrane voltages with conductance-based neuron models
AU - Meliza, C.D.
AU - Kostuk, M.
AU - Huang, H.
AU - Nogaret, A.
AU - Margoliash, D.
AU - Abarbanel, H.D.I.
PY - 2014/8/1
Y1 - 2014/8/1
N2 - 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 1,500ms 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.
AB - 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 1,500ms 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.
UR - http://www.scopus.com/inward/record.url?scp=84906948411&partnerID=8YFLogxK
U2 - 10.1007/s00422-014-0615-5
DO - 10.1007/s00422-014-0615-5
M3 - Article
AN - SCOPUS:84906948411
VL - 108
SP - 495
EP - 516
JO - Biological Cybernetics
JF - Biological Cybernetics
SN - 0340-1200
IS - 4
ER -
