Efficient estimation of detailed single-neuron models
Journal
of Neurophysiology 96: 872-890.
Biophysically accurate multi-compartmental models of individual
neurones have signi cantly advanced our understanding of the
input-output function of single cells. These models depend on a large
number of parameters which are dif cult to estimate. In practise, they
are often hand tuned to match measured physiological behaviors, thus
raising questions of identifiability and interpretability. We propose a
statistical approach to the automatic estimation of various
biologically relevant parameters, including 1) the distribution of
channel densities; 2) the spatiotemporal pattern of synaptic input;
and 3) axial resistances across extended dendrites. Recent
experimental advances, notably in voltage-sensitive imaging, motivate
us to assume access to: a) the spatiotemporal voltage signal in the
dendrite, and b) an approximate description of the channel kinetics of
interest. We show here that, given a) and b), the parameters 1)-3) can
be inferred simultaneously by nonnegative linear regression; that this
optimization problem possesses a unique solution and is guaranteed to
converge despite the large number of parameters and their complex
nonlinear interaction; and that standard optimization algorithms ef
ciently reach this optimum with modest computational and data
requirements. We demonstrate that the method leads to accurate
estimations on a wide variety of challenging model data sets that
include up to on the order of 10,000 parameters (roughly two orders of
magnitude more than previously feasible), and describe how the method
gives insights into the functional interaction of groups of channels.
A preliminary account of this work appeared as "Large-scale
biophysical parameter estimation in single neurons via constrained
linear regression," by Ahrens, Huys, and Paninski, in Advances in
Neural Information Processing 2006.
Reprint (pdf, 2.5M)
| Sample
code | Liam
Paninski's research