Title: An optimization method to simultaneously estimate electrophysiology and connectivity in a model central pattern generator,
Abstract: Central pattern generators (CPGs) appear to have evolved multiple times
throughout the animal kingdom, indicating that their design imparts a
significant evolutionary advantage. Insight into how this design is achieved is
hindered by the difficulty inherent in examining relationships among
electrophysiological properties of the constituent cells of a CPG and their
functional connectivity. That is: experimentally it is challenging to estimate
the values of more than two or three of these properties simultaneously. We
employ a method of statistical data assimilation (D.A.) to estimate the
synaptic weights, synaptic reversal potentials, and maximum conductances of ion
channels of the constituent neurons in a multi-modal network model. We then use
these estimates to predict the functional mode of activity that the network is
expressing. The measurements used are the membrane voltage time series of all
neurons in the circuit. We find that these measurements provide sufficient
information to yield accurate predictions of the network's associated
electrical activity. This experiment can apply directly in a real laboratory
using intracellular recordings from a known biological CPG whose structural
mapping is known, and which can be completely isolated from the animal. The
simulated results in this paper suggest that D.A. might provide a tool for
simultaneously estimating tens to hundreds of CPG properties, thereby offering
the opportunity to seek possible systematic relationships among these
properties and the emergent electrical activity.