AUTOMATED CONSTRUCTION OF ION-CHANNEL MODELS IN A MULTI-COMPARMENT MODELS

摘要

Disclosed is a novel system and method to develop computational models of membrane conductances through training a multi-layer perceptron to predict a channel's responses (conductances) to different conditions (voltage histories). Initially, at each time step of the dAPC protocol the current generated across the TCL1 cell membrane is determined by the history of voltages calculated in the model compartment. These data collected from dAPC are then used to train a perceptron, whose inputs are select time points in this history, and whose output predicts the channel conductances measured by the dAPC apparatus. The trained perceptron then becomes a model of the channel, comprising a specific set of historical voltage data points from compartment models provided as inputs to a specific combination of hidden units to produce an output that predicts channel conductance given a particular voltage history.