Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
A relational model of data for large shared data banks
Communications of the ACM
Neural Network Classification and Prior Class Probabilities
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Applications of Computational Intelligence in Biology: Current Trends and Open Problems
Applications of Computational Intelligence in Biology: Current Trends and Open Problems
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To model a biological system despite a lack of complete information, statistical and machine learning can be used to replace a missing component with a classifier that is trained to give a near-optimal estimation of a target behavior. By filling the information gap in the system, this classifier can improve the analysis of better known components. We applied this approach to study the parameters of a proposed activity sensor of a biological neuronal network model by replacing the unknown sensor readout mechanism with an artificial neural network classifier. The classifier derives an error signal for homeostatic regulation of the pattern-generating neuronal network from the lobster stomatogastric ganglion. Using this approach, we predict optimal biological activity sensor parameters for homeostatic regulation and also provide insights into the biological architecture of the replaced sensor readout mechanism itself.