  Multi-Electrode Arrays (MEAs) have been widely used to record neuronal
activities, which could be used in the diagnosis of gene defects and drug
effects. In this paper, we address the problem of classifying in vitro MEA
recordings of mouse and human neuronal cultures from different genotypes, where
there is no easy way to directly utilize raw sequences as inputs to train an
end-to-end classification model. While carefully extracting some features by
hand could partially solve the problem, this approach suffers from obvious
drawbacks such as difficulty of generalizing. We propose a deep learning
framework to address this challenge. Our approach correctly classifies neuronal
culture data prepared from two different genotypes -- a mouse Knockout of the
delta-catenin gene and human induced Pluripotent Stem Cell-derived neurons from
Williams syndrome. By splitting the long recordings into short slices for
training, and applying Consensus Prediction during testing, our deep learning
approach improves the prediction accuracy by 16.69% compared with feature based
Logistic Regression for mouse MEA recordings. We further achieve an accuracy of
95.91% using Consensus Prediction in one subset of mouse MEA recording data,
which were all recorded at six days in vitro. As high-density MEA recordings
become more widely available, this approach could be generalized for
classification of neurons carrying different mutations and classification of
drug responses.
