Xavier Vasques

The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithms to translate theoretical research into practical solutions. Previous studies have shown the advantages of quantum algorithms on artificially generated datasets, and initial experiments with small binary classification problems have yielded comparable outcomes to classical algorithms. However, it is essential to investigate the potential quantum advantage using real-world data. To the best of our knowledge, this study is the first to propose the utilization of quantum systems to classify neuron morphologies, thereby enhancing our understanding of the performance of automatic multiclass neuron classification using quantum kernel methods. We examined the influence of feature engineering on classification accuracy and found that quantum kernel methods achieved similar performance to classical methods, with certain advantages observed in various configurations.

Classification and quantitative characterization of neuronal morphologies from histological neuronal reconstruction are challenging since still unclear how to delineate a neuronal cell class and which would be the best features to define them. The morphological neuron characterization represents primary sources to address anatomical comparisons, morphometric analysis of cells or brain modeling. The objective of this paper are i), to develop and integrate a pipeline that goes from morphological features extraction to classification and ii), to assess and compare the accuracy of machine learning algorithms to classify neuron morphologies. The algorithms were trained on 430 digitally reconstructed neurons subjectively classified into layers and/or m-types using young and/or adult development state population of the somatosensory cortex in rats. For supervised algorithms, Linear Discriminant Analysis provided better classification results by comparison with others. For unsupervised, algorithms providing slightly better results are the affinity propagation and the Ward algorithms.