عنوان انگلیسی مقاله:
Predicting transgenic markers of a neuron by electrophysiological properties using machine learning
ترجمه فارسی عنوان مقاله:
پیش بینی نشانگرهای تراریخته یک نورون توسط خواص الکتروفیزیولوژیکی با استفاده از یادگیری ماشین
Sciencedirect - Elsevier - Brain Research Bulletin, 150 (2019) 102-110: doi:10:1016/j:brainresbull:2019:05:012
Incheol Seoa,1, Hyunsu Leeb,⁎
The task of classifying and identifying neurons, the essential components of the nervous system, has been undertaken
in a variety of ways. The transcriptomic approach has become more accessible with the development of
genetic engineering techniques. Considering the information processing function of the brain, however, it is
necessary to consider the physiological characteristics of neurons.
Recently, the Allen Institute for Brain Science has published the electrophysiological characteristics of neurons
which were tagged with a transgenic reporter. We used these electrophysiological features to predict the
transgenic markers of neurons. Using linear regression, random forest, and an artificial neural network, we
assessed the performance of supervised machine learning models by comparing the prediction accuracy or the
As a result, in the binary classification problem of classifying excitatory and inhibitory neurons, the accuracy
was 90% or more regardless of the model. The models showed better performance than merely distinguishing
neurons by suprathreshold features such as the ratio of upstrokes and downstrokes of a single spike (ρ).
However, when excitatory neurons were classified, the accuracy was 28˜47%, and the accuracy of classifying
inhibitory neurons was 59˜73%.
The present study was based on the results of electrophysiological experiments to determine whether
transgenic markers of neurons could be predicted. Future research is needed to acquire electrophysiological data
and transcriptomic data simultaneously on the single cell level to reveal the correlation between the gene expression
and the physiological function of a neuron in building the neural network.
Keywords: Neuron | Electrophysiology | Transgenic mice | Machine learning