Predicting transgenic markers of a neuron by electrophysiological properties using machine learning.

Predicting transgenic markers of a neuron by electrophysiological properties using machine learning. Brain Res Bull. 2019 May 21;: Authors: Seo I, Lee H Abstract 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 confusion matrix. 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 neuro...
Source: Brain Research Bulletin - Category: Neurology Authors: Tags: Brain Res Bull Source Type: research