Discrimination between Inhibitory and Excitatory Neurons of Mouse Hippocampus Based on the Shape of Extracellular Spike Waveforms

Mehrdad Oghazian; Farzad Saffari; Ali  Khadem

doi:10.18502/fbt.v8i3.7110

Mehrdad Oghazian Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Farzad Saffari Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Ali Khadem Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran

DOI: https://doi.org/10.18502/fbt.v8i3.7110

Keywords: Inhibitory and Excitatory Neurons; Classification; Mouse Hippocampus; Shape Features; Spike Waveform; Extracellular Recordings

Abstract

Purpose: Inhibitory and excitatory neurons play an essential role in brain function, and we aim to introduce an automatic method to discriminate these two populations based on features of the shape of their spikes. Consequently, we will explain the spike extraction from raw data of a single shank electrode and determine the best features of spike waveforms for the classification of neurons. It is noteworthy that, to the best of our knowledge, classification of inhibitory and excitatory neurons using the shape features extracted from their spike waveforms has not been done before.

Materials and Methods: In this paper, we use a dataset of mouse hippocampus neurons in which the neuron types (inhibitory or excitatory) have been verified optogenetically. For the classification of mouse hippocampus neurons, we extracted eight shape features of their spike waveforms in addition to their firing rates and used three types of classifiers: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to analyze the discriminatory power of features based on the accuracy of the classifications.

Results: We showed that Spike asymmetry, Peak-to-trough ratio, Recovery slope, and Duration between peaks were four shape features of spike waveforms participated in the optimum feature subsets that resulted in maximum classification accuracy. Moreover, the SVM classifier with RBF kernel resulted in maximum accuracy of %96.91 ± %13.03 and was identified as the best classifier.

Conclusion: In this study, we found that shape features of spike waveforms can accurately classify inhibitory and excitatory neurons of mouse hippocampus. Also, we found an optimum subset of shape features of spike waveforms that resulted in better classification performance than previously proposed subsets of features used for clustering of neurons. Our findings open a promising way toward a functional classification of neurons automatically.