Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks

• Published in: Molecules (Basel, Switzerland) Vol. 27; no. 19; p. 6256
• Main Authors: Barros, Michael Taynnan, Siljak, Harun, Mullen, Peter, Papadias, Constantinos, Hyttinen, Jari, Marchetti, Nicola
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 23.09.2022; MDPI
• Subjects: Algorithms; Bioinformatics; Brain; cell-classification; Classification; Communication; Comparative analysis; Computational neuroscience; cortical circuits; Firing pattern; Inference; information theory; Interfaces; Investigations; Learning algorithms; Libraries; Machine learning; Man-machine interfaces; Measurement techniques; Morphology; network tomography; Network topologies; Neural networks; neuroinformatics; Neurons; Simulation; supervised machine learning; Support vector machines; Tomography; Topology
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules27196256

The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain–machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain’s structure.