  Classification of biological neuron types and networks poses challenges to
the full understanding of the brain's organisation and functioning. In this
paper, we develop a novel objective classification model of biological neuronal
types and networks based on the communication metrics of neurons. This presents
advantages against the existing approaches since the mutual information or the
delay between neurons obtained from spike trains are more abundant data compare
to conventional morphological data. We firstly designed two open-access
supporting computational platforms of various neuronal circuits from the Blue
Brain Project realistic models, named Neurpy and Neurgen. Then we investigate
how the concept of network tomography could be achieved with cortical neuronal
circuits for morphological, topological and electrical classification of
neurons. We extract the simulated data to many different classifiers (including
SVM, Decision Trees, Random Forest, and Artificial Neuron Networks) classifying
the specific cell type (and sub-group types) achieving accuracies of up to
70\%. Inference of biological network structures using network tomography
reached up to 65\% of accuracy. We also analysed recall, precision and F1score
of the classification of five layers, 25 cell m-types, and 14 cell e-types. Our
research not only contributes to existing classification efforts but sets the
road-map for future usage of cellular-scaled brain-machine interfaces for
in-vivo objective classification of neurons as a sensing mechanism of the
brain's structure.
