Abstract
Our brain works as a complex network system. Experiential knowledge seems to be coded into the organism’s network architecture rather than retaining only properties of individual neurons.
In order to be better able to consider the high complexity of this network architecture, extracting simple rules through both automated as well as interpretable analysis of topological patterns will be necessary in order to allow more useful observations of interrelationships within the complex neural architecture.
By combining these two types of analysis methods, we could automatically compress and naturally interpret topological patterns of functional connectivities, which produced electrical activities from many neurons simultaneously from acute slices of mice brain for 2.5 hours [Kajiwara et al. 2021].
As the first type of analysis, this study trained an artificial neural network system called Neural Network Embedding (NNE), and automatically compressed the functional connectivities into only small (25%) dimensions.
As the second type of analysis, we widely compared the compressed features with ~15 representative network metrics, having clear interpretations, including > 5 centrality-type metrics and newly developed network metrics, that quantify degrees or ratio of hubs distanced by several-nodes from initially focused hubs.
As the result, although we could give interpretations for only 55-60% of the extracted features, these new metrics, together with the commonly utilized network metrics, enabled interpretations for 80-100% features, using automated analysis.
The result demonstrates not only the fact that the NNE method surpasses limitations of commonly used human-made variables, but also the possibility that acknowledgement of our own limitations drives us to extend interpretable possibilities by developing new analytic methodologies.
Competing Interest Statement
The authors have declared no competing interest.
