Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data

Abstract

Understanding the mechanisms by which neurons create or suppress connections to enable communication in brain-derived neuronal cultures can inform how learning, cognition and creative behavior emerge. While prior studies have shown that neuronal cultures possess self-organizing criticality properties, we further demonstrate that in vitro brain-derived neuronal cultures exhibit a self-optimization phenomenon. More precisely, we analyze the multiscale neural growth data obtained from label-free quantitative microscopic imaging experiments and reconstruct the in vitro neuronal culture networks (microscale) and neuronal culture cluster networks (mesoscale). We investigate the structure and evolution of neuronal culture networks and neuronal culture cluster networks by estimating the importance of each network node and their information flow. By analyzing the degree-, closeness-, and betweenness-centrality, the node-to-node degree distribution (informing on neuronal interconnection phenomena), the clustering coefficient/transitivity (assessing the “small-world” properties), and the multifractal spectrum, we demonstrate that murine neurons exhibit self-optimizing behavior over time with topological characteristics distinct from existing complex network models. The time-evolving interconnection among murine neurons optimizes the network information flow, network robustness, and self-organization degree. These findings have complex implications for modeling neuronal cultures and potentially on how to design biological inspired artificial intelligence.

Prediction of Chinese Semantic Word-Building Patterns Based on Complex Network Features

“…After understanding the related problems in the field of natural language processing, Shen et al [16] found that the research on word formation based on semantic perspective is of great significance to solve the ambiguity and polysemy of unregistered words and texts. Yin et al [17] combined conceptual integration theory with physical structure, which complements each other, and provided a good reference method for us to analyze the specific ways of semantic word formation. Chen et al [18] considered that noun use belongs to logical metonymy, which can be explained by event coercion in generative thesaurus theory, and put forward that physical structure is equivalent to noun and argument structure is equivalent to verb.…”

Deciphering the generating rules and functionalities of complex networks

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Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks

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