It is well proven that the functional electrophysiological behavior of in-vitro neuronal networks is influenced by the structural connectivity. Thus, the automatic extraction of the topology in large assemblies of interconnected neurons can be a valuable tool for investigating the basic mechanisms underlying high-level cognitive functions. In this paper we propose a method for estimating the structural connectivity of neuronal networks from multimodal datasets combining high-resolution Multi-Electrode Arrays (MEA) and fluorescence microscopy. Probabilistic directional features are used in a graph heat kernel framework to identify the structural connectivity of the neuronal network. Electrode connectivity maps are computed as weighted graphs in which the edge weights represent the strength of the structural connection.
Neuronal network structural connectivity estimation by probabilistic features and graph heat kernels2013 IEEE 10th International Symposium on Biomedical Imaging
Sona, Diego;
2013-01-01
Abstract
It is well proven that the functional electrophysiological behavior of in-vitro neuronal networks is influenced by the structural connectivity. Thus, the automatic extraction of the topology in large assemblies of interconnected neurons can be a valuable tool for investigating the basic mechanisms underlying high-level cognitive functions. In this paper we propose a method for estimating the structural connectivity of neuronal networks from multimodal datasets combining high-resolution Multi-Electrode Arrays (MEA) and fluorescence microscopy. Probabilistic directional features are used in a graph heat kernel framework to identify the structural connectivity of the neuronal network. Electrode connectivity maps are computed as weighted graphs in which the edge weights represent the strength of the structural connection.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
