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The importance of the whole: Topological data analysis for the network neuroscientist
2018
Network Neuroscience
Data analysis techniques from network science have fundamentally improved our understanding of neural systems and the complex behaviors that they support. Yet the restriction of network techniques to the study of pairwise interactions prevents us from taking into account intrinsic topological features such as cavities that may be crucial for system function. To detect and quantify these topological features, we must turn to algebro-topological methods that encode data as a simplicial complex
doi:10.1162/netn_a_00073
pmid:31410372
pmcid:PMC6663305
fatcat:svlizgxp4ne75hydwxtqibyn7e