Two’s company, three (or more) is a simplex: Algebraic-topological tools for understanding higher-order structure in neural data

Journal of Computational Neuroscience

Volumn 41, Issue 1, 2016, Pages

  Giusti, Chad ^a,b   Ghrist, Robert ^a,b   Bassett, Danielle S ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Filtration; Networks; Simplicial complex; Topology

Indexed keywords

ALGEBRA; BRAIN; COMPLEX NETWORKS; COMPUTATION THEORY; ELECTROPHYSIOLOGY;

GRAPH NETWORKS; HIGH-ORDER STRUCTURE; HIGHER-ORDER STRUCTURE; NETWORK; NETWORK SCIENCE; NEURAL DATA; NEURAL FUNCTIONS; OR-NETWORKS; SIMPLICIAL COMPLEX; SIMPLIFYING ASSUMPTIONS;

GRAPH THEORY;

BRAIN ELECTROPHYSIOLOGY; BRAIN FUNCTION; BRAIN REGION; COGNITION; FUNCTIONAL CONNECTIVITY; GEOMETRY; GRAPH THEORY; HUMAN; MATHEMATICAL COMPUTING; MATHEMATICS; NERVE CELL NETWORK; NEUROPHYSIOLOGY; NEUROSCIENCE; NONHUMAN; PRIORITY JOURNAL; REVIEW; SIMPLICIAL COMPLEX; BIOLOGICAL MODEL; BRAIN; BRAIN MAPPING; CYTOLOGY; FILTRATION; NERVE CELL; PHYSIOLOGY; THEORETICAL MODEL;

BRAIN; BRAIN MAPPING; FILTRATION; HUMANS; MODELS, NEUROLOGICAL; MODELS, THEORETICAL; NERVE NET; NEURONS;

EID: 84973617915     PISSN: 09295313     EISSN: 15736873     Source Type: Journal    
DOI: 10.1007/s10827-016-0608-6     Document Type: Review

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