Paper Info
Title
Network-Related Challenges and Insights from Neuroscience
Abstract
At nearly every spatio-temporal scale and level of integration, the brain may be studied as a network of nearly unrivaled complexity. The network perspective provides valuable insights into the structure and function of the brain. In turn, the structure and function of the brain provide insights into the nature and capabilities of networks. As a consequence, neuroscience provides a rich offering of network-related challenges and insights for those designing networks to solve complex problems. This paper explores techniques for extracting and characterizing the networks of the brain, classification of brain function based on networks derived from fMRI, and specific challenges, such as the disambiguation of classification network representations, and functional self-organization of cortical networks. This exploration visits theory and data driven neural system modeling validated respectively by capabilities and biological experiments, analysis of biological data, and theoretical analysis of static networks. Finally, techniques that build upon the network perspective are presented.
Year
DOI
Venue
2007
10.1007/978-3-540-92191-2_7
Lecture Notes in Computer Science
Keywords
Field
DocType
functional self-organization,exploration visits theory,static network,cortical network,classification network representation,network perspective,biological data,biological experiment,theoretical analysis,complex problem,modeling,neural,analysis,neuroscience,network
Biological data,Nervous system network models,Neuroscience,Data-driven,Structure and function,Computer science,Neural system,Artificial intelligence,Systems neuroscience,Complex problems
Conference
Volume
ISSN
Citations 
5151
0302-9743
1
PageRank 
References 
Authors
0.38
8
7
Name
Order
Citations
PageRank
Charles Peck1508.67
James Kozloski2526.91
Guillermo Cecchi3324.40
Sean Hill4837.22
Felix Schürmann524527.04
Henry Markram61620199.38
Ravi Rao7142.81