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Role of Graph Theory in Computational Neuroscience

Role of Graph Theory in Computational Neuroscience

Hitesh Marwaha, Anurag Sharma, Vikrant Sharma
Copyright: © 2022 |Pages: 12
ISBN13: 9781799874331|ISBN10: 1799874338|ISBN13 Softcover: 9781668441893|EISBN13: 9781799874348
DOI: 10.4018/978-1-7998-7433-1.ch005
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MLA

Marwaha, Hitesh, et al. "Role of Graph Theory in Computational Neuroscience." Futuristic Design and Intelligent Computational Techniques in Neuroscience and Neuroengineering, edited by Harjit Pal Singh, et al., IGI Global, 2022, pp. 86-97. https://doi.org/10.4018/978-1-7998-7433-1.ch005

APA

Marwaha, H., Sharma, A., & Sharma, V. (2022). Role of Graph Theory in Computational Neuroscience. In H. Singh, A. Sharma, & V. Khullar (Eds.), Futuristic Design and Intelligent Computational Techniques in Neuroscience and Neuroengineering (pp. 86-97). IGI Global. https://doi.org/10.4018/978-1-7998-7433-1.ch005

Chicago

Marwaha, Hitesh, Anurag Sharma, and Vikrant Sharma. "Role of Graph Theory in Computational Neuroscience." In Futuristic Design and Intelligent Computational Techniques in Neuroscience and Neuroengineering, edited by Harjit Pal Singh, Anurag Sharma, and Vikas Khullar, 86-97. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-7433-1.ch005

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Abstract

Neuroscience is the study of the brain and its impact on behavior and cognitive functions. Computational neuroscience is the subfield that deals with the study of the ability of the brain to think and compute. It also analyzes various electrical and chemical signals that take place in the brain to represent and process the information. In this chapter, a special focus will be given on the processing of signals by the brain to solve the problems. In the second section of the chapter, the role of graph theory is discussed to analyze the pattern of neurons. Graph-based analysis reveals meaningful information about the topological architecture of human brain networks. The graph-based analysis also discloses the networks in which most nodes are not neighbors of each other but can be reached from every other node by a small number of steps. In the end, it is concluded that by using the various operations of graph theory, the vertex centrality, betweenness, etc. can be computed to identify the dominant neurons for solving different types of computational problems.

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