Abstract
Studying and understanding human brain structures and functions have become one of the most challenging issues in neuroscience today. However, the mammalian nervous system is made up of hundreds of millions of neurons and billions of synapses. This complexity made it impossible to reconstruct such a huge nervous system in the laboratory. So, most researchers focus on C. elegans neural network. The C. elegans neural network is the only biological neural network that is fully mapped. This nervous system is the simplest neural network that exists. However, many fundamental behaviors like movement emerge from this basic network. These features made C. elegans a convenient case to study the nervous systems. Many studies try to propose a network formation model for C. elegans neural network. However, these studies could not meet all characteristics of C. elegans neural network, such as significant factors that play a role in the formation of C. elegans neural network. Thus, new models are needed to be proposed in order to explain all aspects of C. elegans neural network. In this paper, a new model based on game theory is proposed in order to understand the factors affecting the formation of nervous systems, which meet the C. elegans frontal neural network characteristics. In this model, neurons are considered to be agents. The strategy for each neuron includes either making or removing links to other neurons. After choosing the basic network, the utility function is built using structural and functional factors. In order to find the coefficients for each of these factors, linear programming is used. Finally, the output network is compared with C. elegans frontal neural network and previous models. The results implicate that the game-theoretical model proposed in this paper can better predict the influencing factors in the formation of C. elegans neural network compared to previous models.
Highlights
One of the major goals in neuroscience is studying and understanding human brain functions and structures
The results from power-law clustering random network and the ones in C. elegans frontal neural network, specially for the case of clustering coefficient, have more similarities compared to the other models
Some directed random networks are compared with C. elegans frontal neural network
Summary
One of the major goals in neuroscience is studying and understanding human brain functions and structures. A Game on Network official web site, The human brain is consist of 86 billion neurons and each neuron has 7,000 connections on average This complexity makes it impossible to models the human brain using current computers (European-Union, 2017). While some studies that use computational models and complex network analysis are conducted on the human brain, it is nearly impossible to simulate human brain structure due to complexity (Sporns et al, 2005; Elliott et al, 2017). The adult hermaphrodite worm has 302 neurons connecting through ∼6,400 chemical synapses, 900 gap junctions, and 1,500 neuromuscular junctions (Jarrell et al, 2012) Understanding how these neural networks evolved and why they are in the shape they are, can lead to a better understanding of the structure of nervous systems. The remainder of the paper organized as follows: In the first section, the related literature is reviewed in order to let the reader follow up work, in the second section the methodology that is used to implement the model is described, in the discussion section, the model is evaluated and compared with previous studies
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