A Game-Theoretical Network Formation Model for C. elegans Neural Network - Khajezade, Mohamad; Goliaei, Sama; Veisi, Hadi (Frontiers in Computational Neuroscience, 2019)
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.