Abstract
Developed through evolution, brain neural system self-organizes into an economical and dynamic network structure with the modulation of repetitive neuronal firing activities through synaptic plasticity. These highly variable electric activities inevitably produce a spontaneous magnetic field, which also significantly modulates the dynamic neuronal behaviors in the brain. However, how this spontaneous electromagnetic induction affects the self-organization process and what is its role in the formation of an economical neuronal network still have not been reported. Here, we investigate the effects of spontaneous electromagnetic induction on the self-organization process and the topological properties of the self-organized neuronal network. We first find that spontaneous electromagnetic induction slows down the self-organization process of the neuronal network by decreasing the neuronal excitability. In addition, spontaneous electromagnetic induction can result in a more homogeneous directed-weighted network structure with lower causal relationship and less modularity which supports weaker neuronal synchronization. Furthermore, we show that spontaneous electromagnetic induction can reconfigure synaptic connections to optimize the economical connectivity pattern of self-organized neuronal networks, endowing it with enhanced local and global efficiency from the perspective of graph theory. Our results reveal the critical role of spontaneous electromagnetic induction in the formation of an economical self-organized neuronal network and are also helpful for understanding the evolution of the brain neural system.
Highlights
Neurons in the nervous system embedded in neural networks are connected via synapses where the efficiency of the network’s connectivity pattern is crucial for effective communication of neural information[1,2]
To investigate the self-organization process of the neuronal network structure as modulated by spontaneous electromagnetic induction, we first focus on the negative feedback of magnetic fields on neurons rather than the magnetic coupling between them, i.e., fixing the magnetic coupling strength D = 0 and varying the negative feedback strength k1
We investigate how the brain neuronal network self-organizes into an economical structure under modulation by spontaneous electromagnetic induction
Summary
The Euler-Maruyama algorithm is used to solve the differential equations with a time step of 0.005 ms and a total time of 200 ms. Despite the similar evolutionary processes of neuronal networks for different negative feedbacks, the spontaneous magnetic field significantly affects the self-organization speed. The neuronal network has a higher self-organization speed for a shorter time T This measure is apparently dependent on the fluctuation coefficient f and reflects the relative evolutionary speed. As k1 increases, the transition time T tends to increases — in particular, neuronal networks with k1 > 0.0 have significantly higher transition times than that for k1 = 0.0 — and this tendency is robust for different fluctuation coefficients (Fig. 2).
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