The Intrinsic Properties of Brain Based on the Network Structure
This work addresses the fundamental challenge of linking network structure to brain functions for neuroscience, but it appears incremental as it builds on existing network models without introducing new paradigms.
The study tackled the problem of understanding brain functions through network properties, finding that network stability depends on excitatory/inhibitory synapse proportions, activity evolves into distributions related to decision-making, and short memory forms via assembly coupling.
Objective: Brain is a fantastic organ that helps creature adapting to the environment. Network is the most essential structure of brain, but the capability of a simple network is still not very clear. In this study, we try to expound some brain functions only by the network property. Methods: Every network can be equivalent to a simplified network, which is expressed by an equation set. The dynamic of the equation set can be described by some basic equations, which is based on the mathematical derivation. Results (1) In a closed network, the stability is based on the excitatory/inhibitory synapse proportion. Spike probabilities in the assembly can meet the solution of a nonlinear equation set. (2) Network activity can spontaneously evolve into a certain distribution under different stimulation, which is closely related to decision making. (3) Short memory can be formed by coupling of network assemblies. Conclusion: The essential property of a network may contribute to some important brain functions.