基于神经元连接的脑网络减边演化算法

神经元间的连接以先增大后减小的方式演化,这提示了大脑神经元网络在演化过程中存在“剪除”过程。以“剪除”为启发,本文首先使用数学方法对大脑网络进行建模,然后基于大脑网络中的神经元连接方式,设计了网络减边演化算法。最后,考虑布线消耗和信息传输之间的效率,建立了大神经元优先与距离优先的演化博弈模型,探索该模型对脑网络拓扑结构特性的影响。实验结果显示,在此演化过程中呈现了中心节点度≈200、小世界特性S>1和高效率代价比等性质, 表明该算法能够有效模拟仿神经元剪除机制。上述算法和模型为有效模拟高效低能耗的脑网络提供了一种新途径。

The algorithm of cutting edge of the brain network basedon neurons connections

The connections between neurons develops according to a model of increasing at first and then decreasing ,which suggests that there is a pruning process during the evolution of neural networks. Inspired by the pruning process, this paper first carried out the numerical simulation to the network, and then established the network cutting edge and evolutionary algorithm based on the neurons connections pattern in the brain network. Finally, based on the tradeoff between the cost of routing and the efficiency of information transmission, the evolutionary game model of large neurons priority and distance priority was established, which in order to explore the influence of the model on network topology. The experimental results show that, during the process of evolution, some properties appear such as central node degree is about 200, the small world characteristic S>1 and the high efficiency cost ratio, which indicate that this algorithm can simulate the neurons pruning mechanism effectively. The algorithm and model provides a new approach for simulating high efficiency and low energy SbrainS network effectively.