Simulation Lab Neuroscience, Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Forschungszentrum Jülich Jülich, Germany.
Student of the Medical Faculty, University of Freiburg Freiburg, Germany.
Front Synaptic Neurosci. 2014 Apr 1;6:7. doi: 10.3389/fnsyn.2014.00007. eCollection 2014.
In networks with small-world topology, which are characterized by a high clustering coefficient and a short characteristic path length, information can be transmitted efficiently and at relatively low costs. The brain is composed of small-world networks, and evolution may have optimized brain connectivity for efficient information processing. Despite many studies on the impact of topology on information processing in neuronal networks, little is known about the development of network topology and the emergence of efficient small-world networks. We investigated how a simple growth process that favors short-range connections over long-range connections in combination with a synapse formation rule that generates homeostasis in post-synaptic firing rates shapes neuronal network topology. Interestingly, we found that small-world networks benefited from homeostasis by an increase in efficiency, defined as the averaged inverse of the shortest paths through the network. Efficiency particularly increased as small-world networks approached the desired level of electrical activity. Ultimately, homeostatic small-world networks became almost as efficient as random networks. The increase in efficiency was caused by the emergent property of the homeostatic growth process that neurons started forming more long-range connections, albeit at a low rate, when their electrical activity was close to the homeostatic set-point. Although global network topology continued to change when neuronal activities were around the homeostatic equilibrium, the small-world property of the network was maintained over the entire course of development. Our results may help understand how complex systems such as the brain could set up an efficient network topology in a self-organizing manner. Insights from our work may also lead to novel techniques for constructing large-scale neuronal networks by self-organization.
在具有小世界拓扑的网络中，信息可以以相对较低的成本高效传输，其特点是聚类系数高，特征路径长度短。大脑由小世界网络组成，进化可能优化了大脑连接，以实现高效的信息处理。尽管有许多关于拓扑结构对神经元网络中信息处理的影响的研究，但对于网络拓扑的发展和高效小世界网络的出现知之甚少。我们研究了一种简单的生长过程如何结合突触形成规则来塑造神经元网络拓扑，该生长过程优先考虑短程连接，而突触形成规则则使突触后放电率保持平衡。有趣的是，我们发现小世界网络通过效率的提高而受益，效率定义为通过网络的最短路径的平均倒数。当小世界网络接近所需的电活动水平时，效率特别增加。最终，具有自平衡的小世界网络几乎与随机网络一样高效。效率的提高是由自平衡生长过程的涌现特性引起的，即当神经元的电活动接近自平衡设定点时，尽管速率较低，但它们开始形成更多的远程连接。虽然当神经元活动接近自平衡时，全局网络拓扑仍在继续变化，但网络的小世界特性在整个发展过程中得以保持。我们的结果可能有助于理解大脑等复杂系统如何以自组织的方式建立有效的网络拓扑。我们工作的见解也可能为通过自组织构建大规模神经元网络提供新的技术。
