Bauer Roman, Zubler Frédéric, Pfister Sabina, Hauri Andreas, Pfeiffer Michael, Muir Dylan R, Douglas Rodney J
Institute of Neuroinformatics, University/ETH Zürich, Zürich, Switzerland; School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom.
Institute of Neuroinformatics, University/ETH Zürich, Zürich, Switzerland; Department of Neurology, Inselspital Bern, Bern University Hospital, University of Bern, Bern, Switzerland.
PLoS Comput Biol. 2014 Dec 4;10(12):e1003994. doi: 10.1371/journal.pcbi.1003994. eCollection 2014 Dec.
The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.
神经回路的产前发育必须提供足够的结构,以支持至少一组核心的产后行为。尽管关于发育的各种遗传和细胞方面的知识正在迅速积累,但对于这些不同的过程如何共同作用以构建这样的功能网络,系统性的理解却较少。在这里,我们朝着这种理解迈出了一些步伐,通过详细的模拟展示了一个竞争性合作(“胜者全得”,WTA)网络架构如何从单个前体细胞发育而来。这个前体细胞被赋予一个简化的基因调控网络,该网络指导细胞有丝分裂、分化、迁移、神经突生长和突触形成。一旦建立了初始的轴突连接模式,它们的突触权重就会经历由波状输入模式塑造的稳态无监督学习。我们展示了这种自主的基因导向发育序列如何产生自我校准的WTA网络,并将我们的模拟结果与生物学数据进行比较。