Reimann Michael W, Nolte Max, Scolamiero Martina, Turner Katharine, Perin Rodrigo, Chindemi Giuseppe, Dłotko Paweł, Levi Ran, Hess Kathryn, Markram Henry
Blue Brain Project, École Polytechnique Fédérale de LausanneGeneva, Switzerland.
Laboratory for Topology and Neuroscience, Brain Mind Institute, École Polytechnique Fédérale de LausanneLausanne, Switzerland.
Front Comput Neurosci. 2017 Jun 12;11:48. doi: 10.3389/fncom.2017.00048. eCollection 2017.
The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the direction of information flow, and analyzing these directed graphs using algebraic topology. Applying this approach to a local network of neurons in the neocortex revealed a remarkably intricate and previously unseen topology of synaptic connectivity. The synaptic network contains an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity. In response to stimuli, correlated activity binds synaptically connected neurons into functional cliques and cavities that evolve in a stereotypical sequence toward peak complexity. We propose that the brain processes stimuli by forming increasingly complex functional cliques and cavities.
神经网络结构与其涌现功能之间缺乏正式联系,这阻碍了我们对大脑如何处理信息的理解。现在,我们通过考虑突触传递的方向、构建反映信息流方向的网络图形并使用代数拓扑分析这些有向图,更接近于描述这种联系。将这种方法应用于新皮质中的局部神经元网络,揭示了一种极其复杂且前所未见的突触连接拓扑结构。突触网络包含大量神经元团,这些团被束缚在引导相关活动出现的空洞中。响应刺激时,相关活动通过突触将相连的神经元绑定到功能团和空洞中,这些功能团和空洞以刻板的顺序朝着峰值复杂度演化。我们提出,大脑通过形成越来越复杂的功能团和空洞来处理刺激。