Haldeman Clayton, Beggs John M
Department of Physics, Indiana University, Bloomington, Indiana, USA.
Phys Rev Lett. 2005 Feb 11;94(5):058101. doi: 10.1103/PhysRevLett.94.058101. Epub 2005 Feb 7.
Recent experimental work has shown that activity in living neural networks can propagate as a critical branching process that revisits many metastable states. Neural network theory suggests that attracting states could store information, but little is known about how a branching process could form such states. Here we use a branching process to model actual data and to explore metastable states in the network. When we tune the branching parameter to the critical point, we find that metastable states are most numerous and that network dynamics are not attracting, but neutral.
最近的实验工作表明,活体神经网络中的活动可以作为一个关键的分支过程进行传播,该过程会多次回到许多亚稳态。神经网络理论表明,吸引态可以存储信息,但对于分支过程如何形成这样的状态却知之甚少。在这里,我们使用一个分支过程来对实际数据进行建模,并探索网络中的亚稳态。当我们将分支参数调整到临界点时,我们发现亚稳态数量最多,并且网络动力学不是吸引性的,而是中性的。