Attractor dynamics of network UP states in the neocortex

Attractor dynamics of network UP states in the neocortex
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DOI:
10.1038/nature01614
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发表时间:
2003-05-15
期刊:
影响因子:
64.8
通讯作者:
Yuste, R
Yuste, R
中科院分区:
综合性期刊1区
文献类型:
--
作者:
Cossart, R;Aronov, D;Yuste, R

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大脑皮层接收来自下脑区域的输入,传统上认为其功能是通过连续的阶段处理输入以达到适当的输出(1,2)。然而,皮层回路包含许多相互连接,包括那些来自高级中枢的反馈(3-6),即使在没有感觉输入的情况下也会持续活跃(7-9)。这种自发放电具有反映特定神经元群协调活动的结构(10-12)。此外,皮层神经元的膜电位在静息(DOWN)和去极化(UP)状态之间自发波动(11,13-16),这也可能是协调的。在感觉刺激之后,UP状态下的放电率升高(16),并为持续活动提供了基础,这是一种可能介导工作记忆的网络状态(17-21)。利用双光子钙成像技术,我们重建了小鼠视觉皮层中多达1400个神经元的自发活动动态。在这里,我们报告了同步UP状态转换(“皮质闪光”)的发生,这种状态发生在涉及少量神经元的空间组织集合中。由于其刻板的时空动态,我们得出结论,网络UP状态是电路吸引子——反馈神经网络的涌现特征(22),可以实现记忆状态或计算问题的解决方案。
The cerebral cortex receives input from lower brain regions, and its function is traditionally considered to be processing that input through successive stages to reach an appropriate output(1,2). However, the cortical circuit contains many interconnections, including those feeding back from higher centres(3-6), and is continuously active even in the absence of sensory inputs(7-9). Such spontaneous firing has a structure that reflects the coordinated activity of specific groups of neurons(10-12). Moreover, the membrane potential of cortical neurons fluctuates spontaneously between a resting ( DOWN) and a depolarized (UP) state(11,13-16), which may also be coordinated. The elevated firing rate in the UP state follows sensory stimulation(16) and provides a substrate for persistent activity, a network state that might mediate working memory(17-21). Using two-photon calcium imaging, we reconstructed the dynamics of spontaneous activity of up to 1,400 neurons in slices of mouse visual cortex. Here we report the occurrence of synchronized UP state transitions ('cortical flashes') that occur in spatially organized ensembles involving small numbers of neurons. Because of their stereotyped spatiotemporal dynamics, we conclude that network UP states are circuit attractors-emergent features of feedback neural networks(22) that could implement memory states or solutions to computational problems.