Network-timing-dependent plasticity
Network-timing-dependent plasticity
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DOI：
10.3339/fncel.2015.00220
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发表时间：
2015-06-09
影响因子：
5.3
通讯作者：
Muller, Eilif B.
中科院分区：
文献类型：
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作者：
Delattre, Vincent;Keller, Daniel;Muller, Eilif B.
神经元网络中的活动爆发被认为传递重要信息并驱动突触可塑性。在这里，我们报告了网络突发也对峰值时间依赖的可塑性（STDP）产生深远的影响。在幼年大鼠体感觉皮层急性切片中，我们将单独诱导长期抑郁（LTD）的网络爆发与stdp诱导的长期增强（LTP）和LTD配对。我们观察到，STDP诱导的LTP要么不受影响，要么被网络突发阻塞或翻转为LTD， STDP诱导的LTD要么饱和，要么翻转为LTP，这取决于相对于STDP事件的峰值巧合的网络突发的相对时间。我们假设网络突发通过耗尽LTP所需的资源将STDP诱导的LTP翻转为LTD，因此开发了一个资源依赖的STDP学习规则。在资源依赖STDP规则影响下的神经网络模型中，我们发现兴奋性突触耦合被稳态调节以产生反映自组织临界的幂律分布的突发振幅，这是一种确保最佳信息编码的状态。
Bursts of activity in networks of neurons are thought to convey salient information and drive synaptic plasticity. Here we report that network bursts also exert a profound effect on Spike-Timing-Dependent Plasticity (STDP). In acute slices of juvenile rat somatosensory cortex we paired a network burst, which alone induced long-term depression (LTD), with STDP-induced long-term potentiation (LTP) and LTD. We observed that STDP-induced LTP was either unaffected, blocked or flipped into LTD by the network burst, and that STDP-induced LTD was either saturated or flipped into LTP, depending on the relative timing of the network burst with respect to spike coincidences of the STDP event. We hypothesized that network bursts flip STDP-induced LTP to LTD by depleting resources needed for LTP and therefore developed a resource-dependent STDP learning rule. In a model neural network under the influence of the proposed resource-dependent STDP rule, we found that excitatory synaptic coupling was homeostatically regulated to produce power law distributed burst amplitudes reflecting self-organized criticality, a state that ensures optimal information coding.