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Propagating synchrony in feed-forward networks

机译:在前馈网络中传播同步

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摘要

Coordinated patterns of precisely timed action potentials (spikes) emerge in a variety of neural circuits but their dynamical origin is still not well understood. One hypothesis states that synchronous activity propagating through feed-forward chains of groups of neurons (synfire chains) may dynamically generate such spike patterns. Additionally, synfire chains offer the possibility to enable reliable signal transmission. So far, mostly densely connected chains, often with all-to-all connectivity between groups, have been theoretically and computationally studied. Yet, such prominent feed-forward structures have not been observed experimentally. Here we analytically and numerically investigate under which conditions diluted feed-forward chains may exhibit synchrony propagation. In addition to conventional linear input summation, we study the impact of non-linear, non-additive summation accounting for the effect of fast dendritic spikes. The non-linearities promote synchronous inputs to generate precisely timed spikes. We identify how non-additive coupling relaxes the conditions on connectivity such that it enables synchrony propagation at connectivities substantially lower than required for linearly coupled chains. Although the analytical treatment is based on a simple leaky integrate-and-fire neuron model, we show how to generalize our methods to biologically more detailed neuron models and verify our results by numerical simulations with, e.g., Hodgkin Huxley type neurons.
机译:精确定时动作电位(尖峰)的协调模式出现在各种神经回路中,但它们的动力学起源仍未得到很好的理解。一种假设指出,通过神经元组前馈链(synfire链)传播的同步活动可能动态生成这种尖峰模式。另外,synfire链提供了实现可靠信号传输的可能性。到目前为止,已经在理论上和计算上研究了大多数密集连接的链,通常在组之间具有所有连接。然而,尚未通过实验观察到这种突出的前馈结构。在这里,我们通过分析和数值研究在哪些条件下稀释的前馈链可能表现出同步传播。除了常规的线性输入求和,我们还研究了非线性,非加法求和对快速树突状尖峰影响的影响。非线性促使同步输入产生精确定时的尖峰。我们确定了非加性耦合如何放松连接性条件,从而使其在比线性耦合链所需的连接性低得多的连接性上实现同步传播。尽管分析处理基于简单的泄漏积分并发射神经元模型,但我们展示了如何将我们的方法推广到生物学上更详细的神经元模型,以及如何通过使用霍奇金·赫x黎(Hodgkin Huxley)型神经元进行数值模拟来验证我们的结果。

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