Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking.
Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking.
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DOI：
10.3389/fncom.2011.00045
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发表时间：
2011
影响因子：
3.2
通讯作者：
Gutkin B
中科院分区：
文献类型：
--
作者：
Muller L;Brette R;Gutkin B
在海马和新皮层中，局部场电位（LFP）振荡和单个神经元的尖峰之间的耦合可以是高度精确的，跨神经元群体和细胞类型。尖峰相位（即，相对于参考振荡的尖峰时间）已知携带可靠的信息，具有锁相行为和更复杂的相位关系，例如相位进动。然而，这种精确度是如何通过神经元群体实现的，其膜特性和总输入可能是相当异质的，是未知的。在这篇文章中，我们研究了一种简单的机制，用于学习前馈网络中精确的LFP-尖峰耦合-振荡期间突触前放电率的可靠的周期性调制，再加上尖峰定时依赖的可塑性。当振荡在生物范围内（2-150 Hz）时，输入的放电率在与尖峰定时依赖可塑性（STDP）高度相关的时间尺度上变化。通过分析和计算的方法，我们发现点的稳定锁相与塑料输入突触的神经元。这些点对应于前馈网络中的精确锁相行为。这些点的位置取决于输入的振荡频率、STDP时间常数以及STDP规则中的增强和去增强的平衡。对于给定的输入振荡，STDP规则中的增强和去增强的平衡是决定输出神经元将学习发放尖峰的相位的关键参数。这些发现对内在突触后特性的变化是稳健的。最后，我们讨论了这一机制的影响，稳定学习的尖峰时间在海马。
In the hippocampus and the neocortex, the coupling between local field potential (LFP) oscillations and the spiking of single neurons can be highly precise, across neuronal populations and cell types. Spike phase (i.e., the spike time with respect to a reference oscillation) is known to carry reliable information, both with phase-locking behavior and with more complex phase relationships, such as phase precession. How this precision is achieved by neuronal populations, whose membrane properties and total input may be quite heterogeneous, is nevertheless unknown. In this note, we investigate a simple mechanism for learning precise LFP-to-spike coupling in feed-forward networks – the reliable, periodic modulation of presynaptic firing rates during oscillations, coupled with spike-timing dependent plasticity. When oscillations are within the biological range (2–150 Hz), firing rates of the inputs change on a timescale highly relevant to spike-timing dependent plasticity (STDP). Through analytic and computational methods, we find points of stable phase-locking for a neuron with plastic input synapses. These points correspond to precise phase-locking behavior in the feed-forward network. The location of these points depends on the oscillation frequency of the inputs, the STDP time constants, and the balance of potentiation and de-potentiation in the STDP rule. For a given input oscillation, the balance of potentiation and de-potentiation in the STDP rule is the critical parameter that determines the phase at which an output neuron will learn to spike. These findings are robust to changes in intrinsic post-synaptic properties. Finally, we discuss implications of this mechanism for stable learning of spike-timing in the hippocampus.
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影响因子：
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