Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity.
Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity.
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DOI：
10.1371/journal.pcbi.1004750
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发表时间：
2016-03
影响因子：
4.3
通讯作者：
Abbott LF
中科院分区：
文献类型：
--
作者：
Babadi B;Abbott LF
发放时间依赖性可塑性（Spike-timing dependent plasticity，STDP）是神经系统广泛存在的一种可塑性机制。STDP的最简单描述仅考虑成对的突触前和突触后尖峰，当突触前尖峰先于突触后尖峰时突触增强，否则抑制。根据探索各种尖峰模式的实验，基于对的STDP模型已被增强，以考虑多个突触前和突触后尖峰相互作用。因此，许多不同的“多穗”STDP模型已被提出了基于不同的实验观察。这些模型在群体水平上的行为对于理解学习和记忆机制至关重要。对于基于对的模型，突触群体的稳定性与其竞争性修饰之间具有挑战性的平衡已得到充分研究，但对于多尖峰模型，尚未对其进行充分分析。在这里，我们解决这个问题，通过数值模拟的集成和消防模型神经元与兴奋性突触受到STDP所描述的三个不同的建议多尖峰模型。我们还分析计算平均突触的变化和波动这些平均值。我们的研究结果表明，不同的多穗模型表现出相当不同的人口水平。虽然每个模型都能在某些参数区域产生突触竞争，但没有一个模型在其原始拟合参数下引起突触竞争。突触稳定性和赫布竞争之间的二分法，这是很好的特点对为基础的STDP模型，坚持在多穗模型。然而，在某些模型中，反赫布竞争可以与突触稳定性共存。我们建议，在人口水平上的突触可塑性模型的集体行为，应作为一个额外的指导方针，在应用现象学模型的基础上观察单个突触。突触可塑性被认为是学习和记忆的基础，通过竞争性加强和削弱神经网络中的突触。然而，在维持旧记忆的同时形成新记忆的能力涉及突触稳定性和竞争之间的复杂平衡。在最普遍的这种机制之一，尖峰定时依赖可塑性（STDP），突触前和突触后尖峰的时间顺序决定了它是加强还是减弱。早期对STDP的描述只考虑了成对的突触前和突触后尖峰。然而，最近的实验结果表明，“基于对”的描述不足以完全解释STDP下的突触修饰，并激发了更复杂的“多尖峰”STDP模型。虽然基于配对的STDP导致突触稳定性和/或竞争的条件得到了很好的研究，但尚不清楚多尖峰STDP模型何时以及如何导致突触稳定性和竞争。在这里，我们解决这些问题，通过数值模拟和分析人口的塑料兴奋性突触收敛到一个神经元。我们发现，不同的多尖峰STDP模型可以诱导突触的稳定性和竞争在根本不同的条件下，这有重要的意义，在学习和记忆的生物物理特性的突触。
Spike-timing dependent plasticity (STDP) is a widespread plasticity mechanism in the nervous system. The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes, with potentiation of the synapse when a presynaptic spike precedes a postsynaptic spike and depression otherwise. In light of experiments that explored a variety of spike patterns, the pair-based STDP model has been augmented to account for multiple pre- and postsynaptic spike interactions. As a result, a number of different “multi-spike” STDP models have been proposed based on different experimental observations. The behavior of these models at the population level is crucial for understanding mechanisms of learning and memory. The challenging balance between the stability of a population of synapses and their competitive modification is well studied for pair-based models, but it has not yet been fully analyzed for multi-spike models. Here, we address this issue through numerical simulations of an integrate-and-fire model neuron with excitatory synapses subject to STDP described by three different proposed multi-spike models. We also analytically calculate average synaptic changes and fluctuations about these averages. Our results indicate that the different multi-spike models behave quite differently at the population level. Although each model can produce synaptic competition in certain parameter regions, none of them induces synaptic competition with its originally fitted parameters. The dichotomy between synaptic stability and Hebbian competition, which is well characterized for pair-based STDP models, persists in multi-spike models. However, anti-Hebbian competition can coexist with synaptic stability in some models. We propose that the collective behavior of synaptic plasticity models at the population level should be used as an additional guideline in applying phenomenological models based on observations of single synapses. Synaptic plasticity is believed to underlie learning and memory by competitive strengthening and weakening of synapses in neural networks. However, the ability to form new memories while maintaining the old ones involves an intricate balance between synaptic stability and competition. In one of the most widespread such mechanisms, spike-timing dependent plasticity (STDP), the temporal order of pre- and postsynaptic spiking across a synapse determines whether it is strengthened or weakened. Early description of STDP only took into account pairs of pre- and postsynaptic spikes. However, more recent experimental results showed that the “pair-based” description is not sufficient to fully account for synaptic modifications under STDP, and motivated more complex “multi-spike” STDP models. While the conditions under which the pair-based STDP leads to synaptic stability and/or competition are well studied, it is not clear when and how multi-spike STDP models lead to synaptic stability and competition. Here, we address these questions through numerical simulation and analysis of a population of plastic excitatory synapses that converge to a neuron. We show that different multi-spike STDP models can induce synaptic stability and competition under radically different conditions, which have important implications in relating learning and memory to biophysical properties of synapses.