Lab for Neural Circuit Theory, Riken Brain Science Institute, Saitama, Japan.
PLoS One. 2011;6(10):e25339. doi: 10.1371/journal.pone.0025339. Epub 2011 Oct 7.
Spike-timing-dependent plasticity (STDP) modifies the weight (or strength) of synaptic connections between neurons and is considered to be crucial for generating network structure. It has been observed in physiology that, in addition to spike timing, the weight update also depends on the current value of the weight. The functional implications of this feature are still largely unclear. Additive STDP gives rise to strong competition among synapses, but due to the absence of weight dependence, it requires hard boundaries to secure the stability of weight dynamics. Multiplicative STDP with linear weight dependence for depression ensures stability, but it lacks sufficiently strong competition required to obtain a clear synaptic specialization. A solution to this stability-versus-function dilemma can be found with an intermediate parametrization between additive and multiplicative STDP. Here we propose a novel solution to the dilemma, named log-STDP, whose key feature is a sublinear weight dependence for depression. Due to its specific weight dependence, this new model can produce significantly broad weight distributions with no hard upper bound, similar to those recently observed in experiments. Log-STDP induces graded competition between synapses, such that synapses receiving stronger input correlations are pushed further in the tail of (very) large weights. Strong weights are functionally important to enhance the neuronal response to synchronous spike volleys. Depending on the input configuration, multiple groups of correlated synaptic inputs exhibit either winner-share-all or winner-take-all behavior. When the configuration of input correlations changes, individual synapses quickly and robustly readapt to represent the new configuration. We also demonstrate the advantages of log-STDP for generating a stable structure of strong weights in a recurrently connected network. These properties of log-STDP are compared with those of previous models. Through long-tail weight distributions, log-STDP achieves both stable dynamics for and robust competition of synapses, which are crucial for spike-based information processing.
尖峰时间依赖可塑性（STDP）改变神经元之间突触连接的权重（或强度），被认为是产生网络结构的关键。在生理学中已经观察到，除了尖峰时间外，权重更新还取决于权重的当前值。这一特性的功能意义在很大程度上仍不清楚。加法 STDP 导致突触之间的激烈竞争，但由于缺乏权重依赖性，它需要硬性边界来确保权重动态的稳定性。具有线性权重依赖性的乘法 STDP 可确保稳定性，但缺乏足够强的竞争来获得明确的突触特化。解决稳定性与功能之间的这种困境的方法可以在加法和乘法 STDP 之间的中间参数化中找到。在这里，我们提出了一种新的解决方案，称为对数 STDP，其关键特征是抑郁的次线性权重依赖性。由于其特定的权重依赖性，这个新模型可以产生非常广泛的权重分布，没有硬性上限，类似于最近在实验中观察到的那些。对数 STDP 在突触之间产生分级竞争，使得接收更强输入相关性的突触在（非常）大权重的尾部进一步推进。强权重对于增强神经元对同步尖峰脉冲群的反应非常重要。根据输入配置，接收更强输入相关性的多个突触组表现出胜者全得或胜者全得的行为。当输入相关配置发生变化时，单个突触会迅速而稳健地重新适应以表示新的配置。我们还展示了对数 STDP 在生成具有强权重的递归连接网络中的稳定结构方面的优势。将对数 STDP 的这些特性与之前的模型进行了比较。通过长尾权重分布，对数 STDP 实现了突触的稳定动力学和稳健竞争，这对于基于尖峰的信息处理至关重要。
