Guise Mira, Knott Alistair, Benuskova Lubica
Department of Computer Science, University of Otago Dunedin, New Zealand.
Front Comput Neurosci. 2015 Feb 5;9:9. doi: 10.3389/fncom.2015.00009. eCollection 2015.
Computational models of metaplasticity have usually focused on the modeling of single synapses (Shouval et al., 2002). In this paper we study the effect of metaplasticity on network behavior. Our guiding assumption is that the primary purpose of metaplasticity is to regulate synaptic plasticity, by increasing it when input is low and decreasing it when input is high. For our experiments we adopt a model of metaplasticity that demonstrably has this effect for a single synapse; our primary interest is in how metaplasticity thus defined affects network-level phenomena. We focus on a network-level phenomenon called polychronicity, that has a potential role in representation and memory. A network with polychronicity has the ability to produce non-synchronous but precisely timed sequences of neural firing events that can arise from strongly connected groups of neurons called polychronous neural groups (Izhikevich et al., 2004). Polychronous groups (PNGs) develop readily when spiking networks are exposed to repeated spatio-temporal stimuli under the influence of spike-timing-dependent plasticity (STDP), but are sensitive to changes in synaptic weight distribution. We use a technique we have recently developed called Response Fingerprinting to show that PNGs formed in the presence of metaplasticity are significantly larger than those with no metaplasticity. A potential mechanism for this enhancement is proposed that links an inherent property of integrator type neurons called spike latency to an increase in the tolerance of PNG neurons to jitter in their inputs.
元可塑性的计算模型通常聚焦于单个突触的建模（Shouval等人，2002年）。在本文中，我们研究元可塑性对网络行为的影响。我们的指导假设是，元可塑性的主要目的是调节突触可塑性，即在输入较低时增强它，而在输入较高时减弱它。对于我们的实验，我们采用一种元可塑性模型，该模型已证明对单个突触具有这种作用；我们主要关注这样定义的元可塑性如何影响网络层面的现象。我们聚焦于一种称为多时间性的网络层面现象，它在表征和记忆中可能发挥作用。具有多时间性的网络能够产生非同步但精确计时的神经放电事件序列，这些序列可由称为多同步神经组的强连接神经元组产生（Izhikevich等人，2004年）。当脉冲网络在脉冲时间依赖可塑性（STDP）的影响下受到重复的时空刺激时，多同步组（PNGs）很容易形成，但对突触权重分布的变化很敏感。我们使用我们最近开发的一种称为响应指纹识别的技术来表明，在存在元可塑性的情况下形成的PNGs比没有元可塑性时形成的PNGs要大得多。我们提出了一种这种增强的潜在机制，它将积分器类型神经元的一种固有属性（称为脉冲潜伏期）与PNG神经元对其输入抖动的耐受性增加联系起来。
