Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network
Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network
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DOI：
10.1162/neco_a_00003-bush
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发表时间：
2010-08-01
影响因子：
2.9
通讯作者：
O'Shea, Michael
中科院分区：
文献类型：
--
作者：
Bush, Daniel;Philippides, Andrew;O'Shea, Michael
以 BCM 公式为特征的速率编码赫布学习是一种已建立的突触可塑性计算模型。最近已经证明，体内突触强度的变化也可以明确取决于突触前和突触后放电的相对时间。这种尖峰时间依赖性可塑性（STDP）的计算模型表明，它可以提供基于局部突触变量的固有稳定性或竞争。然而，也已证明这些特性依赖于平均随机放电率的增加而抑制或不变的突触权重，这直接与经验数据相矛盾。一些分析研究已经解决了这种明显的二分法，并确定了不同且不同的 STDP 规则可以与速率编码的赫布学习相协调的条件。本研究的目的是通过依次操作标准计算 STDP 模型的每个元素来验证、统一和扩展这些先前的发现。这使我们能够确定这种可塑性规则可以复制在尖峰循环神经网络中使用速率和时间刺激协议获得的实验数据的条件。我们的结果描述了如何通过分别调整不对称学习窗口的精确轮廓和尖峰对相互作用的时间限制来操纵平均突触权重的相对规模及其对随机突触前或突触后放电率的依赖性。这些发现意味着，以前分别由对称和不对称连接介导的速率编码自关联学习和时间编码异关联学习的不同模型可以使用单个可塑性规则在单个网络中实现。然而，我们还证明，可以与速率编码赫布学习相协调的 STDP 形式不会产生固有的突触竞争，因此需要一些额外的机制来保证长期的输入输出选择性。
Rate-coded Hebbian learning, as characterized by the BCM formulation, is an established computational model of synaptic plasticity. Recently it has been demonstrated that changes in the strength of synapses in vivo can also depend explicitly on the relative timing of pre- and postsynaptic firing. Computational modeling of this spike-timing-dependent plasticity (STDP) has demonstrated that it can provide inherent stability or competition based on local synaptic variables. However, it has also been demonstrated that these properties rely on synaptic weights being either depressed or unchanged by an increase in mean stochastic firing rates, which directly contradicts empirical data. Several analytical studies have addressed this apparent dichotomy and identified conditions under which distinct and disparate STDP rules can be reconciled with rate-coded Hebbian learning. The aim of this research is to verify, unify, and expand on these previous findings by manipulating each element of a standard computational STDP model in turn. This allows us to identify the conditions under which this plasticity rule can replicate experimental data obtained using both rate and temporal stimulation protocols in a spiking recurrent neural network. Our results describe how the relative scale of mean synaptic weights and their dependence on stochastic pre- or postsynaptic firing rates can be manipulated by adjusting the exact profile of the asymmetric learning window and temporal restrictions on spike pair interactions respectively. These findings imply that previously disparate models of rate-coded autoassociative learning and temporally coded heteroassociative learning, mediated by symmetric and asymmetric connections respectively, can be implemented in a single network using a single plasticity rule. However, we also demonstrate that forms of STDP that can be reconciled with rate-coded Hebbian learning do not generate inherent synaptic competition, and thus some additional mechanism is required to guarantee long-term input-output selectivity.