Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Programme, Departments of Medicine, Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, Quebec, Canada.
Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
PLoS Comput Biol. 2022 Jun 14;18(6):e1009409. doi: 10.1371/journal.pcbi.1009409. eCollection 2022 Jun.
A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.
大量的实验研究表明，长期突触可塑性可以在突触前或突触后表达，这取决于一系列因素，如发育阶段、突触类型和活动模式。这种多样性的功能后果尚不清楚，尽管人们知道，突触后表达的可塑性主要影响突触反应幅度，而突触前表达则改变突触反应幅度和短期动力学。在大多数神经元学习模型中，长期突触可塑性被实现为连接权重的变化。将长期可塑性视为幅度的固定变化更符合突触后表达，而不是突触前表达，这意味着基于这种实现选择的理论结果可能具有突触后偏向。为了探索长期突触可塑性表达多样性的功能意义，我们改编了一个长期可塑性模型，更具体地说是尖峰时间依赖可塑性（STDP），使其可以独立地在突触前或突触后表达，或者以混合的方式表达。我们比较了基于对的标准 STDP 模型和经过生物调整的三对 STDP 模型，并在一个最小的设置中使用两种不同的学习方案进行了研究：在第一种方案中，输入在不同的延迟处触发，在第二种方案中，一部分输入是时间相关的。我们发现，突触前变化调整了学习的速度，而突触后表达在调节尖峰时间和频率方面更有效率。当结合两种表达位置时，突触后变化放大了响应范围，而突触前可塑性允许控制突触后放电率，这可能提供了一种活动平衡的形式。我们的研究结果强调了通过单个权重修改来实现突触可塑性的看似微不足道的选择可能在建模结果中无意识地引入突触后偏向。我们得出结论，突触前和突触后表达的可塑性不是可互换的，而是能够实现互补的功能。
