Soft-bound synaptic plasticity increases storage capacity.
Soft-bound synaptic plasticity increases storage capacity.
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DOI：
10.1371/journal.pcbi.1002836
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发表时间：
2012
影响因子：
4.3
通讯作者：
Barrett AB
中科院分区：
文献类型：
--
作者：
van Rossum MC;Shippi M;Barrett AB
准确的突触可塑性模型对于理解神经系统的适应性特性以及学习和记忆的现实模型至关重要。实验表明，突触可塑性不仅取决于突触前和突触后的活动模式，还取决于连接本身的强度。也就是说，较弱的突触比已经较强的突触更容易得到强化。这种所谓的软结合可塑性自动限制了突触强度。众所周知，这对可塑性动力学和突触权重分布具有重要影响，但其对信息存储的影响尚不清楚。在这项建模研究中，我们引入了一个信息理论框架来分析在线学习环境中的记忆存储。我们表明，软结合可塑性比硬结合可塑性将各种性能标准提高了约 18%，并且可能最大化突触的存储容量。人们普遍认为，我们的记忆存储在神经元之间的突触连接中。因此，大量实验研究检验了突触连接何时以及如何变化。与此同时，许多计算研究已经检查了记忆和突触可塑性的特性，旨在更好地理解人类记忆并允许大脑的神经网络模型。然而，大多数研究中使用的可塑性规则都高度简化，并且没有考虑到实验中发现的丰富行为。例如，在实验中观察到，很难让强突触变得更强。在这里，我们证明了可塑性的饱和增加了可存储的记忆数量，并引入了一个通用框架来计算在线学习范式中的信息存储。
Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of the connection itself. Namely, weaker synapses are more easily strengthened than already strong ones. This so called soft-bound plasticity automatically constrains the synaptic strengths. It is known that this has important consequences for the dynamics of plasticity and the synaptic weight distribution, but its impact on information storage is unknown. In this modeling study we introduce an information theoretic framework to analyse memory storage in an online learning setting. We show that soft-bound plasticity increases a variety of performance criteria by about 18% over hard-bound plasticity, and likely maximizes the storage capacity of synapses. It is generally believed that our memories are stored in the synaptic connections between neurons. Numerous experimental studies have therefore examined when and how the synaptic connections change. In parallel, many computational studies have examined the properties of memory and synaptic plasticity, aiming to better understand human memory and allow for neural network models of the brain. However, the plasticity rules used in most studies are highly simplified and do not take into account the rich behaviour found in experiments. For instance, it has been observed in experiments that it is hard to make strong synapses even stronger. Here we show that this saturation of plasticity enhances the number of memories that can be stored and introduce a general framework to calculate information storage in online learning paradigms.
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