Structural Plasticity, Effectual Connectivity, and Memory in Cortex.
Structural Plasticity, Effectual Connectivity, and Memory in Cortex.
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DOI：
10.3389/fnana.2016.00063
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发表时间：
2016
影响因子：
2.9
通讯作者：
Sommer FT
中科院分区：
文献类型：
--
作者：
Knoblauch A;Sommer FT
学习和记忆通常归因于神经元网络中突触强度的改变。最近的实验也揭示了结构可塑性的主要作用，包括突触的消除和再生，树突棘的生长和收缩，以及轴突和树突的重塑。在这里，我们得出这样的观点：结构可塑性的一个可能功能是增加“有效连接”，以提高稀疏连接网络存储代表记忆的赫比细胞集合的能力。为此，我们将有效连通性定义为代表记忆的细胞组装中突触连接的神经元对的比例。我们通过理论和数值模拟证明了有效连通性与神经网络的信息存储能力和有效连通性之间的密切联系，有效连通性通常用于脑功能成像和连接组分析。然后，通过将我们的模型应用于最近提出的记忆模型，我们可以在假设现实连接的情况下，对可以存储在皮质大柱中的细胞组合的数量给出改进的估计。最后，我们推导了一个简化的结构可塑性模型，以实现对记忆现象的大规模模拟，并应用我们的模型将正在进行的成人结构可塑性与最近关于学习间隔效应的行为数据联系起来。
Learning and memory is commonly attributed to the modification of synaptic strengths in neuronal networks. More recent experiments have also revealed a major role of structural plasticity including elimination and regeneration of synapses, growth and retraction of dendritic spines, and remodeling of axons and dendrites. Here we work out the idea that one likely function of structural plasticity is to increase “effectual connectivity” in order to improve the capacity of sparsely connected networks to store Hebbian cell assemblies that are supposed to represent memories. For this we define effectual connectivity as the fraction of synaptically linked neuron pairs within a cell assembly representing a memory. We show by theory and numerical simulation the close links between effectual connectivity and both information storage capacity of neural networks and effective connectivity as commonly employed in functional brain imaging and connectome analysis. Then, by applying our model to a recently proposed memory model, we can give improved estimates on the number of cell assemblies that can be stored in a cortical macrocolumn assuming realistic connectivity. Finally, we derive a simplified model of structural plasticity to enable large scale simulation of memory phenomena, and apply our model to link ongoing adult structural plasticity to recent behavioral data on the spacing effect of learning.