The Role of Additive Neurogenesis and Synaptic Plasticity in a Hippocampal Memory Model with Grid-Cell Like Input
The Role of Additive Neurogenesis and Synaptic Plasticity in a Hippocampal Memory Model with Grid-Cell Like Input
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DOI：
10.1371/journal.pcbi.1001063
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发表时间：
2011-01-01
影响因子：
4.3
通讯作者：
Wiskott, Laurenz
中科院分区：
文献类型：
--
作者：
Appleby, Peter A.;Kempermann, Gerd;Wiskott, Laurenz
最近，我们提出了一个成年神经发生在一个简化的海马记忆模型的研究。尽管输入统计数据不断变化，但网络仍需要对记忆模式进行编码和解码。我们发现，与神经元周转和传统的突触可塑性相比，加性神经发生是一种更有效的适应策略，因为它允许网络对输入统计数据的变化做出反应，同时保留早期环境的表征。在这里，我们扩展我们的模型，包括现实的，空间驱动的输入放电模式的形式，在内嗅皮层的网格细胞。我们使用三种不同的适应策略比较了一系列空间环境中的网络性能：传统的突触可塑性，其中网络具有固定的大小，但连接是可塑的;神经元周转，其中网络具有固定的大小，但网络中的单元可能会死亡并被替换;和添加剂神经发生，其中网络开始时具有较少的初始单元，但随着时间的推移而增长。我们证实，添加剂神经发生是一个上级适应策略时，使用现实的，空间结构化的输入模式。然后，我们表明，一个更合理的生物学神经发生的规则，结合细胞死亡和增强可塑性的新颗粒细胞的整体性能显着优于任何一个单独操作的三个单独的策略。这种自适应规则可以被定制为在作为短期或长期记忆存储器操作时最大化网络的性能。我们还研究了在不同假设饲养条件下饲养的动物一生中成年神经发生的时间过程。这些生长曲线具有几个不同的特征，形成了可以通过实验进行测试的理论预测。最后，我们表明，在我们的模型中，位置细胞可以出现和细化的一个现实的方式作为一个直接的结果，由齿状回层进行稀疏化。
Recently, we presented a study of adult neurogenesis in a simplified hippocampal memory model. The network was required to encode and decode memory patterns despite changing input statistics. We showed that additive neurogenesis was a more effective adaptation strategy compared to neuronal turnover and conventional synaptic plasticity as it allowed the network to respond to changes in the input statistics while preserving representations of earlier environments. Here we extend our model to include realistic, spatially driven input firing patterns in the form of grid cells in the entorhinal cortex. We compare network performance across a sequence of spatial environments using three distinct adaptation strategies: conventional synaptic plasticity, where the network is of fixed size but the connectivity is plastic; neuronal turnover, where the network is of fixed size but units in the network may die and be replaced; and additive neurogenesis, where the network starts out with fewer initial units but grows over time. We confirm that additive neurogenesis is a superior adaptation strategy when using realistic, spatially structured input patterns. We then show that a more biologically plausible neurogenesis rule that incorporates cell death and enhanced plasticity of new granule cells has an overall performance significantly better than any one of the three individual strategies operating alone. This adaptation rule can be tailored to maximise performance of the network when operating as either a short-or long-term memory store. We also examine the time course of adult neurogenesis over the lifetime of an animal raised under different hypothetical rearing conditions. These growth profiles have several distinct features that form a theoretical prediction that could be tested experimentally. Finally, we show that place cells can emerge and refine in a realistic manner in our model as a direct result of the sparsification performed by the dentate gyrus layer.