Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model.
Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model.
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DOI：
10.1016/j.bbr.2019.112180
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发表时间：
2019-12-30
影响因子：
2.7
通讯作者：
Frankland PW
中科院分区：
文献类型：
--
作者：
Tran LM;Josselyn SA;Richards BA;Frankland PW
在哺乳动物海马的齿状回区域，神经发生持续一生。计算模型已经确定，神经元的添加会降低现有的记忆（即，产生遗忘）。这些预测得到了啮齿动物实验观察的支持，在啮齿动物中，训练后神经发生的增加也促进了对依赖于大脑的记忆的遗忘。然而，在这些使用10- 1，000个神经元来代表齿状回的计算模型中，仅在新神经元添加速率大大超过体内观察到的成年神经发生速率的情况下观察到遗忘。为了解决这个问题，我们在这里生成了一个人工神经网络，它包含了海马体更真实的特征-包括增加的网络大小（多达20，000个齿状回神经元），稀疏活动和稀疏连接-这些特征在早期模型中不存在。此外，我们探索了新神经元的特性-它们的连接性，兴奋性和可塑性-如何使用模式分类任务影响遗忘。我们的结果表明，神经原性网络会忘记之前学习过的输入-输出模式关联。与静态网络（没有添加神经元）相比，这种遗忘预测了后续冲突学习的性能增强。这些影响是特别敏感的变化，增加输出连接和兴奋性的新神经元。重要的是，在更大的网络中，观察到遗忘的神经发生率要低得多，只要增加总DG人口的0.2%就足以诱导遗忘。
Neurogenesis persists throughout life in the dentate gyrus region of the mammalian hippocampus. Computational models have established that the addition of neurons degrades existing memories (i.e., produces forgetting). These predictions are supported by empirical observations in rodents, where post-training increases in neurogenesis also promote forgetting of hippocampus-dependent memories. However, in these computational models which use 10–1,000 neurons to represent the dentate gyrus, forgetting is only observed at rates of new neuron addition that greatly exceed adult neurogenesis rates observed in vivo. In order to address this, here we generated an artificial neural network which incorporated more realistic features of the hippocampus – including increased network size (with up to 20,000 dentate gyrus neurons), sparse activity, and sparse connectivity – features that were not present in earlier models. In addition, we explored how properties of new neurons – their connectivity, excitability, and plasticity – impact forgetting using a pattern categorization task. Our results revealed that neurogenic networks forget previously learned input-output pattern associations. This forgetting predicted a performance enhancement in subsequent conflictual learning, compared to static networks (with no added neurons). These effects were especially sensitive to changes in increased output connectivity and excitability of new neurons. Crucially, forgetting was observed at much lower rates of neurogenesis in larger networks, with the addition of as little as 0.2% of the total DG population sufficient to induce forgetting.
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影响因子：
5.3
作者：
Gao, Aijing;Xia, Frances;Frankland, Paul W.
通讯作者：
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