Sleep-Dependent Synaptic Down-Selection (II): Single-Neuron Level Benefits for Matching, Selectivity, and Specificity.
Sleep-Dependent Synaptic Down-Selection (II): Single-Neuron Level Benefits for Matching, Selectivity, and Specificity.
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DOI：
10.3389/fneur.2013.00148
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发表时间：
2013
影响因子：
3.4
通讯作者：
Tononi G
中科院分区：
文献类型：
--
作者：
Hashmi A;Nere A;Tononi G
在一篇配套的论文中，我们使用计算机模拟来表明，清醒时的活动依赖性，在线网络突触增强，然后在睡眠时离线突触抑制的策略，可以在系统水平上为睡眠的几个记忆益处提供一个简洁的解释，包括程序性和陈述性记忆的巩固，要点提取，以及新旧记忆的整合。在本文中，我们在单神经元水平上考虑了这两步过程的理论优势，并采用大脑与环境之间匹配的理论概念来衡量这一过程如何提高神经元捕获环境中的干扰的能力，并在内部对其进行建模。我们发现，在睡眠中的向下选择有利于增加或恢复匹配后学习，整合新的与旧的记忆，并忘记无关的材料。相比之下，替代方案，如清醒时的额外增强，睡眠时的增强，或清醒时的突触重整，减少匹配。我们还认为，通过选择合适的回路，在大脑中将前馈突触与同一树突域中的反馈突触联系起来，不同的神经元子集可以学会专门处理不同的突发事件，并形成嵌套的感知-动作回路序列。通过在清醒时与环境交互时增强这种回路，并在睡眠时与环境断开时抑制它们，神经元可以学会匹配环境的长期统计结构，同时避免虚假的功能模式和灾难性的干扰。最后，这样的两步过程还具有降低神经元学习能力饱和度和维持细胞稳态的额外好处。因此，睡眠依赖性突触重整提供了一个简约的帐户，细胞和系统水平的睡眠对学习和记忆的影响。
In a companion paper, we used computer simulations to show that a strategy of activity-dependent, on-line net synaptic potentiation during wake, followed by off-line synaptic depression during sleep, can provide a parsimonious account for several memory benefits of sleep at the systems level, including the consolidation of procedural and declarative memories, gist extraction, and integration of new with old memories. In this paper, we consider the theoretical benefits of this two-step process at the single-neuron level and employ the theoretical notion of Matching between brain and environment to measure how this process increases the ability of the neuron to capture regularities in the environment and model them internally. We show that down-selection during sleep is beneficial for increasing or restoring Matching after learning, after integrating new with old memories, and after forgetting irrelevant material. By contrast, alternative schemes, such as additional potentiation in wake, potentiation in sleep, or synaptic renormalization in wake, decrease Matching. We also argue that, by selecting appropriate loops through the brain that tie feedforward synapses with feedback ones in the same dendritic domain, different subsets of neurons can learn to specialize for different contingencies and form sequences of nested perception-action loops. By potentiating such loops when interacting with the environment in wake, and depressing them when disconnected from the environment in sleep, neurons can learn to match the long-term statistical structure of the environment while avoiding spurious modes of functioning and catastrophic interference. Finally, such a two-step process has the additional benefit of desaturating the neuron’s ability to learn and of maintaining cellular homeostasis. Thus, sleep-dependent synaptic renormalization offers a parsimonious account for both cellular and systems level effects of sleep on learning and memory.
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影响因子：
25
作者：
Ji, Daoyun;Wilson, Matthew A.
通讯作者：
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DOI：
10.1073/pnas.1119527109
发表时间：
2012-07-03
影响因子：
11.1
作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
4.3
作者：
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