成人神经发生的计算模型

J. Aimone, Laurenz Wiskott
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引用次数: 7

摘要

成人和发育中的神经发生之间最有趣的区别之一是,在成人大脑中,新的神经元正在整合到已经发育的功能回路中。新生神经元发育出高度复杂的神经元形态——考虑到细胞外信号环境(在发育过程中被认为是重要的)与成年神经元有很大不同,这是一项令人印象深刻的壮举。在大多数动物物种中,无论是在正常的生命过程中,还是在许多非哺乳动物对损伤的反应中,都观察到成年神经发生。事实上,成年神经发生基本上局限于哺乳动物大脑的两个区域,这表明在这些区域(嗅球[OB]和齿状回[DG])添加新的神经元是特别重要的。虽然再生神经发生的功能是不言而喻的,但终身神经发生的目的尚不清楚。采用计算建模方法有潜力的原因有几个。其一,任何增加新神经元的影响都会首先在网络中表现出来,然后才会在行为上被观察到。建模可以观察到在标准行为分析中看不到的效果。这提供了一个框架,通过这个框架可以做出新的预测,这些预测可以通过实验进行具体检验。此外,一个发展良好的计算模型或理论可以以一种在动物模型中不切实际或不可能的方式进行改变,例如将神经发生率提高十倍或研究神经发生对非神经发生区域的影响。最后,对系统的计算方面进行建模通常有助于关注未来的实验,…
本文章由计算机程序翻译,如有差异,请以英文原文为准。
22 Computational Modeling of Adult Neurogenesis
One of the most intriguing differences between adult and developmental neurogenesis is that in the adult brain, new neurons are integrating into already-developed, functioning circuits. Newborn neurons develop highly complex neuronal morphology—an impressive feat, considering that the extracellular signaling environment (thought to be important during development) is considerably different in the adult. Adult neurogenesis has been observed in most animal species, both in the normal course of life and in response to injury in many nonmammals. The fact that adult neurogenesis is essentially limited to two regions in mammalian brains suggests that the addition of new neurons to these regions (the olfactory bulb [OB] and dentate gyrus [DG]) is of particular importance. Although the function of regenerative neurogenesis is self-evident, the purpose for lifelong neurogenesis remains unclear. There are several reasons why taking a computational modeling approach has potential. One is that any effect of adding new neurons will first be manifested computationally in the network and will only then be observed behaviorally. Modeling can permit the observation of an effect that otherwise would go unseen in standard behavioral assays. This provides a framework by which new predictions can be made that can be specifically tested experimentally. Furthermore, a well-developed computational model or theory can be altered in a manner that is impractical or impossible in animal models, such as increasing the rate of neurogenesis by tenfold or studying the effects of neurogenesis in nonneurogenic areas. Finally, modeling the computational aspects of a system often helps focus future experiments,..
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