Variability, compensation, and modulation in neurons and circuits
Variability, compensation, and modulation in neurons and circuits
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DOI：
10.1073/pnas.1010674108
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发表时间：
2011-09-13
影响因子：
11.1
通讯作者：
Marder, Eve
中科院分区：
文献类型：
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作者：
Marder, Eve
我总结了最近的计算和实验工作，解决了正常健康大脑中突触和固有电导的固有变异性，并表明多个解决方案（参数集）可以产生类似的电路性能。然后，我讨论了一些问题提出了这一观察，如参数变化引起的补偿机制，反映了这些特定参数的不敏感性。我想知道，具有不同底层参数的网络是否仍然能够可靠地对神经调节和其他全局扰动做出反应。在计算层面上，我描述了一个范式转变，它是越来越普遍的发展家庭的模型，反映了在生物数据的差异，模型是为了照亮，而不是单一的，高度调谐的模型。在实验方面，我讨论了固有的局限性，过度依赖平均数据，并建议，重要的是要寻找尽可能多的系统参数之间的补偿和相关性，以及每个系统参数和电路性能之间。这第二个范式转变将需要远离孤立地测量每个系统组件，但应该揭示以前未描述的复杂系统（如大脑）的组织中的重要原则。
I summarize recent computational and experimental work that addresses the inherent variability in the synaptic and intrinsic conductances in normal healthy brains and shows that multiple solutions (sets of parameters) can produce similar circuit performance. I then discuss a number of issues raised by this observation, such as which parameter variations arise from compensatory mechanisms and which reflect insensitivity to those particular parameters. I ask whether networks with different sets of underlying parameters can nonetheless respond reliably to neuromodulation and other global perturbations. At the computational level, I describe a paradigm shift in which it is becoming increasingly common to develop families of models that reflect the variance in the biological data that the models are intended to illuminate rather than single, highly tuned models. On the experimental side, I discuss the inherent limitations of overreliance on mean data and suggest that it is important to look for compensations and correlations among as many system parameters as possible, and between each system parameter and circuit performance. This second paradigm shift will require moving away from measurements of each system component in isolation but should reveal important previously undescribed principles in the organization of complex systems such as brains.