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Inhibitory stabilization and cortical computation
Nature Reviews Neuroscience ( IF 34.7 ) Pub Date : 2020-11-11 , DOI: 10.1038/s41583-020-00390-z
Sadra Sadeh , Claudia Clopath

Neuronal networks with strong recurrent connectivity provide the brain with a powerful means to perform complex computational tasks. However, high-gain excitatory networks are susceptible to instability, which can lead to runaway activity, as manifested in pathological regimes such as epilepsy. Inhibitory stabilization offers a dynamic, fast and flexible compensatory mechanism to balance otherwise unstable networks, thus enabling the brain to operate in its most efficient regimes. Here we review recent experimental evidence for the presence of such inhibition-stabilized dynamics in the brain and discuss their consequences for cortical computation. We show how the study of inhibition-stabilized networks in the brain has been facilitated by recent advances in the technological toolbox and perturbative techniques, as well as a concomitant development of biologically realistic computational models. By outlining future avenues, we suggest that inhibitory stabilization can offer an exemplary case of how experimental neuroscience can progress in tandem with technology and theory to advance our understanding of the brain.



中文翻译:

抑制性稳定和皮层计算

具有强大的循环连接性的神经网络为大脑提供了执行复杂计算任务的强大手段。但是,高增益兴奋性网络容易受到不稳定的影响,这可能导致失控的活动,这在诸如癫痫病之类的病态中表现出来。抑制性稳定提供了动态,快速和灵活的补偿机制,以平衡原本不稳定的网络,从而使大脑能够在其最有效的状态下进行操作。在这里,我们审查了大脑中这种抑制稳定的动力学的最新实验证据,并讨论了它们对皮层计算的影响。我们展示了技术工具箱和微扰技术的最新进展如何促进了大脑中抑制稳定网络的研究,以及随之而来的生物学现实计算模型的发展。通过概述未来的途径,我们建议抑制性稳定可以提供一个示例性案例,说明实验神经科学如何与技术和理论一起发展以增进我们对大脑的理解。

更新日期:2020-11-12
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