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多神经元不应期使中枢产生的行为适应奖励。

Multi-neuronal refractory period adapts centrally generated behaviour to reward.

机构信息

Sussex Centre for Neuroscience, School of Life Sciences, University of Sussex, Brighton, United Kingdom.

出版信息

PLoS One. 2012;7(7):e42493. doi: 10.1371/journal.pone.0042493. Epub 2012 Jul 31.

Abstract

Oscillating neuronal circuits, known as central pattern generators (CPGs), are responsible for generating rhythmic behaviours such as walking, breathing and chewing. The CPG model alone however does not account for the ability of animals to adapt their future behaviour to changes in the sensory environment that signal reward. Here, using multi-electrode array (MEA) recording in an established experimental model of centrally generated rhythmic behaviour we show that the feeding CPG of Lymnaea stagnalis is itself associated with another, and hitherto unidentified, oscillating neuronal population. This extra-CPG oscillator is characterised by high population-wide activity alternating with population-wide quiescence. During the quiescent periods the CPG is refractory to activation by food-associated stimuli. Furthermore, the duration of the refractory period predicts the timing of the next activation of the CPG, which may be minutes into the future. Rewarding food stimuli and dopamine accelerate the frequency of the extra-CPG oscillator and reduce the duration of its quiescent periods. These findings indicate that dopamine adapts future feeding behaviour to the availability of food by significantly reducing the refractory period of the brain's feeding circuitry.

摘要

振荡神经元回路,即中央模式发生器(CPG),负责产生节律性行为,如行走、呼吸和咀嚼。然而,CPG 模型本身并不能解释动物能够适应未来行为的能力,以适应感官环境中奖励信号的变化。在这里,我们使用多电极阵列(MEA)记录在中央产生节律性行为的既定实验模型中,我们表明,Lymnaea stagnalis 的摄食 CPG 本身与另一个、迄今尚未确定的振荡神经元群体相关联。这个额外的 CPG 振荡器的特征是高范围活动与全范围静止交替。在静止期,CPG 对与食物相关的刺激的激活具有不应期。此外,不应期的持续时间预测 CPG 下一次激活的时间,可能是几分钟后。奖励性食物刺激和多巴胺会加速额外 CPG 振荡器的频率,并减少其静止期的持续时间。这些发现表明,多巴胺通过显著减少大脑摄食回路的不应期,使未来的摄食行为适应食物的可用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d2f/3409166/eeebe85b81e8/pone.0042493.g001.jpg

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