ELSC, The Hebrew University of Jerusalem, Israel, Edmond Jacob Safra Campus, Givat Ram 91904, Jerusalem.
Center for Neurophysics, Physiology and Pathology, Cerebral Dynamics, Learning and Memory Lab, CNRS-UMR8119 and University Paris Descartes, 45 Rue des Saints Pères, Paris 75270, France.
Nat Commun. 2017 May 22;8:15415. doi: 10.1038/ncomms15415.
The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5-6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these 'universal' statistics.
产生可变运动的能力对于学习和调整复杂行为至关重要。这种可变性与中枢神经系统中神经元活动的时间不规则性有关。然而，神经元的不规则性实际上如何转化为行为的可变性尚不清楚。在这里，我们结合建模、电生理和行为研究来解决这个问题。我们证明，由具有拓扑组织和强烈递归的神经网络组成的模型电路可以自主产生不规则的运动行为。在鸣禽唱歌时同时记录神经元，我们发现，随着驱动歌声变化的整个电路的变化，神经元相关性增加，这与模型预测一致。通过分析行为数据，我们发现 5-6 个月大的人类婴儿和三种鸣禽的幼鸟的咿呀学语统计数据具有惊人的相似性，并表明我们的模型自然解释了这些“通用”统计数据。
