Laboratory for Neural Coding and Brain Computing, RIKEN Center for Brain Science, Wako, Japan.
Department of Behavior and Brain Organization, Research Center Caesar, Bonn, Germany.
Nat Neurosci. 2018 Dec;21(12):1764-1773. doi: 10.1038/s41593-018-0263-5. Epub 2018 Nov 12.
In the brain, decision making is instantiated in dedicated neural circuits. However, there is considerable individual variability in decision-making behavior, particularly under uncertainty. The origins of decision variability within these conserved neural circuits are not known. Here we demonstrate in the rat medial frontal cortex (MFC) that individual variability is a consequence of altered stability in neuronal populations. In a sensory-guided choice task, rats trained on familiar stimuli were exposed to unfamiliar stimuli, resulting in variable choice responses across individuals. We created a recurrent network model to examine the source of variability in MFC neurons, and found that the landscape of neural population trajectories explained choice variability across different unfamiliar stimuli. We experimentally confirmed model predictions showing that trial-by-trial variability in neuronal activity indexes the landscape and predicts individual variation. These results show that neural stability is a critical component of the MFC neural dynamics that underpins individual variation in decision-making.
在大脑中，决策是由专门的神经回路体现的。然而，在决策行为中存在相当大的个体差异，尤其是在不确定的情况下。这些保守的神经回路中决策变异性的起源尚不清楚。在这里，我们在大鼠的内侧前额叶皮层（MFC）中证明，个体差异是神经元群体稳定性改变的结果。在一个感觉引导的选择任务中，用熟悉的刺激训练大鼠，然后让它们接触不熟悉的刺激，导致个体的选择反应出现变化。我们创建了一个递归网络模型来研究 MFC 神经元变异性的来源，并发现神经群体轨迹的景观解释了不同不熟悉刺激下的选择变异性。我们通过实验验证了模型的预测，表明神经元活动的逐次变异性指数了景观并预测了个体的变化。这些结果表明，神经稳定性是 MFC 神经动力学的一个关键组成部分，它支持了决策中的个体差异。
