Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
Department of Automation, Tsinghua University, Beijing, China.
Nat Commun. 2024 Nov 15;15(1):9894. doi: 10.1038/s41467-024-54346-3.
Large-scale neural recording with single-neuron resolution has revealed the functional complexity of the neural systems. However, even under well-designed task conditions, the cortex-wide network exhibits highly dynamic trial variability, posing challenges to the conventional trial-averaged analysis. To study mesoscale trial variability, we conducted a comparative study between fluorescence imaging of layer-2/3 neurons in vivo and network simulation in silico. We imaged up to 40,000 cortical neurons' triggered responses by deep brain stimulus (DBS). And we build an in silico network to reproduce the biological phenomena we observed in vivo. We proved the existence of ineluctable trial variability and found it influenced by input amplitude and range. Moreover, we demonstrated that a spatially heterogeneous coding community accounts for more reliable inter-trial coding despite single-unit trial variability. A deeper understanding of trial variability from the perspective of a dynamical system may lead to uncovering intellectual abilities such as parallel coding and creativity.
大规模的神经记录具有单神经元分辨率，揭示了神经系统的功能复杂性。然而，即使在精心设计的任务条件下，皮层范围的网络也表现出高度动态的试验可变性，这对传统的试验平均分析构成了挑战。为了研究中尺度试验可变性，我们在活体层 2/3 神经元荧光成像和计算机模拟网络之间进行了比较研究。我们通过深部脑刺激（DBS）对多达 40000 个皮质神经元的触发反应进行成像。我们建立了一个计算机模拟网络来再现我们在体内观察到的生物学现象。我们证明了不可避免的试验可变性的存在，并发现它受输入幅度和范围的影响。此外，我们证明了尽管存在单单元试验可变性，但空间异质编码社区更能保证可靠的试验间编码。从动力系统的角度更深入地了解试验可变性可能会揭示出诸如并行编码和创造力等智力能力。
