Mesoscale neuronal granular trial variability in vivo illustrated by nonlinear recurrent network in silico

Guihua Xiao, Yeyi Cai, Yuanlong Zhang, Jingyu Xie, Lifan Wu, Hao Xie, Jiamin Wu () and Qionghai Dai ()
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Guihua Xiao: Tsinghua University
Yeyi Cai: Tsinghua University
Yuanlong Zhang: Tsinghua University
Jingyu Xie: Tsinghua University
Lifan Wu: Tsinghua University
Hao Xie: Tsinghua University
Jiamin Wu: Tsinghua University
Qionghai Dai: Tsinghua University

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-54346-3

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