Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Stockholm, Stockholm, Sweden.
Scilife Lab, Stockholm, Sweden.
Commun Biol. 2023 Aug 25;6(1):874. doi: 10.1038/s42003-023-05231-0.
Trial-by-trial variability is a ubiquitous property of neuronal activity in vivo which shapes the stimulus response. Computational models have revealed how local network structure and feedforward inputs shape the trial-by-trial variability. However, the role of input statistics and different interneuron subtypes in this process is less understood. To address this, we investigate the dynamics of stimulus response in a cortical microcircuit model with one excitatory and three inhibitory interneuron populations (PV, SST, VIP). Our findings demonstrate that the balance of inputs to different neuron populations and input covariances are the primary determinants of output trial-by-trial variability. The effect of input covariances is contingent on the input balances. In general, the network exhibits smaller output trial-by-trial variability in a PV-dominated regime than in an SST-dominated regime. Importantly, our work reveals mechanisms by which output trial-by-trial variability can be controlled in a context, state, and task-dependent manner.
逐次变异性是体内神经元活动的普遍特性，它塑造了刺激反应。计算模型揭示了局部网络结构和前馈输入如何塑造逐次变异性。然而，输入统计和不同中间神经元亚型在这个过程中的作用还不太清楚。为了解决这个问题，我们研究了具有一个兴奋性和三个抑制性中间神经元群体（PV、SST、VIP）的皮质微电路模型中的刺激反应动力学。我们的研究结果表明，不同神经元群体的输入平衡和输入协方差是输出逐次变异性的主要决定因素。输入协方差的影响取决于输入的平衡。一般来说，在 PV 主导的状态下，网络的输出逐次变异性比 SST 主导的状态下更小。重要的是，我们的工作揭示了在特定环境、状态和任务下，输出逐次变异性可以被控制的机制。
