Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA.
Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
Nat Neurosci. 2021 Jan;24(1):129-139. doi: 10.1038/s41593-020-00753-w. Epub 2020 Dec 7.
Cortical neurons process information on multiple timescales, and areas important for working memory (WM) contain neurons capable of integrating information over a long timescale. However, the underlying mechanisms for the emergence of neuronal timescales stable enough to support WM are unclear. By analyzing a spiking recurrent neural network model trained on a WM task and activity of single neurons in the primate prefrontal cortex, we show that the temporal properties of our model and the neural data are remarkably similar. Dissecting our recurrent neural network model revealed strong inhibitory-to-inhibitory connections underlying a disinhibitory microcircuit as a critical component for long neuronal timescales and WM maintenance. We also found that enhancing inhibitory-to-inhibitory connections led to more stable temporal dynamics and improved task performance. Finally, we show that a network with such microcircuitry can perform other tasks without disrupting its pre-existing timescale architecture, suggesting that strong inhibitory signaling underlies a flexible WM network.
皮质神经元在多个时间尺度上处理信息，而对于工作记忆 (WM) 很重要的区域包含能够在长时间尺度上整合信息的神经元。然而，支持 WM 的神经元时间尺度稳定出现的潜在机制尚不清楚。通过分析在 WM 任务上训练的尖峰发放递归神经网络模型和灵长类动物前额叶皮层中单个神经元的活动，我们表明我们的模型和神经数据的时间特性非常相似。对我们的递归神经网络模型进行剖析揭示了作为长神经元时间尺度和 WM 维持的关键组成部分的抑制性-抑制性连接的强抑制性微电路。我们还发现，增强抑制性-抑制性连接可导致更稳定的时间动态和提高任务性能。最后，我们表明具有这种微电路的网络可以执行其他任务而不会破坏其预先存在的时间尺度结构，这表明强抑制性信号是灵活的 WM 网络的基础。
