Biologically detailed models of brain circuitry are challenging to build and simulate due to the large number of neurons, their complex interactions, and the many unknown physiological parameters. Simplified mathematical models are more tractable, but harder to evaluate when too far removed from neuroanatomy/physiology. We propose that a multiscale model, coarse-grained (CG) while preserving local biological details, offers the best balance between biological realism and computability. This paper presents such a model. Generally, CG models focus on the interaction between groups of neurons—here termed “pixels”—rather than individual cells. In our case, dynamics are alternately updated at intra- and interpixel scales, with one informing the other, until convergence to equilibrium is achieved on both scales. An innovation is how we exploit the underlying biology: Taking advantage of the similarity in local anatomical structures across large regions of the cortex, we model intrapixel dynamics as a single dynamical system driven by “external” inputs. These inputs vary with events external to the pixel, but their ranges can be estimated a priori . Precomputing and tabulating all potential local responses speed up the updating procedure significantly compared to direct multiscale simulation. We illustrate our methodology using a model of the primate visual cortex. Except for local neuron-to-neuron variability (necessarily lost in any CG approximation) our model reproduces various features of large-scale network models at a tiny fraction of the computational cost. These include neuronal responses as a consequence of their orientation selectivity, a primary function of visual neurons.

中文翻译：

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通过局部动态平衡和粗粒度相互作用建立有效的皮质活动模型


由于神经元数量众多、其复杂的相互作用以及许多未知的生理参数，构建和模拟大脑电路的生物学详细模型具有挑战性。简化的数学模型更容易处理，但如果远离神经解剖学/生理学，则更难以评估。我们提出多尺度模型，粗粒度（CG），同时保留局部生物细节，提供生物现实性和可计算性之间的最佳平衡。本文提出了这样一个模型。一般来说，CG 模型关注神经元组（此处称为“像素”）之间的相互作用，而不是单个细胞之间的相互作用。在我们的例子中，动态在像素内和像素间尺度上交替更新，一个通知另一个，直到在两个尺度上实现收敛到平衡。一项创新在于我们如何利用潜在的生物学：利用皮层大区域局部解剖结构的相似性，我们将像素内动力学建模为由“外部”输入驱动的单个动力系统。这些输入随着像素外部的事件而变化，但它们的范围是可以估计的先验。与直接多尺度模拟相比，预先计算并列出所有潜在的局部响应可显着加快更新过程。我们使用灵长类视觉皮层模型来说明我们的方法。除了局部神经元到神经元的变异性（在任何 CG 近似中必然会丢失）之外，我们的模型以极小的计算成本再现了大规模网络模型的各种特征。这些包括神经元由于其方向选择性而产生的反应，这是视觉神经元的主要功能。