Neural computations are currently investigated using two separate approaches: sorting neurons into functional subpopulations or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and subpopulation structure play fundamentally complementary roles. Although various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input–output mappings instead require a non-random population structure that can be described in terms of multiple subpopulations. Our analyses revealed that such a subpopulation structure enables flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, for inactivation experiments and for the implication of different neurons in multi-tasking.

目前使用两种不同的方法研究神经计算：将神经元分类为功能亚群或检查集体活动的低维动态。目前尚不清楚这两个方面是否以及如何相互作用以形成计算。使用一种新方法从受过神经科学任务训练的网络中提取计算机制，在这里我们展示了动力学和亚群结构的维度在根本上起到了互补的作用。尽管可以通过在具有完全随机种群结构的网络中增加维数来实现各种任务，但灵活的输入-输出映射需要一个可以用多个子种群来描述的非随机种群结构。我们的分析表明，这种亚群结构可以通过基于增益控制调制的机制实现灵活的计算，从而灵活地塑造集体动态。我们的结果导致对神经选择性结构、失活实验以及不同神经元在多任务处理中的影响的特定任务预测。