We present a multi-scale differentiable brain modeling workflow utilizing BrainPy, a unique differentiable brain simulator that combines accurate brain simulation with powerful gradient-based optimization. We leverage this capability of BrainPy across different brain scales. At the single-neuron level, we implement differentiable neuron models and employ gradient methods to optimize their fit to electrophysiological data. On the network level, we incorporate connectomic data to construct biologically constrained network models. Finally, to replicate animal behavior, we train these models on cognitive tasks using gradient-based learning rules. Experiments demonstrate that our approach achieves superior performance and speed in fitting generalized leaky integrate-and-fire and Hodgkin-Huxley single neuron models. Additionally, training a biologically-informed network of excitatory and inhibitory spiking neurons on working memory tasks successfully replicates observed neural activity and synaptic weight distributions. Overall, our differentiable multi-scale simulation approach offers a promising tool to bridge neuroscience data across electrophysiological, anatomical, and behavioral scales.

翻译：我们提出了一种利用BrainPy的多尺度可微分脑建模工作流程，BrainPy是一种独特的可微分脑模拟器，它将精确的脑模拟与强大的基于梯度的优化相结合。我们利用BrainPy在不同脑尺度上的这一能力。在单神经元层面，我们实现了可微分神经元模型，并采用梯度方法优化其与电生理数据的拟合。在网络层面，我们整合连接组数据以构建生物学约束的网络模型。最后，为了复现动物行为，我们使用基于梯度的学习规则在认知任务上训练这些模型。实验表明，我们的方法在拟合广义泄漏积分发放与Hodgkin-Huxley单神经元模型时实现了更优的性能和速度。此外，在工作记忆任务上训练一个具有生物学背景的兴奋性与抑制性脉冲神经元网络，成功复现了观测到的神经活动与突触权重分布。总体而言，我们的可微分多尺度模拟方法为连接电生理、解剖学和行为尺度的神经科学数据提供了一个有前景的工具。