  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.
