1. A Differentiable Approach to Multi-scale Brain Modeling
- Author
-
Wang, Chaoming, Lyu, Muyang, Zhang, Tianqiu, He, Sichao, and Wu, Si
- Subjects
Computer Science - Neural and Evolutionary Computing ,Computer Science - Artificial Intelligence ,Computer Science - Computational Engineering, Finance, and Science ,Quantitative Biology - Neurons and Cognition - Abstract
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., Comment: 2nd Differentiable Almost Everything Workshop at ICML 2024. https://github.com/chaoming0625/differentiable-brain-modeling-workflow
- Published
- 2024