Inter-Regional Communication Benchmark '26: Simulated Datasets and Evaluation Metrics for Multi-Regional Models of Neural Population Activity

Multi-region neural recordings have motivated a growing family of data-driven models that aim to infer the pathways and content of inter-regional communication. Evaluating these models is challenging because communication is typically unobserved, and accurate reconstruction of observed neural activity does not imply accurate recovery of unobserved communication. Model validation has therefore largely relied on synthetic datasets with ground-truth communication. However, due to the lack of standardized datasets and evaluation metrics, each existing model has been validated in a unique testbed, often designed alongside the model itself. This fragmented approach precludes systematic comparison across models and risks obscuring models' capabilities and limitations. To address this gap, we introduce a benchmark comprising 42 synthetic multi-region datasets and a suite of evaluation metrics for rigorous assessment of communication models. We demonstrate the benchmark on two communication models, revealing model limitations and conditions that impact model performance. Altogether, we believe this resource will stimulate and guide model development, and support scientific interpretation of large-scale multi-region neural recordings.