Chen Zeng

Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains

It is demonstrated that incorporating biologically realistic diversity during training is critical for developing reliable machine-learning tools for large-scale synaptic inference from neural recordings, and training on pooled, biologically grounded simulation data substantially improves robustness across parameter perturbations, outperforming models trained under narrow conditions.