Xingyun Wang

The paper develops a method to analyze brain-wide electrical fluctuations and finds modular organization in the mouse brain that changes with context.

This paper introduces and evaluates Burstprop, a burst-dependent algorithm for spiking neural networks, showing its effectiveness in image classification and robustness to feedback connectivity and weight discretization.

This study uses deep learning to infer synaptic plasticity rules from single-neuron spiketrains, finding that transformers outperform other models but caution against applying simulation-trained classifiers to real data.

The paper identifies two distinct neural codes, burst fraction and event rate, that convey stimulus information and error signals during learning, with burst fraction showing stronger modulation and temporal alignment with behavioral accuracy.