Mufeng Tang

This paper investigates temporal predictive coding (tPC) as a framework for learning temporal dependencies in recurrent neural networks, showing its equivalence to BPTT variants and its ability to handle complex tasks with local plasticity.

This paper shows that predictive coding models can detect novelty at different levels of representation hierarchy, linking novelty detection, memory, and learning.

This paper introduces a temporal predictive coding model implemented in recurrent networks that can predict future stimuli and produce neuron-like responses without complex computations.

This study trains spiking neural networks to perform a state change detection task and finds that cross-stimulus-modulated inhibition emerges as an optimal solution under Dales law constraints.

This paper proposes predictive coding models that implicitly learn covariance information for associative memory, unifying predictive coding and covariance learning in hippocampal networks.