Recurrent neural network models reveal unified mechanisms generating event-related potentials from MMN to P300

Overview & Synopsis

The brain's ability to detect behaviorally relevant stimuli from sensory inputs is fundamental to cognition, yet the neural mechanisms linking synaptic processes to event-related potential (ERP) signatures remain unclear. Here, we develop recurrent neural network (RNN) models of ERP responses demonstrating that short-term synaptic depression -a ubiquitous plasticity mechanism -provides a unified computational framework for mismatch negativity (MMN) and P300 responses across passive and active oddball paradigms. Our models reveal that neural populations spontaneously organize stimulus representations into probability-dependent geometric manifolds, where rare events occupy expanded state space. Hierarchical connectivity creates 9-fold signal amplification with enhanced noise robustness, explaining P300's functional advantages over sensory responses. Critically, the same synaptic mechanism accounts for attentional modulation of behaviorally relevant stimuli, providing the first unified explanation bridging automatic and controlled attention. This framework offers quantitative predictions for how synaptic and connectivity disruptions manifest as altered MMN and P300 characteristics in neuropsychiatric disorders including schizophrenia and autism.