MODELLING OF AUDITORY NOVELTY DETECTION IN THE MISMATCH NEGATIVITY PROTOCOLS AND ITS DEFICIT IN SCHIZOPHRENIA

The human auditory system efficiently differentiates expected from unexpected sounds, a capability indexed by mismatch negativity (MMN), an EEG marker of auditory novelty processing. MMN responses are consistently reduced in psychiatric disorders, particularly schizophrenia, but the underlying neuronal mechanisms remain poorly understood. In this study, we combined computational modelling with genetic insights to examine how SCZ-associated cellular alterations influence auditory novelty detection. We constructed an integrate-and-fire spiking network model that detects multiple forms of auditory deviance, including stimulus omission. The model incorporates short-term synaptic depression between network subpopulations and rhythmic, phase-locked neuronal inputs shaped by recent stimulus history, enabling robust MMN-like responses. We further show that such phase-locking can emerge through entrainment in a synfire chain architecture with spike-timing–dependent plasticity in recurrent feedback connections. Using this framework, we systematically tested cellular abnormalities linked to SCZ. Simulations revealed that both reduced pyramidal neuron excitability, associated with ion-channel dysfunction, and decreased dendritic spine density disrupt novelty detection, with spine loss producing more pronounced deficits. Overall, this work introduces a versatile spiking network model that connects cellular-level pathology to MMN impairments, offering mechanistic insight into MMN deficits and the heterogeneity of schizophrenia.