A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia

Abstract

Schizophrenia results in part from a failure of prefrontal networks but we lack full understanding of how disruptions at a synaptic level cause failures at the network level. This is a crucial gap in our understanding because it prevents us from discovering how genetic mutations and environmental risks that alter synaptic function cause prefrontal network to fail in schizophrenia. To address that question, we developed a recurrent spiking network model of prefrontal local circuits that can explain the link between NMDAR synaptic and spike timing deficits we recently observed in a pharmacological monkey model of prefrontal network failure in schizophrenia. We analyze how the balance between AMPA and NMDA components of recurrent excitation and GABA inhibition in the network influence spike timing to inform the biological data. We show that reducing recurrent NMDAR synaptic currents prevents the network from shifting from a steady to oscillatory state in response to extrinsic inputs such as might occur during behavior. This explains how NMDAR synaptic deficits, implicated by genetic evidence as causal in schizophrenia, could prevent the emergence of 0-lag synchronous spiking in prefrontal local circuits during behavior, potentially disconnecting those circuits via spike-timing dependent mechanisms in the human disease.