title: Persistent storage capability impairs decision making in a biophysical network model.
abstract: Two long-standing questions in neuroscience concern the mechanisms underlying our abilities to make decisions and to store goal-relevant information in memory for seconds at a time. Recent experimental and theoretical advances suggest that NMDA receptors at intrinsic cortical synapses play an important role in both these functions. The long NMDA time constant is suggested to support persistent mnemonic activity by maintaining excitatory drive after the removal of a stimulus and to enable the slow integration of afferent information in the service of decisions. These findings have led to the hypothesis that the local circuit mechanisms underlying decisions must also furnish persistent storage of information. We use a local circuit cortical model of spiking neurons to test this hypothesis, controlling intrinsic drive by scaling NMDA conductance strength. Our simulations provide further evidence that persistent storage and decision making are supported by common mechanisms, but under biophysically realistic parameters, our model demonstrates that the processing requirements of persistent storage and decision making may be incompatible at the local circuit level. Parameters supporting persistent storage lead to strong dynamics that are at odds with slow integration, whereas weaker dynamics furnish the speed-accuracy trade-off common to psychometric data and decision theory.
authors.affiliation: Canadian Institutes of Health Research Group in Sensory-Motor Integration, Queen's University, 18 Stuart Street, Kingston, Ontario, Canada. standage@biomed.queensu.ca
authors.forename: Dominic
authors.initials: D
authors.lastname: Standage
authors.forename: Martin
authors.initials: M
authors.lastname: Paré
mesh_terms: D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D008568:Memory / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer