A Population Network Model of Neuronal and Neurotransmitter Interactions Regulating Sleep–Wake Behavior in Rodent Species

Abstract

Mammalian sleep and wake states are controlled by transitions in the activation levels of brainstem and hypothalamic neuronal nuclei that are proposed to form a sleep–wake regulatory network. Circadian variation in mammalian sleep–wake patterning is presumed to occur through modulation of the neuronal populations composing the sleep–wake regulatory network by the circadian pacemaker in the suprachiasmatic nucleus. Many of the key neuronal populations involved in sleep–wake and circadian regulation are conserved across mammalian species, suggesting that perturbations of a single network structure can produce the reported variability in sleep–wake behavior across species and across the 24 h day. We recently introduced a dynamic, mathematical model of the mammalian sleep–wake regulatory network using a novel modeling formalism that describes both the activity levels of each neuronal population and the release of their associated neurotransmitters in postsynaptic targets (Diniz Behn and Booth in J. Neurophysiol. 103:1937–1953, 2010). Using a specific network architecture, this model network captured dynamical patterns of state-dependent neuronal activity and state-dependent concentrations of key neurotransmitters to produce patterns of wake, NREM sleep, and REM sleep consistent with rat sleep in the light period. In this chapter, we extended our sleep–wake regulatory network model to include physiologically identified inputs from the SCN in order to investigate mechanisms for the circadian variation of rat sleep–wake behavior. In addition, we identified model parameters that could be modified to produce mouse sleep–wake behavior and its circadian modulation. By keeping the network structure, including the sites of action of circadian effects, fixed between species, we identified both the flexibility and the limitations of the prescribed network structure to account for differences in sleep–wake patterning across the 24 h day and across species. Our analysis of model results illustrated how specific components of network architecture dictate dynamic interactions influencing maintenance of sleep–wake states and transitions between states, and it provided insights into mechanisms through which the network can generate the range of sleep–wake patterning observed with circadian variation and across mammalian species.