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lus to the contralateral hand. 130 This regional use-depen-
dent effect was subsequently confirmed and extended. 131
Analogous findings were obtained in the rat and human
where unilateral sensory or optic stimuli, respectively,
during waking caused an interhemispheric shift in low-
frequency power in the NREM sleep EEG. 132 , 133 Con-
versely, in a human study the selective understimulation of
the cortical arm projection area during waking achieved by
unilateral arm immobilization induced a reduction of
power over the corresponding cortical region during sub-
sequent sleep. 134
The notion of sleep homeostasis, originally derived from
the sleep-wake-dependent changes of the EEG slow
waves, was recently expanded to the synaptic level. Tononi
and Cirelli 135 , 136 proposed a synaptic homeostasis hypoth-
esis postulating that synaptic strength is maintained over
time by alternating phases of predominant potentiation
during waking with phases of predominant depression
during sleep. NREM sleep and the typical slow waves
would subserve synaptic downscaling and thereby safe-
guard energy, space, and cellular supplies. The synaptic
homeostasis hypothesis has the merit of relating the
changes at the level of the EEG to well-known mecha-
nisms at the synaptic level and thereby allowing specific
predictions that can be simulated 37 and also tested by elec-
trophysiologic and neurochemical techniques in both
humans and animals. 137-140 Krueger and collaborators 141
also underline the local aspect of sleep. Starting from an
early theoretical paper, 129 they view sleep as an emergent
property of cortical columns and propose a nonlinear
mathematical model to account for the interactions
between columns. 118 The term “model” was applied by
Saper and colleagues 109 to the flip-flop switch, a mode of
interaction between different neurotransmitter systems
that accounts for the sharp state transitions between waking
and sleep. However, the processes were not characterized
in mathematical terms.
In conclusion, models have proved useful for delineating
regulating processes underlying such a complex and little-
understood phenomenon as sleep, and they thereby offer
a conceptual framework for analyzing existing and new
data. The major models have already inspired a consider-
able number of experiments. Ideally, the specification of
model parameters should be based on physiologic data
obtained for the entire sleep episode. Once the parameter
estimation is satisfactory for a specific empirical data set,
predictions can be made for different experimental proto-
cols. A new empirical data set can then be used to validate
the model and, if necessary, to adjust the model parame-
ters. Such an iterative approach has been taken in the
attempt to validate an extended version of the two-process
model 79 and also in the continuous refinement of the cir-
cadian pacemaker model accounting for the changes
induced by light. 91 , 142 Future challenges of the modeling
approach to sleep include the simulation of changes at the
level of individuals as well as accounting for the regional
differences of sleep-related variables.
Clinical Pearl
The common experience that a good night's sleep
dissipates fatigue and tiredness and regenerates
energy points to a specific restorative function of
sleep that cannot be achieved by merely resting.
Sleep homeostasis denotes a basic principle of sleep
regulation that can lead to a better understanding of
sleep pathologies. Deficient sleep homeostasis might
account for the altered sleep architecture in depres-
sion, and the transient normalization of sleep pro-
pensity can explain the antidepressant effect of sleep
deprivation. The elucidation of sleep homeostasis at
the cellular and molecular levels is likely to open new
avenues for the pharmacologic therapy of sleep
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This work was supported by the Swiss National Science
Foundation and the Zurich Center for Integrative Human
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