Neural representations of time for the judgment of temporal durations are reflected in electroencephalographic (EEG) slow brain potentials, as established in time production and perception tasks. Here, we investigated whether anticipatory processes in reaction-time procedures are governed by similar mechanisms of interval timing. We used a choice reaction task with two different, temporally regular stimulus presentation regimes, both with occasional deviant interstimulus intervals. Temporal preparation was shown in the form of adjustments in time course of slow brain potentials, such that they reached their maximum amplitude just before a new trial, independent of the duration of the interstimulus interval. Preparation was focused on a brief time window, demonstrated by a drop in amplitude of slow potentials as the standard interval had elapsed in deviant interstimulus intervals. Implicit timing influencing perceptual processing was shown in reduced visual-evoked responses to delayed stimuli after a deviant interstimulus interval and in a reduction of EEG ␣ power over the visual cortex at the time when the standard interval had elapsed. In contrast to explicit timing tasks, the slow brain potential manifestations of implicit timing originated in the lateral instead of the medial premotor cortex. Together, the results show that temporal regularities set up a narrow time window of motor and sensory attention, demonstrating the operation of interval timing in reaction time performance. The divergence in slow brain potential distribution between implicit and explicit timing tasks suggests that interval timing for different behaviors relies on qualitatively similar mechanisms implemented in distinct cortical substrates.

Spontaneous activity in biological neural networks shows patterns of dynamic synchronization. We propose that these patterns support the formation of a small-world structure-network connectivity optimal for distributed information processing. We present numerical simulations with connected Hindmarsh-Rose neurons in which, starting from random connection distributions, small-world networks evolve as a result of applying an adaptive rewiring rule. The rule connects pairs of neurons that tend fire in synchrony, and disconnects ones that fail to synchronize. Repeated application of the rule leads to smallworld structures. This mechanism is robustly observed for bursting and irregular firing regimes.

The Sheffield Intelligent Ventilator Advisor is a hybrid knowledge-and-model-based advisory system designed for intensive care ventilator management. It consists of a top-level fuzzy rule-based module to give the qualitative component of the advice, and a lower-level model-based module to give the quantitative component of the advice. It is structured to offer adaptive patient-specific decision support. It can be operated in either invasive or noninvasive modes depending on the availability of data from invasive clinical measurements. The user can choose between the full-advisory mode and the clinician-directed mode. The advice given by the top-level module has been validated against retrospective real patient data and compared with intensivists expertise and performance under simulation conditions. Closed-loop simulations were performed assuming various clinical scenarios including sudden changes in the patient parameters such as the shunt or deadspace with noise and disturbances. They have shown that the advice given was appropriate and the blood gases resulting from the closed-loop decision support were acceptable. The system was also shown to be tolerant to noise and disturbances. It is implemented in MATLAB/SIMULINK and LabVIEW.