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The STSM architecture uses a non-stationary Markov chain where the probability of a state transitioning to another state can differ at particular time-steps.
The proposed synaptic drive firing-rate model predicts the conscious unconscious transition as the applied anesthetic concentration increases, where excitatory neural activity is characterized by a Poincaré Andronov Hopf bifurcation with the awake state transitioning to a stable limit cycle and then subsequently to an asymptotically stable unconscious equilibrium state.
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the state transition matrix.
Decomposed state transition approach.
F State transition matrix.
State transition arrays.
Table 2 State transition table.
Stage 3: Markov state transition.
Figure 5 State transition diagram.
Figure 5 vCPU state transitions.
Fig. 4 State transition diagram.
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