This dissertation explores the neural coding and mechanisms associated with consciousness by analyzing electrical rhythms of the brain under altered states of consciousness, namely epilepsy and anesthesia. First, transformation of neural coding under epileptogenic conditions is examined by computing the Volterra kernels in a rodent epilepsy model, where the epileptogenic condition is induced by altering the concentrations of Mg2+ and K+ of the perfusate for different levels of excitability. Principal dynamic modes (PDMs) are further deduced from the Volterra kernels to compare the changes in neural dynamics under epileptogenic conditions. The integrating PDMs are shown to dominate at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs shift to higher ranges under epileptogenic conditions, from ripple activities (75 - 200 Hz) to fast ripple activities (200 - 500 Hz). Second, markers of anesthetic states are explored by analyzing amplitude and phase of brain rhythms as well as their interaction and modulation, utilizing electroencephalogram (EEG) recorded from patients undergoing anesthesia. Anesthesia shifts the power to low frequency rhythms, especially alpha rhythms. Additionally anesthesia increases the coupling between alpha rhythms and gamma rhythms while disrupting the coupling between alpha rhythms and ripples (70 - 200 Hz). The results also indicate that the dose responses (i.e. depth of anesthesia) are not necessarily monophasic or linear. The commonality and differences of the changes in brain rhythms associated with these conditions are discussed to elucidate on the possible underlying mechanisms involved in producing consciousness.

This dissertation explores the neural coding and mechanisms associated with consciousness by analyzing electrical rhythms of the brain under altered states of consciousness, namely epilepsy and anesthesia. First, transformation of neural coding under epileptogenic conditions is examined by computing the Volterra kernels in a rodent epilepsy model, where the epileptogenic condition is induced by altering the concentrations of Mg2+ and K+ of the perfusate for different levels of excitability. Principal dynamic modes (PDMs) are further deduced from the Volterra kernels to compare the changes in neural dynamics under epileptogenic conditions. The integrating PDMs are shown to dominate at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs shift to higher ranges under epileptogenic conditions, from ripple activities (75 - 200 Hz) to fast ripple activities (200 - 500 Hz). Second, markers of anesthetic states are explored by analyzing amplitude and phase of brain rhythms as well as their interaction and modulation, utilizing electroencephalogram (EEG) recorded from patients undergoing anesthesia. Anesthesia shifts the power to low frequency rhythms, especially alpha rhythms. Additionally anesthesia increases the coupling between alpha rhythms and gamma rhythms while disrupting the coupling between alpha rhythms and ripples (70 - 200 Hz). The results also indicate that the dose responses (i.e. depth of anesthesia) are not necessarily monophasic or linear. The commonality and differences of the changes in brain rhythms associated with these conditions are discussed to elucidate on the possible underlying mechanisms involved in producing consciousness.