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Seizure activity leads to increases in extracellular potassium concentration ([K${}^{+}$]o), which can result in changes in neuronal passive and active membrane properties as well as in population activities. In this study, we examined how extracellular potassium modulates seizure activities using an acute 4-AP induced seizure model in the neocortex, both in vivo and in vitro. Moderately elevated [K${}^{+}$]o up to 9mM prolonged seizure durations and shortened interictal intervals as well as depolarized the neuronal resting membrane potential (RMP). However, when [K${}^{+}$]o reached higher than 9mM, seizure like events (SLEs) were blocked and neurons went into a depolarization-blocked state. Spreading depression was never observed as the blockade of ictal events could be reversed within 1–2min after the raised [K${}^{+}$]o was changed back to control levels. This concentration-dependent dual effect of [K${}^{+}$]o was observed using in vivo and in vitro mouse brain preparations as well as in human neocortical tissue resected during epilepsy surgery. Blocking the Ih current, mediated by hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, modulated the elevated [K${}^{+}$]o influence on SLEs by promoting the high [K${}^{+}$]o inhibitory actions. These results demonstrate biphasic actions of raised [K${}^{+}$]o on neuronal excitability and seizure activity.

To enable an accurate recognition of neuronal excitability in an epileptic brain for modeling or localization of epileptic zone, here the brain response to single-pulse electrical stimulation (SPES) has been decomposed into its constituent components using adaptive singular spectrum analysis (SSA). Given the response at neuronal level, these components are expected to be the inhibitory and excitatory components. The prime objective is to thoroughly investigate the nature of delayed responses (elicited between 100ms–1 s after SPES) for localization of the epileptic zone. SSA is a powerful subspace signal analysis method for separation of single channel signals into their constituent uncorrelated components. The consistency in the results for both early and delayed brain responses verifies the usability of the approach.

This article argues that conscious attention exists not so much for selecting an immediate action as for using the current task to focus specialized learning for the action-selection mechanism(s) and predictive models on tasks and environmental contingencies likely to affect the conscious agent. It is perfectly possible to build this sort of a system into machine intelligence, but it would not be strictly necessary unless the intelligence needs to learn and is resource-bounded with respect to the rate of learning versus the rate of relevant environmental change. Support for this theory is drawn from scientific research and AI simulations. Consequences are discussed with respect to self-consciousness and ethical obligations to and for AI.