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Drover, D. R., Lemmens, H. J., Pierce, E. T., Plourde, G., Ornstein, E., Prichep, L. S., Chabot, R. J., & Gugino, L. (2002). Patient State Index (PSI): Titration of Delivery and Recovery from Propofol, Alfentanil, and Nitrous Oxide Anesthesia. Anesthesiology, 97, 82-89.
https://doi.org/10.1097/00000542-200207000-00012

has been cited by the following article:

• TITLE: The Experience-Dependent Dynamics of Human Consciousness

  AUTHORS: Birgitta Dresp-Langley

  KEYWORDS: Consciousness, Human, Time, Temporal Neural Signatures, Experience-Dependent Brain Learning

  JOURNAL NAME: Open Journal of Philosophy, Vol.8 No.2, April 9, 2018

  ABSTRACT: By reviewing most of the neurobiology of consciousness, this article highlights some major reasons why a successful emulation of the dynamics of human consciousness by artificial intelligence is unlikely. The analysis provided leads to conclude that human consciousness is epigenetically determined, experience, and context-dependent at the individual level. It is subject to changes in time that are essentially unpredictable. If cracking the code to human consciousness were possible, the result would most likely have to consist of a temporal pattern code simulating long-distance signal reverberation and de-correlation of all spatial signal contents from temporal signals. In the light of the massive evidence for complex interactions between implicit (non-conscious) and explicit (conscious) contents of representation, the code would have to be capable of making implicit (non-conscious) processes explicit. It would have to be capable of a progressively less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure identical to that of the human brain, from the synaptic level to that of higher cognitive functions. The code’s activation thresholds would depend on specific temporal signal coincidence probabilities, vary considerably with time and across individual experience data, and would therefore require dynamically adaptive computations capable of emulating the properties of individual human experience. No known machine or neural network learning approach has such potential.