| Journal of Advanced Neuroscience Research (Special Issue - May 2017) | |
| Is Consciousness Dissectible? Acute Slice Electrophysiology and a Bayesian Interpretation of Neural Correlates of Consciousness |  |
| Pages 37-47 Richard König, Alexander Mirnig, Ludwig Aigner, and Thomas M. Weiger DOI: http://dx.doi.org/10.15379/2409-3564.2017.05Published: 12 May 2017 |
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| Abstract | |
| The acute brain slicing method has become one of the foundations of modern neuroscience research. It is a laboratory technique in electrophysiology, which allows the study of electrical properties directly on a freshly prepared slice of animal brain tissue. During recording and/or stimulation, the acutely isolated brain slice is artificially kept “alive” up to many hours after the animals’ death. During an acute brain slice preparation, cortical and subcortical areas, which are suggested to correlate with conscious experience in humans, such as the claustrum and the thalamus, are dissected. In this paper, we investigate whether scientific statements can be made regarding the likelihood that some neural activities on the brain slice still support consciousness or degrees thereof.
We exemplarily demonstrate how acute slices are produced and provide own electrophysiological data combined with a short literature review. Subsequently, we introduce the concept of Neural Correlates of Consciousness (NCC) and apply conditional probabilities inferred from Bayes´ theorem, in order to draw from it an informed hypothesis on the likelihood that specific neural activities that sustain on the slice still correlate with some form of conscious experience. We propose that the probability that there is something that is it like to be, even on the acutely isolated brain slice, is similar to the likelihood that certain mental states correlate with certain brain activities in a healthy human subject, depending on the robustness of the underlying NCC. |
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| Keywords | |
| Neural correlate of consciousness (NCC), Acute slice electrophysiology, Philosophy, Empirical bayes methods, Conditional probability. | |
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