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Ebubekir İnan

Publications and source records attributed to Ebubekir İnan.

2 recordsLinked to original sources

PRIMARY EVIDENCE OF A DONUT-LIKE, FOURTH SPATIAL DIMENSION IN THE BRAIN

We introduce a novel method for the measurement of information in fMRI neuroimages, i.e., nucleus clusterings Renyi entropy derived from strong proximities in feature-based Voronoi tessellations, e.g., maximal nucleus clustering (MNC). We show how MNC is a novel, fast and inexpensive image-analysis technique, independent from the standard blood-oxygen-level dependent signals, which facilitates the objective detection of hidden temporal patterns of entropy/information in zones of fMRI images generally not taken into account by the subjective standpoint of the observer. In order to evaluate the potential applications of MNC, we looked for the presence of a fourth dimensions distinctive hallmarks in a temporal sequence of 2D images taken during spontaneous brain activity. Indeed, recent findings suggest that several brain activities, such as mind-wandering and memory retrieval, might take place in the functional space of a four dimensional hypersphere, which is a double donut-like structure undetectable in the usual three dimensions. We found that the Renyi entropy is higher in MNC areas than in the surrounding ones, and that these temporal patterns closely resemble the trajectories predicted by the possible presence of a hypersphere in the brain.

Neuroscience

ENTROPY IN PRIMARY SENSORY AREAS LOWER THAN IN ASSOCIATIVE ONES: THE BRAIN LIES IN HIGHER DIMENSIONS THAN THE ENVIRONMENT

The brain, rather than integrate sensory inputs and concentrate them into concepts as currently believed, appears to increase the complexity from the perceived object to the idea of it. Topological models predict indeed an increase in dimensions and symmetries from the environment to the higher activities of the brain. Models predict that informational entropy in the primary sensory areas must be lower than in the higher associative ones. In order to demonstrate the novel hypothesis, we introduce a method for the measurement of information in fMRI neuroimages, i.e., nucleus clusterings Renyi entropy derived from strong proximities in feature-based Voronoi tessellations, e.g., maximal nucleus clustering. The technique facilitates the objective detection of entropy/information in zones of fMRI images generally not taken into account. We found that the Renyi entropy is higher in associative cortices than in the visual primary ones. It suggests that the brain lies in higher dimensions than the environment and that it does not concentrate, but rather dilutes the message coming from external inputs.

Neuroscience