The human brain from above: an increase in complexity from environmental stimuli to abstractions

Abstract

Contrary to common belief, the brain appears to increase the complexity from the perceived object to the idea of it. Topological models predict indeed that: (a) increases in anatomical/functional dimensions and symmetries occur in the transition from the environment to the higher activities of the brain, and (b) informational entropy in the primary sensory areas is lower than in the higher associative ones. To demonstrate this novel hypothesis, we introduce a straightforward approach to measuring island information levels in fMRI neuroimages, via Rényi entropy derived from tessellated fMRI images. This approach facilitates objective detection of entropy and corresponding information levels in zones of fMRI images generally not taken into account. We found that the Rényi entropy is higher in associative cortices than in the visual primary ones. This suggests that the brain lies in dimensions higher than the environment and that it does not concentrate, but rather dilutes messages coming from external inputs.

Simulation of retinal ganglion cell response using fast independent component analysis

“…Consecutively, single neuronal activities are primarily maintained at a low level during stimulation (Olshausen and Field 2004;Zheng et al 2016;Lewick 2002). Sparse coding is essential for the processing of visual information; it reduces the number of neurons involved, saves energy consumption, improves the efficacy of information transmission and enhances the ability of information processing (Hubel and Wiesel 1997;Peters et al 2017). In recent years, visual information processing and coding have been comprehensively investigated and the results (Lewick 2002;Hubel and Wiesel 1997;Peters et al 2017;Vinje and Gallant 2002) allow simulation of the visual system in silico.…”

“…Sparse coding is essential for the processing of visual information; it reduces the number of neurons involved, saves energy consumption, improves the efficacy of information transmission and enhances the ability of information processing (Hubel and Wiesel 1997;Peters et al 2017). In recent years, visual information processing and coding have been comprehensively investigated and the results (Lewick 2002;Hubel and Wiesel 1997;Peters et al 2017;Vinje and Gallant 2002) allow simulation of the visual system in silico. Combining the data and results obtained by neurophysiologists using signal processing and computing theory facilitates the simulation of visual system by computers in order to resolve the challenges encountered in image processing (Huberman et al 2008;Pillow et al 2008;Tozzi and Peters 2017).…”

The Energy Coding of a Structural Neural Network Based on the Hodgkin–Huxley Model

“…(2) The neural energy can be combined with spiking pattern of membrane potentials to resolve the neural information (Wang et al, 2006 , 2008 , 2016 , 2017 ; Wang and Wang, 2014 ; Wang R. et al, 2014 ; Wang Z. et al, 2014 ; Kozma, 2016 ; Yan et al, 2016 ; Zheng et al, 2016 ). (3) Neural energy can describe the interaction of large-scale neurons referring to the interaction of multiple brain regions that cannot be achieved by any conventional coding theory (Wang et al, 2009 ; Vuksanović and Hövel, 2016 ; Zhang et al, 2016 ; Déli et al, 2017 ; Peters et al, 2017 ). (4) Currently, a simultaneous recording from multiple brain regions in traumatic brain injury experiments is challenging.…”

The essence of neuronal activity from the consistency of two different neuron models

“…But when the brain is activated, it is difficult to obtain an accurate understanding of the neural activities in the brain in various states because of the averaging distribution of blood flow in the brain as well as nonlinear coupling relationship between blood flow and oxygen consumption. At the same time, we cannot understand the interaction among neurons in the brain area with such a method [1,2].…”