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arXiv:2009.10615q-bio.NC

Computational capacity of pyramidal neurons in the cerebral cortex

Danko D. Georgiev, Stefan K. Kolev, Eliahu Cohen, James F. Glazebrook

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Abstract

The electric activities of cortical pyramidal neurons are supported by structurally stable, morphologically complex axo-dendritic trees. Anatomical differences between axons and dendrites in regard to their length or caliber reflect the underlying functional specializations, for input or output of neural information, respectively. For a proper assessment of the computational capacity of pyramidal neurons, we have analyzed an extensive dataset of three-dimensional digital reconstructions from the NeuroMorpho.Org database, and quantified basic dendritic or axonal morphometric measures in different regions and layers of the mouse, rat or human cerebral cortex. Physical estimates of the total number and type of ions involved in neuronal electric spiking based on the obtained morphometric data, combined with energetics of neurotransmitter release and signaling fueled by glucose consumed by the active brain, support highly efficient cerebral computation performed at the thermodynamically allowed Landauer limit for implementation of irreversible logical operations. Individual proton tunneling events in voltage-sensing S4 protein $α$-helices of Na$^{+}$, K$^{+}$ or Ca$^{2+}$ ion channels are ideally suited to serve as single Landauer elementary logical operations that are then amplified by selective ionic currents traversing the open channel pores. This miniaturization of computational gating allows the execution of over 1.2 zetta logical operations per second in the human cerebral cortex without combusting the brain by the released heat.

arXiv comments
18 pages, 4 figures
Journal reference
Brain Research 2020; 1748: 147069
DOI
doi:10.1016/j.brainres.2020.147069
Subjects
q-bio.NCNeurons and Cognition

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    • We have no idea how to estimate the computational capacity of the brain at the moment. We can make silly estimates like saying that 1 human neuron is equivalent to something in an artificial network. But this is definitely wrong, biological neurons are far more complex than…

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