Cracking the barcode of fullerene-like cortical microcolumns

NEUROSCIENCE LETTERS

卷:644

Article

Tozzi, Arturo^1,5  Peters, James F.^2,3,4,5  Ori, Ottorino⁶ 

[1] Univ North Texas, Ctr Nonlinear Sci, 1155 Union Circle 311427, Denton, TX 76203 USA

[2] Univ Manitoba, Dept Elect & Comp Engn, 75A Chancellors Circle, Winnipeg, MB R3T 5V6, Canada

[3] Adiyaman Univ, Dept Math, Adiyaman 02040, Turkey

[4] Adiyaman Univ, Fac Arts & Sci, Dept Math, Adiyaman 02040, Turkey

[5] Univ Manitoba, Computat Intelligence Lab, Winnipeg, MB R3T 5V6, Canada

[6] Actinium Chem Res, Via Casilina 1626-A, I-00133 Rome, Italy

关键词: Brain circuitry;    Cortical neuron;    Neural code;    Lattice;    Artificial neural system;   

DOI  :  10.1016/j.neulet.2017.02.064

来源: Elsevier

【 摘 要 】

Artificial neural systems and nervous graph theoretical analysis rely upon the stance that the neural code is embodied in logic circuits, e.g., spatio-temporal sequences of ON/OFF spiking neurons. Nevertheless, this assumption does not fully explain complex brain functions. Here we show how nervous activity, other than logic circuits, could instead depend on topological transformations and symmetry constraints occurring at the micro-level of the cortical microcolumn, i.e., the embryological, anatomical and functional basic unit of the brain. Tubular microcolumns can be flattened in fullerene-like two-dimensional lattices, equipped with about 80 nodes standing for pyramidal neurons where neural computations take place. We show how the countless possible combinations of activated neurons embedded in the lattice resemble a barcode. Despite the fact that further experimental verification is required in order to validate our claim, different assemblies of firing neurons might have the appearance of diverse codes, each one responsible for a single mental activity. A two-dimensional fullerene-like lattice, grounded on simple topological changes standing for pyramidal neurons' activation, not just displays analogies with the real microcolumn's microcircuitry and the neural connectome, but also the potential for the manufacture of plastic, robust and fast artificial networks in robotic forms of full-fledged neural systems. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free