FULLERENIC-TOPOLOGICAL TOOLS FOR HONEYCOMB NANOMECHANICS. Towards a fullerenic approach to brain functions

Tozzi A, Peters JF, Ori O.  2017.  Fullerenic-topological tools for honeycomb nanomechanics.  Towards a fullerenic approach to brain functions.  Fullerenes, Nanotubes and Carbon nanostructures.  https://dx.doi.org/10.1080/1536383X.2017.1283618




Arturo Tozzi

Center for Nonlinear Science, University of North Texas

1155 Union Circle, #311427

Denton, TX 76203-5017, USA



James F. Peters

Department of Electrical and Computer Engineering, University of Manitoba

75A Chancellor’s Circle, Winnipeg, MB R3T 5V6



Ottorino Ori (corresponding Author)

Actinium Chemical Research, Via Casilina 1626/A, 00133 Rome, Italy





Fullerenic structures equipped with Stone-Wales transformations have been successfully utilized in the study of macromolecular assemblies. Here we show that this approach could be useful in the assessment of issues from a far-flung research area, i.e., neuroscience. Indeed, the basic morphological and functional unit of the brain, called the human microcolumn, is a tubular structure that can be flattened in the guise of a fullerene-like two-dimensional lattice. We describe this procedure in order to build a fullerene-like microcolumn, in which neuronal firing and electric signal propagation are assessed in terms of topological neural network modifications, instead of the canonical logic circuits. Every node stands for a neuron, where neural computations take place. This means that nervous activity, other than logic circuits, could instead depend on topological transformations and symmetry constraints dictated by Stone-Wales transformations occurring in the upper cortical layers. A two-dimensional fullerene-like lattice not only just simulates the real microcolumn’s microcircuitry, but also makes it possible us to build artificial networks equipped with robustness, plasticity and fastness. In this note, electric signal propagation is investigated in terms of pure topological modifications of the neural honeycomb network.