Learning local geometry and nonlinear topology of neural manifolds via spike-timing dependent plasticity

Methods from topological data analysis developed to quantitatively measure propagation of such nonlinear manifolds across populations provide quantitative support for the hypothesis that simple biologically plausible plasticity mechanisms suffice to induce changes in the structure of neural architectures sufficient to explain the appearance of such features in real neural systems.