Topic: eigenvectors

Vasileios Kalantzis develops iterative linear algebra algorithms that tolerate incomplete matrix-vector products in controller-worker cloud systems. Richardson and Chebyshev schemes solve linear systems using randomly available product entries, replacing missing entries with zero. For dominant eigenvectors, modified power iterations substitute zeros, previous entries, or averages of partial iterates for delayed components. The talk presents convergence results in expectation and numerical experiments on sparse matrices for both problem classes.

Environmental boundaries anchor cognitive maps that support memory. However, trapezoidal boundary geometry distorts the regular firing patterns of entorhinal grid cells proposedly providing a metric for cognitive maps. Here, we test the impact of trapezoidal boundary geometry on human spatial memory using immersive virtual reality. Consistent with reduced regularity of grid patterns in rodents and a grid-cell model based on the eigenvectors of the successor representation, human positional memory was degraded in a trapezoid compared to a square environment; an effect particularly pronounced in

Understanding how connectivity structure shapes network dynamics is paramount in the field of neuroscience. Theoretical investigations of multi-population neuronal networks often consider statistically homogeneous populations and incorporate either only the population-averaged mean or i.i.d. fluctuations in synaptic couplings. A newly released synaptic physiology dataset highlighted the strong presence of motifs – specific connectivity patterns between pairs and triplets of neurons–beyond the scope of mean connectivity[1] However, it is a priori not clear which of the experimentally identifie