Carlos Calvo | Searcharxiv

arXiv2025

High viscosity effects on solitary wave propagation

We present an experimental and numerical study of linear and non-linear viscous effects in transient non-linear long wave propagation in Newtonian and shear thinning fluids in the laminar flow regime. Using optical measuring techniques (Fourier Transform Profilometry) and numerical simulations (open-source CFD library OpenFOAM), we show that the wave phase speed decreases in both glycerin and carboxymethylcellulose (CMC) solutions with respect to that in water. A decrease in wave phase speed is observed, and a dispersion relation is obtained for surface waves through dimensional analysis from five dimensionless groups: the dimensionless wave celerity, the shallowness parameter, dimensionless amplitude, Reynolds number and the flow index. To complete the picture on wave propagation, an empirical dependence between the wave attenuation and the last four dimensionless groups mentioned above is found for non-linear long surface waves. We conclude quantitatively about all the viscosity effects in non-linear long wave propagation.

physics.flu-dyn↗

arXiv2017

High-dimensional brain. A tool for encoding and rapid learning of memories by single neurons

Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as, e.g., the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on Stochastic Separation Theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: i) the extreme selectivity of single neurons to the information content, ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories.

q-bio.NC↗