In this talk, we use various tools of statistical physics to understand how some viruses (in particular, influenza A) actively navigate through a dense, extracellular environment. We will show that an asymmetric viral surface-protein distribution not only enhances directed, persistent motion, but enables a type of sensing of their local environment. This rebuts the view that viruses are...
During this lecture and workshop, we will introduce Fractal Space Curve Analysis (FSCA), a novel methodology for characterizing multidimensional data—particularly neuroimaging data—by examining their fractal properties. The core concept of FSCA is to transform multidimensional data (e.g., 2D, 3D, or 3+1D scans) into one-dimensional time series using space-filling curves (SFCs), primarily the...
The use of electronic circuits to model neural systems goes back to C. Mead and is present in models, from leaky-integrate-and-fire to Hodking-Huxley. Simulating neural networks with analog hardware is attractive: it allows to implement neurocomputations in real time without discretization approximations, it has perfect simulation-time scaling with system size, and it provides...
A two-states device such as the Brownian ratchet can be regarded as both a "heat engine" and an "information engine". From this dual perspective, long time series recorded in our centimeter-scale experimental setup [1] allow for a precise investigation of all the observables of interest. These are the heat flux supplied by the athermal hot bath at $kT_{\rm eff.}$, the work produced per time...
Celebrated fluctuation-dissipation theorem (FDT) linking the response function to time dependent correlations of observables measured in the reference unperturbed state is one of the central results in equilibrium statistical mechanics. In this letter we discuss an extension of the standard FDT to the case when multidimensional matrix representing transition probabilities is strictly...
