activity
20212024
most citedResearch Notes: Gradient sensing in Bayesian chemotaxis

9 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.stat-mech2024

Nonequilibrium relaxation inequality on short timescales

Andrea Auconi

An integral relation is derived from the Fokker-Planck equation which connects the steady-state probability currents with the dynamics of relaxation on short timescales in the limi…

physics.bio-ph2023

Conjecture of an information-response inequality

Andrea Auconi

The invariant response was defined from a formulation of the fluctuation-response theorem in the space of probability distributions. An inequality is here conjectured which sets th…

cond-mat.stat-mech2022

Learning stochastic filtering

Rahul O. Ramakrishnan, Andrea Auconi, Benjamin M. Friedrich

We quantify the performance of approximations to stochastic filtering by the Kullback-Leibler divergence to the optimal Bayesian filter. Using a two-state Markov process that drive…

physics.bio-ph2021★ 9 cited

Research Notes: Gradient sensing in Bayesian chemotaxis

Andrea Auconi, Maja Novak, Benjamin M. Friedrich

Bayesian chemotaxis is an information-based target search problem inspired by biological chemotaxis. It is defined by a decision strategy coupled to the dynamic estimation of targe…

cond-mat.soft2021

Composite search of active particles in three-dimensional space based on non-directional cues

Justus A. Kromer, Andrea Auconi, Benjamin M. Friedrich

We theoretically address minimal search strategies of active, self-propelled particles towards hidden targets in three-dimensional space. The particles can sense if a target is clo…

cs.IT2021

Fluctuation-response theorem for Kullback-Leibler divergences to quantify causation

Andrea Auconi, Benjamin M. Friedrich, Andrea Giansanti

We define a new measure of causation from a fluctuation-response theorem for Kullback-Leibler divergences, based on the information-theoretic cost of perturbations. This informatio…