Gradient sensing in Bayesian chemotaxis

Bayesian chemotaxis is an information-based target search problem inspired by biological chemotaxis. It defined a decision strategy coupled to the dynamic estimation of position from detections signaling molecules. We extend case point-like agent previously introduced in [Vergassola et al., Nature 2007], which establishes concentration sensing as dominant contribution information processing, circular small finite size. identify gradient and Laplacian correction two leading-order expansion terms expected entropy variation. Numerically, we find that impact most relevant because it provides direct directional break symmetry likelihood distributions, are generally circle-shaped sensing.