We present a model of the whole visual train to estimate an individual's visual acuity based on their eye's physical properties. Our simulation takes into account the optics of the eye, neural transmission and noise, as well as the recognition process. Personalized input data are represented by the ocular wavefront aberration and pupil diameter, both either coming from in vivo measurements of a subject or being produced by optical design software using a schematic eye. This flexibility opens the door to a broad range of potential applications, such as objective visual acuity measurements and intraocular lens design. Our algorithm contains only two adjustable neural parameters: additive noise σ, and discrimination range δρ, with their values being experimentally calibrated by fitting the results of simulations to the outcome of real acuity tests performed on healthy young subjects with normal vision (visual acuity: 0…-0.3 logMAR range). It was established that by using fixed values of σ = 0.10 and δρ = 0.0025 for each person examined, the residual of the acuity simulations averaged over the calibration group reached its minimum at 0.045 logMAR.