Abstract Visual world is naturally correlated both spatially and temporally. The correlations are used in vision to enhance performance of neurons. For gaining maximal neural performance of the visual neurons, the experiments, from stimulus to the analysis, should be designed to take advantage of the correlations. In this thesis methods for generating and analyzing natural stimuli were examined by using computations and algorithms. For analyzing responses to natural stimuli in visual neurons, a method with only a few assumptions was developed for estimating information rate in long responses. The novel method gave a good agreement with Shannon information rate with linear system and Gaussian input but was able to handle also nonlinear processing and non-Gaussian data. Secondly, a computer controlled 3D virtual environment with a spherical screen was developed, with a large visual field. The image of the world was projected to the screen with a DLP projector, giving good enough temporal performance for insect vision research. A track-ball was used in closed loop experiments. Thirdly, properties of single photon (“bump”) information transfer at various light levels were investigated in cockroach photoreceptor with a coarse computational model. At dim light (< 10 ph/s), where single bump responses were visible, shot noise was dominant. At higher light levels latency distribution of the bump decreased the information rate, but amplitude distribution of bump did not have an effect. Fourthly, the contribution of K⁺ channels to information rate and energy consumption was investigated by creating a database of computation models with varying channel compositions. The information rate has a maximum as a function of mean conductance, which was a sum of the average K⁺ conductance and the leak conductance. This maximum was fine-tuned by the K⁺ channel composition, which had high so-called novel contribution and relatively low amount of other conductances.