On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

Evaluation of the influence of visual parameters on wave transmission velocity in sawn chestnut timber

Non-destructive, visual evaluation and mechanical testing techniques were used to assess the structural properties of 374 samples of chestnut (Castanea sativa). The principal components method was applied to establish and interpret correlations between variables obtained of modulus of elasticity, bending strength and density. The static modulus of elasticity presented higher correlation values than those obtained using non-destructive methods. Bending strength presented low correlations with the non-destructive parameters, but there was some relation to the different knot ratios defined. The relationship was stronger with the most widely used ratio, CKDR. No significant correlations were observed between any of the variables and density.

Mapping the visual landscape quality in Europe using physical attributes

he inclusion of environmental care data in the decision-making process should be based on the results obtained after scienti?cally evaluating different environmental variables. Herein, a European landscape geographic model is presented. This landscape map would allow the environmental care variable ?visual landscape?, along with other information related to vegetation, geology, soils, cultural variables, etc., to be integrated into the planning process. The methodology used is not new since it has already been tested in Spain by the authors. Nevertheless, the model was adapted to cope with the much more extensive territory of the European Union. This meant dealing with computational dif?culties, and a lack of information. The result of this work is a raster map (100 m cell size) that evaluates landscape quality in Europe by dividing the area into seven visual quality classes. This is a practical tool for territorial development that will facilitate the environmental assessment of plans, such as infrastructure plans, within a strategic pan-European framework.