Natural Image Coding in V1: How Much Use is Orientation Selectivity?

Orientation selectivity is the most striking feature of simple cell coding in V1 which has been shown to emerge from the reduction of higher-order correlations in natural images in a large variety of statistical image models. The most parsimonious one among these models is linear Independent Component Analysis (ICA), whereas second-order decorrelation transformations such as Principal Component Analysis (PCA) do not yield oriented filters. Because of this finding it has been suggested that the emergence of orientation selectivity may be explained by higher-order redundancy reduction. In order to assess the tenability of this hypothesis, it is an important empirical question how much more redundancies can be removed with ICA in comparison to PCA, or other second-order decorrelation methods. This question has not yet been settled, as over the last ten years contradicting results have been reported ranging from less than five to more than hundred percent extra gain for ICA. Here, we aim at resolving this conflict by presenting a very careful and comprehensive analysis using three evaluation criteria related to redundancy reduction: In addition to the multi-information and the average log-loss we compute, for the first time, complete rate-distortion curves for ICA in comparison with PCA. Without exception, we find that the advantage of the ICA filters is surprisingly small. Furthermore, we show that a simple spherically symmetric distribution with only two parameters can fit the data even better than the probabilistic model underlying ICA. Since spherically symmetric models are agnostic with respect to the specific filter shapes, we conlude that orientation selectivity is unlikely to play a critical role for redundancy reduction

Understanding the retinal basis of vision across species

The vertebrate retina first evolved some 500 million years ago in ancestral marine chordates. Since then, the eyes of different species have been tuned to best support their unique visuoecological lifestyles. Visual specializations in eye designs, large-scale inhomogeneities across the retinal surface and local circuit motifs mean that all species' retinas are unique. Computational theories, such as the efficient coding hypothesis, have come a long way towards an explanation of the basic features of retinal organization and function; however, they cannot explain the full extent of retinal diversity within and across species. To build a truly general understanding of vertebrate vision and the retina's computational purpose, it is therefore important to more quantitatively relate different species' retinal functions to their specific natural environments and behavioural requirements. Ultimately, the goal of such efforts should be to build up to a more general theory of vision

Population pharmacokinetics of lamotrigine in Indian epileptic patients

<p>The aim of this analysis was to describe the pharmacokinetics of oral lamotrigine (LTG) in Indian epileptic patients using a population pharmacokinetic (PPK) modeling approach to confirm that the PK is similar to that of the Caucasian population, and to evaluate and confirm the impact of covariates predictive of inter-individual variability using a simulation platform.</p><p>Blood samples were obtained from 95 patients, and LTG plasma concentrations were determined. Population PK modeling was performed using NONMEM. A one-compartment PK model with first-order absorption and elimination was used to describe the LTG PK. Log-likelihood profiling and normalized prediction distribution errors (NPDE) were used for model evaluation. A simulation study was performed to investigate dose regimens.</p><p>Clearance (CL) was estimated to be 2.27 L/h with inter-individual variability (IIV) of 29 CV%. Volume of distribution (V) was estimated to be 53.6 L (31 CV% IIV). Body weight and concurrent use of carbamazepine and valproate were identified as significant covariates on clearance. Log-likelihood profiling indicated that parameters could be estimated with adequate precision, and NPDE indicated that the model adequately described the data observed. The simulation study illustrated the impact of carbamazepine and valproate on LTG PK, and negligible differences in PK between Indian and Caucasian patients.</p><p>This is the first PK analysis of LTG in Indian patients. The population PK model developed adequately described the data observed. Comparison of identified PK parameters with previous PK analyses in Caucasian patients indicates that CL of LTG is similar, while V is somewhat lower compared with Caucasian patients, but this is not expected to lead to relevant differences in PK profiles during steady state.</p>