50,910 research outputs found
Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies
© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio
Modeling Life as Cognitive Info-Computation
This article presents a naturalist approach to cognition understood as a
network of info-computational, autopoietic processes in living systems. It
provides a conceptual framework for the unified view of cognition as evolved
from the simplest to the most complex organisms, based on new empirical and
theoretical results. It addresses three fundamental questions: what cognition
is, how cognition works and what cognition does at different levels of
complexity of living organisms. By explicating the info-computational character
of cognition, its evolution, agent-dependency and generative mechanisms we can
better understand its life-sustaining and life-propagating role. The
info-computational approach contributes to rethinking cognition as a process of
natural computation in living beings that can be applied for cognitive
computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201
Network Plasticity as Bayesian Inference
General results from statistical learning theory suggest to understand not
only brain computations, but also brain plasticity as probabilistic inference.
But a model for that has been missing. We propose that inherently stochastic
features of synaptic plasticity and spine motility enable cortical networks of
neurons to carry out probabilistic inference by sampling from a posterior
distribution of network configurations. This model provides a viable
alternative to existing models that propose convergence of parameters to
maximum likelihood values. It explains how priors on weight distributions and
connection probabilities can be merged optimally with learned experience, how
cortical networks can generalize learned information so well to novel
experiences, and how they can compensate continuously for unforeseen
disturbances of the network. The resulting new theory of network plasticity
explains from a functional perspective a number of experimental data on
stochastic aspects of synaptic plasticity that previously appeared to be quite
puzzling.Comment: 33 pages, 5 figures, the supplement is available on the author's web
page http://www.igi.tugraz.at/kappe
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