Sulla illegittimit\ue0 costituzionale dei decreti-legge \u201ctaglia-leggi\u201d

Sommario: 1. I decreti-legge \u201ctaglia-leggi\u201d nel procedimento di semplificazione della legislazione vigente. 2. Sulla asserita necessit\ue0 ed urgenza di una abrogazione espressa. 3. Sulla necessit\ue0 del provvedimento e del provvedere con l\u2019abrogazione differita. 4. Sulla straordinariet\ue0 come fondamento del potere normativo primario del Governo. 5. Sulle interferenze tra decreto-legge e delega legislativa. 6. Infine, alcune considerazioni di merito: una semplificazione\u2026 un po\u2019 troppo complicata

Uncertainty Quantification in Scale-Dependent Models of Flow in Porous Media

Equations governing flow and transport in randomly heterogeneous porous media are stochastic and scale dependent. In the moment equation (ME) method, exact deterministic equations for the leading moments of state variables are obtained at the same support scale as the governing equations. Computable approximations of the MEs can be derived via perturbation expansion in orders of the standard deviation of the random model parameters. As such, their convergence is guaranteed only for standard deviation smaller than one. Here, we consider steady-state saturated flow in a porous medium with random second-order stationary conductivity field. We show it is possible to identify a support scale, $\eta*$, where the typically employed approximate formulations of MEs yield accurate (statistical) moments of a target state variable. Therefore, at support scale $\eta*$ and larger, MEs present an attractive alternative to slowly convergent Monte Carlo (MC) methods whenever lead-order statistical moments of a target state variable are needed. We also demonstrate that a surrogate model for statistical moments can be constructed from MC simulations at larger support scales and be used to accurately estimate moments at smaller scales, where MC simulations are expensive and the ME method is not applicable

Levantamento fitossociológico em Floresta Ombrófila Densa Submontana, Município de Guaraqueçaba, localidade de Serra Negra, Paraná.

1 CD-ROM. Autoria bilíngue: CONGRESSO E EXPOSICAO INTERNACIONAL SOBRE FLORESTAS, 5., 1999, Curitiba

The information transmitted by spike patterns in single neurons

Spike patterns have been reported to encode sensory information in several brain areas. Here we assess the role of specific patterns in the neural code, by comparing the amount of information transmitted with different choices of the readout neural alphabet. This allows us to rank several alternative alphabets depending on the amount of information that can be extracted from them. One can thereby identify the specific patterns that constitute the most prominent ingredients of the code. We finally discuss the interplay of categorical and temporal information in the amount of synergy or redundancy in the neural code.Comment: To be published in Journal of Physiology Paris 200

Computing the local field potential (LFP) from integrate-and-fire network models

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo

Electrodeposition from Deep Eutectic Solvents

Deep eutectic solvents constitute a class of compounds sharing many similarities with properly named ionic liquids. The accepted definition of ionic liquid is a fluid (liquid for T<100 °C) consisting of ions, while DES are eutectic mixtures of Lewis or Brønsted acids and bases. Their most attractive properties are the wide potential windows and the chemical properties largely different from aqueous solutions. In the last few decades, the possibility to electrodeposit decorative and functional coatings employing deep eutectic solvents as electrolytes has been widely investigated. A large number of the deposition procedures described in literature, however, cannot find application in the industrial practice due to competition with existing processes, cost or difficult scalability. From one side, there is the real potential to replace existing plating protocols and to find niche applications for high added-value productions; to the other one, this paves the path towards the electrodeposition of metals and alloys thermodynamically impossible to be obtained via usual aqueous solution processes. The main aim of this chapter is therefore the critical discussion of the applicability of deep eutectic solvents to the electrodeposition of metals and alloys, with a particular attention to the industrial and applicative point of view

How well can we estimate the information carried in neuronal responses from limited samples?

It is difficult to extract the information carried by neuronal responses about a set of stimuli because limited data samples result in biased estimates. Recently two improved procedures have been developed to calculate information from experimental results: a binning-and-correcting procedure and a neural network procedure. We have used data produced from a model of the spatiotemporal receptive fields of parvocellular and magnocellular lateral geniculate neurons to study the performance of these methods as a function of the number of trials used. Both procedures yield accurate results for one-dimensional neuronal codes. They can also be used to produce a reasonable estimate of the extra information in a three-dimensional code, in this instance, within 0.05-0.1 bit of the asymptotically calculated value - about 10% of the total transmitted information. We believe that this performance is much more accurate than previous procedures