A theoretical model of neuronal population coding of stimuli with both continuous and discrete dimensions

In a recent study the initial rise of the mutual information between the firing rates of N neurons and a set of p discrete stimuli has been analytically evaluated, under the assumption that neurons fire independently of one another to each stimulus and that each conditional distribution of firing rates is gaussian. Yet real stimuli or behavioural correlates are high-dimensional, with both discrete and continuously varying features.Moreover, the gaussian approximation implies negative firing rates, which is biologically implausible. Here, we generalize the analysis to the case where the stimulus or behavioural correlate has both a discrete and a continuous dimension. In the case of large noise we evaluate the mutual information up to the quadratic approximation as a function of population size. Then we consider a more realistic distribution of firing rates, truncated at zero, and we prove that the resulting correction, with respect to the gaussian firing rates, can be expressed simply as a renormalization of the noise parameter. Finally, we demonstrate the effect of averaging the distribution across the discrete dimension, evaluating the mutual information only with respect to the continuously varying correlate.Comment: 20 pages, 10 figure

Inclusion complexes of β-cyclodextrin with tricyclic drugs: an X-ray diffraction, NMR and molecular dynamics study

Tricyclic fused-ring cyclobenzaprine (1) and amitriptyline (2) form 1:1 inclusion complexes with β-cyclodextrin (β-CD) in the solid state and in water solution. Rotating frame NOE experiments (ROESY) showed the same geometry of inclusion for both 1/β-CD and 2/β-CD complexes, with the aromatic ring system entering the cavity from the large rim of the cyclodextrin and the alkylammonium chain protruding out of the cavity and facing the secondary OH rim. These features matched those found in the molecular dynamics (MD) simulations in solution and in the solid state from single-crystal X-ray diffraction of 1/β-CD and 2/β-CD complexes. The latter complex was found in a single conformation in the solid state, whilst the MD simulations in explicit water reproduced the conformational transitions observed experimentally for the free molecule

The Effect of Grape Temperature on the Sensory Perception of Méthode Cap Classique Wines

The production process of South African bottle-fermented sparkling wine, the Méthode Cap Classique (MCC), follows the traditional French method (méthode champenoise), although each cellar has its own unique additions to the method. South African winemakers use different techniques and blends to achieve their award-winning MCCs, but there have not been many scientific investigations of the science behind these wines. This project is one of the first scientific studies on MCC. MCC wines were made using Chardonnay and Pinot Noir grapes harvested over two vintages (2014 and 2015) from two regions (Robertson and Darling) and stored at 0°C, 10°C, 25°C and 30°C before processing. The study was aimed at investigating the effect of grape storage temperature on the sensory characteristics of MCCs. The aroma and taste of the final nine-month old MCCs were evaluated, with each region analysed separately. The study showed a grouping of the MCCs according to temperature treatments for both vintages. There werevintage differences in terms of the attributes cited and the frequency of citations. Based on the frequency of citation, the MCCs made 2014 from grapes stored at 0°C and 10°C were described by the judges as having a fruity, fresh and crisp aroma, whilst those made from grapes stored at 25°C and 30°C were described as having oxidised fruit, volatile acidity and solvent-like aromas. The judges perceived less oxidation and volatile acidity (VA) (in terms of the frequency of citation) in the aroma of the 2015 MCCs, although treatments at higher temperatures were still associated with less desirable attributes compared to treatments at lower temperature. This study shown that the temperature of the grape at the time ofprocessing has a significant effect on the aroma of MCCs aged nine months, and not so much of an effect on the taste

The effect of oxygen in the photocatalytic oxidation pathways of perfluorooctanoic acid

The influence of oxygen in the photocatalytic oxidation of perfluorooctanoic acid (PFOA) promoted by a commercial nano-sized titanium dioxide was studied by testing the reaction in different conditions: static air, oxygen flux, nitrogen flux and pre-saturated nitrogen flux. The reaction was monitored by Total Organic Carbon (TOC) analysis and Ionic Chromatography (IC). Shorter chain perfluorocarboxylic acids (PFCAs; C-n, n = 1-7) intermediate degradation products were quantitatively determined by High-Performance Liquid Chromatography combined with Mass Spectrometry (HPLC-MS) analysis. The presence of shorter chain PFCAs in solution was also monitored by F-19 NMR. The experimental findings are in agreement with two major oxidative pathways: C-n -> Cn-1 photo-redox and beta-scissions routes mediated by COF2 elimination. Depending on the experimental conditions, the mutually operating mechanisms could be unbalanced up to the complete predominance of one pathway over the other. In particular, the existence of the beta-scissions route with COF2 elimination was corroborated by the isolation and characterization of carbonyl difluoride, a predicted fluorinated decomposition by-product

Applications of Information Theory to Analysis of Neural Data

Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. It has a number of useful properties: it is a general measure sensitive to any relationship, not only linear effects; it has meaningful units which in many cases allow direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single trials, rather than in averages over multiple trials. A variety of information theoretic quantities are commonly used in neuroscience - (see entry "Definitions of Information-Theoretic Quantities"). In this entry we review some applications of information theory in neuroscience to study encoding of information in both single neurons and neuronal populations.Comment: 8 pages, 2 figure

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

Incremental Mutual Information: A New Method for Characterizing the Strength and Dynamics of Connections in Neuronal Circuits

Understanding the computations performed by neuronal circuits requires characterizing the strength and dynamics of the connections between individual neurons. This characterization is typically achieved by measuring the correlation in the activity of two neurons. We have developed a new measure for studying connectivity in neuronal circuits based on information theory, the incremental mutual information (IMI). By conditioning out the temporal dependencies in the responses of individual neurons before measuring the dependency between them, IMI improves on standard correlation-based measures in several important ways: 1) it has the potential to disambiguate statistical dependencies that reflect the connection between neurons from those caused by other sources (e. g. shared inputs or intrinsic cellular or network mechanisms) provided that the dependencies have appropriate timescales, 2) for the study of early sensory systems, it does not require responses to repeated trials of identical stimulation, and 3) it does not assume that the connection between neurons is linear. We describe the theory and implementation of IMI in detail and demonstrate its utility on experimental recordings from the primate visual system

Replica symmetric evaluation of the information transfer in a two-layer network in presence of continuous+discrete stimuli

In a previous report we have evaluated analytically the mutual information between the firing rates of N independent units and a set of multi-dimensional continuous+discrete stimuli, for a finite population size and in the limit of large noise. Here, we extend the analysis to the case of two interconnected populations, where input units activate output ones via gaussian weights and a threshold linear transfer function. We evaluate the information carried by a population of M output units, again about continuous+discrete correlates. The mutual information is evaluated solving saddle point equations under the assumption of replica symmetry, a method which, by taking into account only the term linear in N of the input information, is equivalent to assuming the noise to be large. Within this limitation, we analyze the dependence of the information on the ratio M/N, on the selectivity of the input units and on the level of the output noise. We show analytically, and confirm numerically, that in the limit of a linear transfer function and of a small ratio between output and input noise, the output information approaches asymptotically the information carried in input. Finally, we show that the information loss in output does not depend much on the structure of the stimulus, whether purely continuous, purely discrete or mixed, but only on the position of the threshold nonlinearity, and on the ratio between input and output noise.Comment: 19 pages, 4 figure