The texture and taste of food in the brain

Oral texture is represented in the brain areas that represent taste, including the primary taste cortex, the orbitofrontal cortex, and the amygdala. Some neurons represent viscosity, and their responses correlate with the subjective thickness of a food. Other neurons represent fat in the mouth, and represent it by its texture not by its chemical composition, in that they also respond to paraffin oil and silicone in the mouth. The discovery has been made that these fat-responsive neurons encode the coefficient of sliding friction and not viscosity, and this opens the way for the development of new foods with the pleasant mouth feel of fat and with health-promoting designed nutritional properties. A few other neurons respond to free fatty acids (such as linoleic acid), do not respond to fat in the mouth, and may contribute to some 'off' tastes in the mouth. Some other neurons code for astringency. Others neurons respond to other aspects of texture such as the crisp fresh texture of a slice of apple vs the same apple after blending. Different neurons respond to different combinations of these texture properties, oral temperature, taste, and in the orbitofrontal cortex to olfactory and visual properties of food. In the orbitofrontal cortex, the pleasantness and reward value of the food is represented, but the primary taste cortex represents taste and texture independently of value. These discoveries were made in macaques that have similar cortical brain areas for taste and texture processing as humans, and complementary human functional neuroimaging studies are described. This article is protected by copyright. All rights reserved. [Abstract copyright: This article is protected by copyright. All rights reserved.

Investigation of additives for improvement of adhesive and elastomer performance Final report

Improvement additives for adhesive and elastomer performanc

Representational capacity of a set of independent neurons

The capacity with which a system of independent neuron-like units represents a given set of stimuli is studied by calculating the mutual information between the stimuli and the neural responses. Both discrete noiseless and continuous noisy neurons are analyzed. In both cases, the information grows monotonically with the number of neurons considered. Under the assumption that neurons are independent, the mutual information rises linearly from zero, and approaches exponentially its maximum value. We find the dependence of the initial slope on the number of stimuli and on the sparseness of the representation.Comment: 19 pages, 6 figures, Phys. Rev. E, vol 63, 11910 - 11924 (2000

A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks

Understanding how the dynamics of a neural network is shaped by the network structure, and consequently how the network structure facilitates the functions implemented by the neural system, is at the core of using mathematical models to elucidate brain functions. This study investigates the tracking dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of neuronal recurrent interactions, CANNs can hold a continuous family of stationary states. They form a continuous manifold in which the neural system is neutrally stable. We systematically explore how this property facilitates the tracking performance of a CANN, which is believed to have clear correspondence with brain functions. By using the wave functions of the quantum harmonic oscillator as the basis, we demonstrate how the dynamics of a CANN is decomposed into different motion modes, corresponding to distortions in the amplitude, position, width or skewness of the network state. We then develop a perturbative approach that utilizes the dominating movement of the network's stationary states in the state space. This method allows us to approximate the network dynamics up to an arbitrary accuracy depending on the order of perturbation used. We quantify the distortions of a Gaussian bump during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable and the reaction time for the network to catch up with an abrupt change in the stimulus.Comment: 43 pages, 10 figure

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

Biotic homogenisation and differentiation as directional change in beta diversity: synthesising driver–response relationships to develop conceptualmodels across ecosystems

Biotic homogenisation is defined as decreasing dissimilarity among ecological assemblages sampled within a given spatial area over time. Biotic differentiation, in turn, is defined as increasing dissimilarity over time. Overall, changes in the spatial dissimilarities among assemblages (termed ‘beta diversity’) is an increasingly recognised feature of broader biodiversity change in the Anthropocene. Empirical evidence of biotic homogenisation and biotic differentiation remains scattered across different ecosystems. Most meta-analyses quantify the prevalence and direction of change in beta diversity, rather than attempting to identify underlying ecological drivers of such changes. By conceptualising the mechanisms that contribute to decreasing or increasing dissimilarity in the composition of ecological assemblages across space, environmental managers and conservation practitioners can make informed decisions about what interventions may be required to sustain biodiversity and can predict potential biodiversity outcomes of future disturbances. We systematically reviewed and synthesised published empirical evidence for ecological drivers of biotic homogenisation and differentiation across terrestrial, marine, and freshwater realms to derive conceptual models that explain changes in spatial beta diversity. We pursued five key themes in our review: (i) temporal environmental change; (ii) disturbance regime; (iii) connectivity alteration and species redistribution; (iv) habitat change; and (v) biotic and trophic interactions. Our first conceptual model highlights how biotic homogenisation and differentiation can occur as a function of changes in local (alpha) diversity or regional (gamma) diversity, independently of species invasions and losses due to changes in species occurrence among assemblages. Second, the direction and magnitude of change in beta diversity depends on the interaction between spatial variation (patchiness) and temporal variation (synchronicity) of disturbance events. Third, in the context of connectivity and species redistribution, divergent beta diversity outcomes occur as different species have different dispersal characteristics, and the magnitude of beta diversity change associated with species invasions also depends strongly on alpha and gamma diversity prior to species invasion. Fourth, beta diversity is positively linked with spatial environmental variability, such that biotic homogenisation and differentiation occur when environmental heterogeneity decreases or increases, respectively. Fifth, species interactions can influence beta diversity via habitat modification, disease, consumption (trophic dynamics), competition, and by altering ecosystem productivity. Our synthesis highlights the multitude of mechanisms that cause assemblages to be more or less spatially similar in composition (taxonomically, functionally, phylogenetically) through time. We consider that future studies should aim to enhance our collective understanding of ecological systems by clarifying the underlying mechanisms driving homogenisation or differentiation, rather than focusing only on reporting the prevalence and direction of change in beta diversity, per se. biodiversity, beta diversity, biotic homogenisation, biotic differentiation, species assemblage, turnoverpublishedVersio

The impact of injecting networks on hepatitis C transmission and treatment in people who inject drugs

With the development of new highly efficacious direct acting antiviral treatments (DAAs) for hepatitis C (HCV), the concept of treatment as prevention is gaining credence. To date the majority of mathematical models assume perfect mixing with injectors having equal contact with all other injectors. This paper explores how using a networks based approach to treat people who inject drugs (PWID) with DAAs affects HCV prevalence. Method: Using observational data we parameterized an Exponential Random Graph Model containing 524 nodes. We simulated transmission of HCV through this network using a discrete time, stochastic transmission model. The effect of five treatment strategies on the prevalence of HCV was investigated; two of these strategies were 1) treat randomly selected nodes and 2) “treat your friends” where an individual is chosen at random for treatment and all their infected neighbours are treated. Results: As treatment coverage increases, HCV prevalence at 10 years reduces for both the high efficacy and low efficacy treatment. Within each set of parameters, the “treat your friends” strategy performed better than the random strategy being most marked for higher efficacy treatment. For example over 10 years of treating 25 per 1000 PWID, the prevalence drops from 50% to 40% for the random strategy, and to 33% for the “treat your friends” strategy (6.5% difference, 95% CI 5.1 – 8.1%). Discussion: “Treat your friends” is a feasible means of utilising network strategies to improve treatment efficiency. In an era of highly efficacious and highly tolerable treatment such an approach will benefit not just the individual but the community more broadly by reducing the prevalence of HCV amongst PWID

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

Multiscale Computations on Neural Networks: From the Individual Neuron Interactions to the Macroscopic-Level Analysis

We show how the Equation-Free approach for multi-scale computations can be exploited to systematically study the dynamics of neural interactions on a random regular connected graph under a pairwise representation perspective. Using an individual-based microscopic simulator as a black box coarse-grained timestepper and with the aid of simulated annealing we compute the coarse-grained equilibrium bifurcation diagram and analyze the stability of the stationary states sidestepping the necessity of obtaining explicit closures at the macroscopic level. We also exploit the scheme to perform a rare-events analysis by estimating an effective Fokker-Planck describing the evolving probability density function of the corresponding coarse-grained observables