Robustness of neural codes and its implication on natural image processing

In this study, based on the view of statistical inference, we investigate the robustness of neural codes, i.e., the sensitivity of neural responses to noise, and its implication on the construction of neural coding. We first identify the key factors that influence the sensitivity of neural responses, and find that the overlap between neural receptive fields plays a critical role. We then construct a robust coding scheme, which enforces the neural responses not only to encode external inputs well, but also to have small variability. Based on this scheme, we find that the optimal basis functions for encoding natural images resemble the receptive fields of simple cells in the striate cortex. We also apply this scheme to identify the important features in the representation of face images and Chinese characters.

Leitlinien zur molekulargenetischen Diagnostik: Fragiles-X und Fragiles-X assoziiertes Tremor/Ataxie Syndrom

Ohne Zusammenfassung

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Individual variability of behavioural responses by Wandering Albatrosses (Diomedea exulans) to human disturbance

Sub-Antarctic Marion Island has had a permanent research station for 50 years and the islands Wandering Albatrosses have been intensively studied for 20 years. The reactions of breeding birds to approaches by a human on foot were recorded. Three response variables were calculated: intensity of vocal reaction (IVR), intensity of non-vocal reaction (INR) and overall response index (ORI). At 5 m from the nest, twice as many birds stood and/or vocalised as at 15 m. Nearest neighbour distance, age and gender did not explain individual variability of responses. Study colony birds had higher IVR scores than non-study colony birds; birds at colonies closest to the station had the highest ORI scores. A better breeding record was associated with lower IVR and ORI scores, but a causative relationship remains to be demonstrated. A minimum viewing distance of 25 m is recommended for breeding Wandering Albatrosses.

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Synchronization properties of networks of electrically coupled neurons in the presence of noise and heterogeneities

We investigate how synchrony can be generated or induced in networks of electrically coupled integrate-and-fire neurons subject to noisy and heterogeneous inputs. Using analytical tools, we find that in a network under constant external inputs, synchrony can appear via a Hopf bifurcation from the asynchronous state to an oscillatory state. In a homogeneous net work, in the oscillatory state all neurons fire in synchrony, while in a heterogeneous network synchrony is looser, many neurons skipping cycles of the oscillation. If the transmission of action potentials via the electrical synapses is effectively excitatory, the Hopf bifurcation is supercritical, while effectively inhibitory transmission due to pronounced hyperpolarization leads to a subcritical bifurcation. In the latter case, the network exhibits bistability between an asynchronous state and an oscillatory state where all the neurons fire in synchrony. Finally we show that for time-varying external inputs, electrical coupling enhances the synchronization in an asynchronous network via a resonance at the firing-rate frequency.

How development changes evolution: conceptual and historical issues in evolutionary developmental biology

Evolutionary developmental biology (Evo-Devo) is a new and rapidly developing field of biology which focuses on questions in the intersection of evolution and development and has been seen by many as a potential synthesis of these two fields. This synthesis is the topic of the books reviewed here. Integrating Evolution and Development (edited by Roger Sansom and Robert Brandon), is a collection of papers on conceptual issues in Evo-Devo, while From Embryology to Evo-Devo (edited by Manfred Laubichler and Jane Maienschein) is a history of the problem of the relations between ontogeny and phylogeny.

Hierarchical analysis of piecewise affine models of gene regulatory networks

A key point in the analysis of dynamical models of biological systems is to handle systems of relatively high dimensions. In the present paper we propose a method to hierarchically organize a certain type of piecewise affine (PWA) differential systems. This specific class of systems has been extensively studied for the past few years, as it provides a good framework to model gene regulatory networks. The method, shown on several examples, allows a qualitative analysis of the asymptotic behavior of a PWA system, decomposing it into several smaller subsystems. This technique, based on the well-known strongly connected components decomposition, is not new. However, its adaptation to the non-smooth PWA differential equations turns out to be quite relevant because of the strong discrete structure underlying these equations. Its biological relevance is shown on a 7-dimensional PWA system modeling the gene network responsible for the carbon starvation response in Escherichia coli.

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Is it a revolution?

Jablonka and Lamb's claim that evolutionary biology is undergoing a ‘revolution’ is queried. But the very concept of revolutionary change has uncertain application to a field organized in the manner of contemporary biology. The explanatory primacy of sequence properties is also discussed.

Hennigian Phylogenetic Systematics and the ‘Groundplan’ vs. ‘Post-Groundplan’ Approaches: A Reply to Kukalová-Peck

The recent contribution by Jarmila Kukalová-Peck on Hennigian phylogenetics and hexapod limb evolution is critically evaluated.

The problem of adaptive individual choice in cultural evolution

This paper tries to explain how individuals manage adaptive individual choice (i.e., the decision to acquire a fitter than average behavior or idea rapidly and tractably) in cultural evolution, despite the fact that acquiring fitness information is very difficult. I argue that the means of solving this problem suggested in the cultural evolution literature largely are various types of decision rules employing representations of fitness correlated properties or states of affairs. I argue that the problem of adaptive individual choice is best solved where some of these learning rule representations are socially transmitted and some are biologically transmitted.

Gridcast—a next generation broadcast infrastructure?

Gridcast is an R&D project investigating grid ideas and technologies in the broadcasting technical infrastructure. In this paper I discuss the business and technical issues in building infrastructures to support broadcasters and outline the structure of the Gridcast grid-based service oriented architecture for broadcasting playout support.

Home Garden Production and Energetic Sustainability in Calakmul,Campeche, Mexico

Energy flows were studied for the 2002–2003 agricultural cycle in four households for which agriculture is part of a diversified survival strategy and four that practice agriculture as a business. Home garden inputs and outputs were measured monthly. Quantified inputs were: household labour, household agro-system production, and purchased external renewable and non-renewable energy. Outputs measured were: sales, family and animal foods. While both strategies had similar indicators in biomass and energy production, vegetable richness, and soil quality, household garden function and sustainability differed between subsistence and commercial householders. Subsistence gardens complemented family diet and contributed to household system resiliency. They relied heavily on renewable energy sources from within their agro-system. Gardens in commercial households reduced fruit tree area and increased animal husbandry for the market. They depended more on purchased non-renewable energy sources and were less sustainable and much less energy efficient than traditional gardens.

Learning-aided predictor integration for system performance prediction

The integration of multiple predictors promises higher prediction accuracy than the accuracy that can be obtained with a single predictor. The challenge is how to select the best predictor at any given moment. Traditionally, multiple predictors are run in parallel and the one that generates the best result is selected for prediction. In this paper, we propose a novel approach for predictor integration based on the learning of historical predictions. Compared with the traditional approach, it does not require running all the predictors simultaneously. Instead, it uses classification algorithms such as k-Nearest Neighbor (k-NN) and Bayesian classification and dimension reduction technique such as Principal Component Analysis (PCA) to forecast the best predictor for the workload under study based on the learning of historical predictions. Then only the forecasted best predictor is run for prediction. Our experimental results show that it achieved 20.18% higher best predictor forecasting accuracy than the cumulative MSE based predictor selection approach used in the popular Network Weather Service system. In addition, it outperformed the observed most accurate single predictor in the pool for 44.23% of the performance traces.

A finite volume method for stochastic integrate-and-fire models

The stochastic integrate and fire neuron is one of the most commonly used stochastic models in neuroscience. Although some cases are analytically tractable, a full analysis typically calls for numerical simulations. We present a fast and accurate finite volume method to approximate the solution of the associated Fokker-Planck equation. The discretization of the boundary conditions offers a particular challenge, as standard operator splitting approaches cannot be applied without modification. We demonstrate the method using stationary and time dependent inputs, and compare them with Monte Carlo simulations. Such simulations are relatively easy to implement, but can suffer from convergence difficulties and long run times. In comparison, our method offers improved accuracy, and decreases computation times by several orders of magnitude. The method can easily be extended to two and three dimensional Fokker-Planck equations.

Multiple realization in comparative perspective

Arguments for multiple realization depend on the idea that the same kind of function is realized by different kinds of structures. It is important to such arguments that we know the kinds used in the arguments have been individuated properly. In the philosophical literature, though, claims about how to individuate kinds are frequently decided on intuitive grounds. This paper criticizes this way of approaching kinds by considering how practicing researchers think about the matter. I will consider several examples in which the practice of researchers on comparative vision conflicts with the standard account of these issues.

Higher order multipole moments for molecular dynamics simulations

In conventional force fields, the electrostatic potential is represented by atom-centred point charges. This choice is in principle arbitrary, but technically convenient. Point charges can be understood as the first term of multipole expansions, which converge with an increasing number of terms towards the accurate representation of the molecular potential given by the electron density distribution. The use of multipole expansions can therefore improve the force field accuracy. Technically, the implementation of atomic multipoles is more involved than the use of point charges. Important points to consider are the orientation of the multipole moments during the trajectory, conformational dependence of the atomic moments and stability of the simulations which are discussed here.

The connectionist framework for gene regulation

I show that gene regulation networks are qualitatively consistent and therefore sufficiently similar to linearly seperable connectionist networks to warrant that the connectionist framework be applied to gene regulation. On this view, natural selection designs gene regulation networks to overcome the difficulty of development. I offer some general lessons about their evolvability that can be learned by examining the generic features of connectionist networks.

Causal Explanation of Indonesian Forest Fires: Concepts, Applications, and Research Priorities

Problems with studies, claims, and assumptions that have been made about the causes of fires in Indonesia's tropical moist forests are identified, and the kinds of concepts, methods, prioritizing, and data needed to resolve the problems are discussed. Separate sections are devoted to studying ignitions, studying fire susceptibility and fire behavior, and using the goal of causal explanation to guide interdisciplinary research.