How many-particle interactions develop after ultrafast excitation of an electron-hole plasma.

Electrostatic coupling between particles is important in many microscopic phenomena found in nature. The interaction between two isolated point charges is described by the bare Coulomb potential, but in many-body systems this interaction is modified as a result of the collective response of the screening cloud surrounding each charge carrier. One such system involves ultrafast interactions between quasi-free electrons in semiconductors-which are central to high-speed and future quantum electronic devices. The femtosecond kinetics of nonequilibrium Coulomb systems has been calculated using static and dynamical screening models that assume the instantaneous formation of interparticle correlations. However, some quantum kinetic theories suggest that a regime of unscreened bare Coulomb collisions might exist on ultrashort timescales. Here we monitor directly the temporal evolution of the charge-charge interactions after ultrafast excitation of an electron-hole plasma in GaAs. We show that the onset of collective behaviour such as Coulomb screening and plasmon scattering exhibits a distinct time delay of the order of the inverse plasma frequency, that is, several 10(-14) seconds.     

Large-scale vortex lattice emerging from collectively moving microtubules

Spontaneous collective motion, as in some flocks of bird and schools of fish, is an example of an emergent phenomenon. Such phenomena are at present of great interest and physicists have put forward a number of theoretical results that so far lack experimental verification. In animal behaviour studies, large-scale data collection is now technologically possible, but data are still scarce and arise from observations rather than controlled experiments. Multicellular biological systems, such as bacterial colonies or tissues, allow more control, but may have many hidden variables and interactions, hindering proper tests of theoretical ideas. However, in systems on the subcellular scale such tests may be possible, particularly in in vitro experiments with only few purified components. Motility assays, in which protein filaments are driven by molecular motors grafted to a substrate in the presence of ATP, can show collective motion for high densities of motors and attached filaments. This was demonstrated recently for the actomyosin system, but a complete understanding of the mechanisms at work is still lacking. Here we report experiments in which microtubules are propelled by surface-bound dyneins. In this system it is possible to study the local interaction: we find that colliding microtubules align with each other with high probability. At high densities, this alignment results in self-organization of the microtubules, which are on average 15?μm long, into vortices with diameters of around 400?μm. Inside the vortices, the microtubules circulate both clockwise and anticlockwise. On longer timescales, the vortices form a lattice structure. The emergence of these structures, as verified by a mathematical model, is the result of the smooth, reptation-like motion of single microtubules in combination with local interactions (the nematic alignment due to collisions)--there is no need for long-range interactions. Apart from its potential relevance to cortical arrays in plant cells and other biological situations, our study provides evidence for the existence of previously unsuspected universality classes of collective motion phenomena.     

Function of identified interneurons in the leech elucidated using neural networks trained by back-propagation

Mechanical stimulation of the body surface of the leech causes a localized withdrawal from dorsal, ventral and lateral stimuli. The pathways from sensory to motor neurons in the reflex include at least one interneuron. We have identified a subset of interneurons contributing to the reflex by intracellular recording, and our analysis of interneuron input and output connections suggests a network in which most interneurons respond to more than one sensory input, most have effects on all motor neurons and in which each form of the behaviour is produced by appropriate and inappropriate effects of many interneurons. To determine whether interneurons of this type can account for the behaviour, or whether additional types are required, model networks were trained by back-propagation to reproduce the physiologically determined input-output function of the reflex. Quantitative comparisons of model and actual connection strengths show that model interneurons are similar to real ones. Consequently, the identified subset of interneurons could control local bending as part of a distributed processing network in which each form of the behaviour is produced by the appropriate and inappropriate effects of many interneurons.     

Adaptive coding of visual information in neural populations

Our perception of the environment relies on the capacity of neural networks to adapt rapidly to changes in incoming stimuli. It is increasingly being realized that the neural code is adaptive, that is, sensory neurons change their responses and selectivity in a dynamic manner to match the changes in input stimuli. Understanding how rapid exposure, or adaptation, to a stimulus of fixed structure changes information processing by cortical networks is essential for understanding the relationship between sensory coding and behaviour. Physiological investigations of adaptation have contributed greatly to our understanding of how individual sensory neurons change their responses to influence stimulus coding, yet whether and how adaptation affects information coding in neural populations is unknown. Here we examine how brief adaptation (on the timescale of visual fixation) influences the structure of interneuronal correlations and the accuracy of population coding in the macaque (Macaca mulatta) primary visual cortex (V1). We find that brief adaptation to a stimulus of fixed structure reorganizes the distribution of correlations across the entire network by selectively reducing their mean and variability. The post-adaptation changes in neuronal correlations are associated with specific, stimulus-dependent changes in the efficiency of the population code, and are consistent with changes in perceptual performance after adaptation. Our results have implications beyond the predictions of current theories of sensory coding, suggesting that brief adaptation improves the accuracy of population coding to optimize neuronal performance during natural viewing.     

Unconventional critical behaviour in a quasi-two-dimensional organic conductor

Changing the interactions between particles in an ensemble--by varying the temperature or pressure, for example--can lead to phase transitions whose critical behaviour depends on the collective nature of the many-body system. Despite the diversity of ingredients, which include atoms, molecules, electrons and their spins, the collective behaviour can be grouped into several families (called 'universality classes') represented by canonical spin models. One kind of transition, the Mott transition, occurs when the repulsive Coulomb interaction between electrons is increased, causing wave-like electrons to behave as particles. In two dimensions, the attractive behaviour responsible for the superconductivity in high-transition temperature copper oxide and organic compounds appears near the Mott transition, but the universality class to which two-dimensional, repulsive electronic systems belongs remains unknown. Here we present an observation of the critical phenomena at the pressure-induced Mott transition in a quasi-two-dimensional organic conductor using conductance measurements as a probe. We find that the Mott transition in two dimensions is not consistent with known universality classes, as the observed collective behaviour has previously not been seen. This peculiarity must be involved in any emergent behaviour near the Mott transition in two dimensions.     

Disentangling nestedness from models of ecological complexity

Complex networks of interactions are ubiquitous and are particularly important in ecological communities, in which large numbers of species exhibit negative (for example, competition or predation) and positive (for example, mutualism) interactions with one another. Nestedness in mutualistic ecological networks is the tendency for ecological specialists to interact with a subset of species that also interact with more generalist species. Recent mathematical and computational analysis has suggested that such nestedness increases species richness. By examining previous results and applying computational approaches to 59 empirical data sets representing mutualistic plant–pollinator networks, we show that this statement is incorrect. A simpler metric—the number of mutualistic partners a species has—is a much better predictor of individual species survival and hence, community persistence. Nestedness is, at best, a secondary covariate rather than a causative factor for biodiversity in mutualistic communities. Analysis of complex networks should be accompanied by analysis of simpler, underpinning mechanisms that drive multiple higher-order network properties.     

Correlation between neural spike trains increases with firing rate

Populations of neurons in the retina, olfactory system, visual and somatosensory thalamus, and several cortical regions show temporal correlation between the discharge times of their action potentials (spike trains). Correlated firing has been linked to stimulus encoding, attention, stimulus discrimination, and motor behaviour. Nevertheless, the mechanisms underlying correlated spiking are poorly understood, and its coding implications are still debated. It is not clear, for instance, whether correlations between the discharges of two neurons are determined solely by the correlation between their afferent currents, or whether they also depend on the mean and variance of the input. We addressed this question by computing the spike train correlation coefficient of unconnected pairs of in vitro cortical neurons receiving correlated inputs. Notably, even when the input correlation remained fixed, the spike train output correlation increased with the firing rate, but was largely independent of spike train variability. With a combination of analytical techniques and numerical simulations using 'integrate-and-fire' neuron models we show that this relationship between output correlation and firing rate is robust to input heterogeneities. Finally, this overlooked relationship is replicated by a standard threshold-linear model, demonstrating the universality of the result. This connection between the rate and correlation of spiking activity links two fundamental features of the neural code.     

Neurons in the orbitofrontal cortex encode economic value

Economic choice is the behaviour observed when individuals select one among many available options. There is no intrinsically 'correct' answer: economic choice depends on subjective preferences. This behaviour is traditionally the object of economic analysis and is also of primary interest in psychology. However, the underlying mental processes and neuronal mechanisms are not well understood. Theories of human and animal choice have a cornerstone in the concept of 'value'. Consider, for example, a monkey offered one raisin versus one piece of apple: behavioural evidence suggests that the animal chooses by assigning values to the two options. But where and how values are represented in the brain is unclear. Here we show that, during economic choice, neurons in the orbitofrontal cortex (OFC) encode the value of offered and chosen goods. Notably, OFC neurons encode value independently of visuospatial factors and motor responses. If a monkey chooses between A and B, neurons in the OFC encode the value of the two goods independently of whether A is presented on the right and B on the left, or vice versa. This trait distinguishes the OFC from other brain areas in which value modulates activity related to sensory or motor processes. Our results have broad implications for possible psychological models, suggesting that economic choice is essentially choice between goods rather than choice between actions. In this framework, neurons in the OFC seem to be a good candidate network for value assignment underlying economic choice.     

Crystalline ribonuclease A loses function below the dynamical transition at 220 K.

When the dynamic properties of many different proteins are plotted as a function of temperature, biphasic behaviour is observed, with a broad transition centred around 220 K. Atomic mean-square displacements from X-ray crystallography and M?ssbauer scattering show this behaviour, as do electron transfer rates and dynamic information from inelastic neutron scattering. Molecular dynamics simulations over a range of temperatures also exhibit a transition at about 220 K: high-temperature atomic fluctuations are dominated by anharmonic collective motions of bonded and nonbonded groups of atoms, but below 220 K the predominant dynamic behaviour is harmonic vibration of individual atoms. Here we show by high-resolution X-ray diffraction that crystalline ribonuclease A does not bind substrate or inhibitor at 212 K but will bind either rapidly at 228 K. Once bound at the higher temperature, inhibitor cannot be washed off after the enzyme is cooled to below the transition temperature. These results suggest that enzyme flexibility is required for catalytic function.     

社会系统中舆情动力学的统计物理研究

近年来,许多物理学家开始关注利用统计物理思想和方法来研究社会系统中群体行为的涌现现象,例如疾病的雪崩、舆情的传播以及同步现象.实证研究表明:真实社会系统具有其特有的性质,例如小世界特性和节点度分布的无标度特性等.一个显而易见的问题:这些特性如何影响社会系统中群体行为的涌现.本文关注复杂网络拓扑结构对舆情传播行为的影响,并扼要介绍了社会系统中3个著名的舆情演化动力学模型及其研究现状,旨在为初学者提供一定的帮助.     

Temporal difference models describe higher-order learning in humans

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.     

Neuropeptide fusion of two motor-pattern generator circuits

Animals make many different movements as circumstances dictate. These movements often involve the coordination of several neural networks, the output of which can be changed by modulatory substances. Here we report that the neuropeptide red pigment concentrating hormone modulates the interactions between two rhythmic pattern-generating networks in the lobster stomatogastric nervous system. Red pigment concentrating hormone markedly enhances the amplitude of synaptic interactions between elements of two pattern-generating networks--the cardiac sac and the gastric mill. Consequently, two networks operating under some circumstances virtually independently can be fused into one functional unit operating differently from either of the two original networks. These results show how a neuropeptide can alter the functional configuration of a neural network so that widely disparate outputs can be produced by the same neurons.     

Synaptic potentiation onto habenula neurons in the learned helplessness model of depression

The cellular basis of depressive disorders is poorly understood. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (that is, disappointment or anticipation of a negative outcome). LHb neurons project to, and modulate, dopamine-rich regions, such as the ventral tegmental area (VTA), that control reward-seeking behaviour and participate in depressive disorders. Here we show that in two learned helplessness models of depression, excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal's helplessness behaviour and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective for patients who are depressed, markedly suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behaviour in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression.     

Switching on and off fear by distinct neuronal circuits

Switching between exploratory and defensive behaviour is fundamental to survival of many animals, but how this transition is achieved by specific neuronal circuits is not known. Here, using the converse behavioural states of fear extinction and its context-dependent renewal as a model in mice, we show that bi-directional transitions between states of high and low fear are triggered by a rapid switch in the balance of activity between two distinct populations of basal amygdala neurons. These two populations are integrated into discrete neuronal circuits differentially connected with the hippocampus and the medial prefrontal cortex. Targeted and reversible neuronal inactivation of the basal amygdala prevents behavioural changes without affecting memory or expression of behaviour. Our findings indicate that switching between distinct behavioural states can be triggered by selective activation of specific neuronal circuits integrating sensory and contextual information. These observations provide a new framework for understanding context-dependent changes of fear behaviour.     

Wavelet Neural Networks for Adaptive Equalization by Using the Orthogonal Least Square Algorithm

Equalizers are widely used in digital communication systems for corrupted or time varying channels. To overcome performance decline for noisy and nonlinear channels, many kinds of neural network models have been used in nonlinear equalization. In this paper, we propose a new nonlinear channel equalization, which is structured by wavelet neural networks. The orthogonal least square algorithm is applied to update the weighting matrix of wavelet networks to form a more compact wavelet basis unit, thus obtaining good equalization performance. The experimental results show that performance of the proposed equalizer based on wavelet networks can significantly improve the neural modeling accuracy and outperform conventional neural network equalization in signal to noise ratio and channel non-linearity.     

Many-body and correlation effects in semiconductors

Solids consist of 1022-1023 particles per cubic centimetre, interacting through infinite-range Coulomb interactions. The linear response of a solid to a weak external perturbation is well described by the concept of non-interacting 'quasiparticles' first introduced by Landau. But interactions between quasiparticles can be substantial in dense systems. For example, studies over the past decade have shown that Coulomb correlations between quasiparticles dominate the nonlinear optical response of semiconductors, in marked contrast to the behaviour of atomic systems. These Coulomb correlations and other many-body interactions are important not only for semiconductors, but also for all condensed-matter systems.     

A role for lateral hypothalamic orexin neurons in reward seeking

The lateral hypothalamus is a brain region historically implicated in reward and motivation, but the identity of the neurotransmitters involved are unknown. The orexins (or hypocretins) are neuropeptides recently identified as neurotransmitters in lateral hypothalamus neurons. Although knockout and transgenic overexpression studies have implicated orexin neurons in arousal and sleep, these cells also project to reward-associated brain regions, including the nucleus accumbens and ventral tegmental area. This indicates a possible role for these neurons in reward function and motivation, consistent with previous studies implicating these neurons in feeding. Here we show that activation of lateral hypothalamus orexin neurons is strongly linked to preferences for cues associated with drug and food reward. In addition, we show that chemical activation of lateral hypothalamus orexin neurons reinstates an extinguished drug-seeking behaviour. This reinstatement effect was completely blocked by prior administration of an orexin A antagonist. Moreover, administration of the orexin A peptide directly into the ventral tegmental area also reinstated drug-seeking. These data reveal a new role for lateral hypothalamus orexin neurons in reward-seeking, drug relapse and addiction.     

Circadian rhythms in olfactory responses of Drosophila melanogaster.

The core mechanism of circadian timekeeping in arthropods and vertebrates consists of feedback loops involving several clock genes, including period (per) and timeless (tim). In the fruitfly Drosophila, circadian oscillations in per expression occur in chemosensory cells of the antennae, even when the antennae are excised and maintained in isolated organ culture. Here we demonstrate a robust circadian rhythm in Drosophila in electrophysiological responses to two classes of olfactory stimuli. These rhythms are observed in wild-type flies during light-dark cycles and in constant darkness, but are abolished in per or tim null-mutant flies (per01 and tim01) which lack rhythms in adult emergence and locomotor behaviour. Olfactory rhythms are also abolished in the per 7.2:2 transgenic line in which per expression is restricted to the lateral neurons of the optic lobe. Because per 7.2:2 flies do not express per in peripheral oscillators, our results provide evidence that peripheral circadian oscillators are necessary for circadian rhythms in olfactory responses. As olfaction is essential for food acquisition, social interactions and predator avoidance in many animals, circadian regulation of olfactory systems could have profound effects on the behaviour of organisms that rely on this sensory modality.     

Strong effect of dispersal network structure on ecological dynamics

A central question in ecology with great importance for management, conservation and biological control is how changing connectivity affects the persistence and dynamics of interacting species. Researchers in many disciplines have used large systems of coupled oscillators to model the behaviour of a diverse array of fluctuating systems in nature. In the well-studied regime of weak coupling, synchronization is favoured by increases in coupling strength and large-scale network structures (for example 'small worlds') that produce short cuts and clustering. Here we show that, by contrast, randomizing the structure of dispersal networks in a model of predators and prey tends to favour asynchrony and prolonged transient dynamics, with resulting effects on the amplitudes of population fluctuations. Our results focus on synchronization and dynamics of clusters in models, and on timescales, more appropriate for ecology, namely smaller systems with strong interactions outside the weak-coupling regime, rather than the better-studied cases of large, weakly coupled systems. In these smaller systems, the dynamics of transients and the effects of changes in connectivity can be well understood using a set of methods including numerical reconstructions of phase dynamics, examinations of cluster formation and the consideration of important aspects of cyclic dynamics, such as amplitude.