Balancing feed-forward excitation and inhibition via Hebbian inhibitory synaptic plasticity

It has been suggested that excitatory and inhibitory inputs to cortical cells are balanced, and that this balance is important for the highly irregular firing observed in the cortex. There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates 'negative feedback' that balances excitation and inhibition, which contrasts with the 'positive feedback' of excitatory Hebbian synaptic plasticity. As a result, this balance may increase the sensitivity of the learning dynamics to the correlation structure of the excitatory inputs.     

Heuristics for the FMS-loading and part-type-selection problems

In this article, we study the FMS-loading and part-type-selection problems, in which each part is processed by a series of operations. Two heuristic methods are presented for the objectives of balancing workloads and meeting due dates. These heuristics perform a specific evaluation of the objective function at each iteration. The goal of Heuristic#1 is to achieve workload balance. The additional goal of Heuristic#2 is to reduce the number of late part types. The loading and part-type selection must satisfy a tooling constraint. Computational results are encouraging and indicate significant improvement over the existing methods.     

A concept of welfare based on reward evaluating mechanisms in the brain: anticipatory behaviour as an indicator for the state of reward systems

In this review we attempt to link the efficiency by which animals behave (economy of animal behaviour) to a neuronal substrate and subjective states to arrive at a definition of animal welfare which broadens the scope of its study. Welfare is defined as the balance between positive (reward, satisfaction) and negative (stress) experiences or affective states. The state of this balance may range from positive (good welfare) to negative (poor welfare). These affective states are momentary or transient states which occur against the background of and are integrated with the state of this balancing system. As will be argued the efficiency in behaviour requires that, for instance, satisfaction is like a moving target: reward provides the necessary feedback to guide behaviour; it is a not steady-state which can be maintained for long. This balancing system is reflected in the brain by the concerted action of opioid and mesolimbic dopaminergic systems. The state of this system reflects the coping capacity of the animal and is determined by previous events. In other words, this integrative approach of behavioural biology and neurobiology aims at understanding how the coping capacity of animals may be affected and measured. We argue that this balancing system underlies the economy of behaviour. Furthermore we argue that among other techniques anticipation in Pavlovian conditioning is an easy and useful tool to assess the state of this balancing system: for estimating the state of an animal in terms of welfare we focus on the conditions when an animal is facing a challenge.     

Inference on the strength of balancing selection for epistatically interacting loci

Existing inference methods for estimating the strength of balancing selection in multi-locus genotypes rely on the assumption that there are no epistatic interactions between loci. Complex systems in which balancing selection is prevalent, such as sets of human immune system genes, are known to contain components that interact epistatically. Therefore, current methods may not produce reliable inference on the strength of selection at these loci. In this paper, we address this problem by presenting statistical methods that can account for epistatic interactions in making inference about balancing selection. A theoretical result due to Fearnhead (2006) is used to build a multi-locus Wright-Fisher model of balancing selection, allowing for epistatic interactions among loci. Antagonistic and synergistic types of interactions are examined. The joint posterior distribution of the selection and mutation parameters is sampled by Markov chain Monte Carlo methods, and the plausibility of models is assessed via Bayes factors. As a component of the inference process, an algorithm to generate multi-locus allele frequencies under balancing selection models with epistasis is also presented. Recent evidence on interactions among a set of human immune system genes is introduced as a motivating biological system for the epistatic model, and data on these genes are used to demonstrate the methods.     

Balanced networks under spike-time dependent plasticity

The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.     

Regularity in an environment produces an internal torque pattern for biped balance control

In this paper, we present a control method for achieving biped static balance under unknown periodic external forces whose periods are only known. In order to maintain static balance adaptively in an uncertain environment, it is essential to have information on the ground reaction forces. However, when the biped is exposed to a steady environment that provides an external force periodically, uncertain factors on the regularity with respect to a steady environment are gradually clarified using learning process, and finally a torque pattern for balancing motion is acquired. Consequently, static balance is maintained without feedback from ground reaction forces and achieved in a feedforward manner.     

A cellular automaton model for tumour growth in inhomogeneous environment

Most of the existing mathematical models for tumour growth and tumour-induced angiogenesis neglect blood flow. This is an important factor on which both nutrient and metabolite supply depend. In this paper we aim to address this shortcoming by developing a mathematical model which shows how blood flow and red blood cell heterogeneity influence the growth of systems of normal and cancerous cells. The model is developed in two stages. First we determine the distribution of oxygen in a native vascular network, incorporating into our model features of blood flow and vascular dynamics such as structural adaptation, complex rheology and red blood cell circulation. Once we have calculated the oxygen distribution, we then study the dynamics of a colony of normal and cancerous cells, placed in such a heterogeneous environment. During this second stage, we assume that the vascular network does not evolve and is independent of the dynamics of the surrounding tissue. The cells are considered as elements of a cellular automaton, whose evolution rules are inspired by the different behaviour of normal and cancer cells. Our aim is to show that blood flow and red blood cell heterogeneity play major roles in the development of such colonies, even when the red blood cells are flowing through the vasculature of normal, healthy tissue.     

Designing criteria suites to identify discrete and networked sites of high value across manifestations of biodiversity

Suites of criteria specifying ecological, biological, social, economic, and governance properties enable the systematic identification of sites and networks of high biodiversity value, and can support balancing ecological and socioeconomic objectives of biodiversity conservation in terrestrial and marine spatial planning. We describe designs of suites of ecological, governance and socioeconomic criteria to comprehensively cover manifestations of biodiversity, from genotypes to biomes; compensate for taxonomic and spatial gaps in available datasets; balance biases resulting from conventionally-employed narrow criteria suites focusing on rare, endemic and threatened species; plan for climate change effects on biodiversity; and optimize the ecological and administrative networking of sites. Representativeness, replication, ecological connectivity, size, and refugia are identified as minimum ecological properties of site networks. Through inclusion of a criterion for phylogenetic distinctiveness, criteria suites identify sites important for maintaining evolutionary processes. Criteria for focal species are needed to overcome data gaps and address limitations in knowledge of factors responsible for maintaining ecosystem integrity.     

Adaptive data collection for intraindividual studies affected by adherence

Recently, the use of mobile technologies in ecological momentary assessments (EMAs) and interventions has made it easier to collect data suitable for intraindividual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subject. In this paper, we address this problem for a specific EMA setting that aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection that considers both the individual variability of the self-reported variable and of the adherence. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon signed rank test shows that the algorithm tends to have both a higher F₁ score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially in studies where data collection is impacted by adherence.     

基于CO2FIX模型的华北落叶松人工林碳循环过程

为了探明华北落叶松(Larix gmelinii var. principis-rupprechtii)人工林的碳循环过程, 该研究以河北围场地区的华北落叶松人工林为例, 基于CO2FIX模型, 以在当地的实际调查数据、文献数据作为输入数据, 从生物量、土壤和木质林产品碳库3个方面探讨了华北落叶松人工林的碳循环过程和碳汇能力。结果表明: 华北落叶松人工林土壤碳库最大, 生物量碳库次之, 林产品碳库最小, 但是林产品碳库随时间呈逐渐增加的趋势; 在一个轮伐期内(50年), 每公顷华北落叶松人工林约固定了250 t碳, 其中约70%通过凋落物和采伐剩余物的方式进入土壤碳库, 约30%进入木质林产品碳库; 华北落叶松人工林在生长的大部分时间是一个碳的吸收汇, 而在森林采伐时成为暂时的排放源, 从长时间尺度上看, 每公顷华北落叶松人工林每年大约固定0.3 t左右的碳。该研究结果表明了木质林产品碳库在人工林碳循环中的重要作用, 这将有助于更加全面地认识人工林的碳循环过程和碳汇能力。     

Carbon cycle of larch plantation based on CO2FIX model

Aims
Plantations play important roles in modifying regional carbon budget and maintaining regional carbon balance. In this study, we assessed larch plantation (Larix gmelinii var. principis-rupprechtii) carbon dynamics in Weichang County from a perspective of the forest biomass-soil-wood-products chain. Our objectives were to elucidate the carbon sink capacity of larch plantation and the influences of biomass, soil and wood product pools on carbon balance.
Methods
CO2FIX model was used to evaluate the carbon storage and flow of larch plantation over a time span of 120 years. Input data for model were derived from practical investigations and published papers. We validated the simulated results and found that this model was suitable in the region and the simulated results were reliable.
Important findings
(1) Soil was the largest carbon pool for larch plantation and the wood product pool had the smallest carbon storage. Meanwhile, carbon storage in wood products gradually increased with time. (2) In a rotation of 50 years from secondary poplar-birch forest to larch plantation, 250 t C·hm^-2 was sequestrated by the larch plantation. 70% of the carbon was transferred into soil in the form of litter and logging slash and the other 30% was transferred into wood products. (3) Larch plantation was a carbon sink during most of its growing period and turned to temporary carbon source when it was harvested. Larch plantation could sequestrate about 0.3 t C·hm^-2·a^-1 in the long term. Our results indicated the importance of wood product carbon pool in carbon dynamics of plantation, which facilitated our understanding in the carbon dynamics and capacity of plantation.     

Effects of Demographic Stochasticity on Population Persistence in Advective Media

Many populations live and disperse in advective media. A fundamental question, known as the “drift paradox” in stream ecology, is how a closed population can survive when it is constantly being transported downstream by the flow. Recent population-level models have focused on the role of diffusive movement in balancing the effects of advection, predicting critical conditions for persistence. Here, we formulate an individual-based stochastic analog of the model described in (Lutscher et al., SIAM Rev. 47(4):749–772, 2005) to quantify the effects of demographic stochasticity on persistence. Population dynamics are modeled as a logistic growth process and dispersal as a position-jump process on a finite domain divided into patches. When there is no correlation in the interpatch movement of residents, stochasticity simply smooths the persistence-extinction boundary. However, when individuals disperse in “packets” from one patch to another and the flow field is memoryless on the timescale of packet transport, the probability of persistence is greatly enhanced. The latter transport mechanism may be characteristic of larval dispersal in the coastal ocean or wind-dispersed seed pods.     

Polygenic variation maintained by balancing selection: pleiotropy, sex-dependent allelic effects and G x E interactions

We investigate three alternative selection-based scenarios proposed to maintain polygenic variation: pleiotropic balancing selection, G x E interactions (with spatial or temporal variation in allelic effects), and sex-dependent allelic effects. Each analysis assumes an additive polygenic trait with n diallelic loci under stabilizing selection. We allow loci to have different effects and consider equilibria at which the population mean departs from the stabilizing-selection optimum. Under weak selection, each model produces essentially identical, approximate allele-frequency dynamics. Variation is maintained under pleiotropic balancing selection only at loci for which the strength of balancing selection exceeds the effective strength of stabilizing selection. In addition, for all models, polymorphism requires that the population mean be close enough to the optimum that directional selection does not overwhelm balancing selection. This balance allows many simultaneously stable equilibria, and we explore their properties numerically. Both spatial and temporal G x E can maintain variation at loci for which the coefficient of variation (across environments) of the effect of a substitution exceeds a critical value greater than one. The critical value depends on the correlation between substitution effects at different loci. For large positive correlations (e.g., rho(ij)2>3/4), even extreme fluctuations in allelic effects cannot maintain variation. Surprisingly, this constraint on correlations implies that sex-dependent allelic effects cannot maintain polygenic variation. We present numerical results that support our analytical approximations and discuss our results in connection to relevant data and alternative variance-maintaining mechanisms.     

Upsetting the balance on sex selection

It is widely assumed that the strongest case for permitting non‐medical sex selection is where parents aim at family balance. This piece criticizes one representative attempt to justify sex selection for family balance. Kluge (2007) assumes that some couples may seek sex selection because they hold discriminatory values, but this need not impugn those who merely have preferences, without evaluative commitments, for a particular sex. This is disputed by those who see any sex selection as inherently sexist because it upholds stereotypes about the sexes. This article takes an alternative approach. I argue that, even if we accept that preference‐based selection is unobjectionable, a policy permitting selection for family balancing does a poor job of distinguishing between value‐based and preference‐based selection. If we wish to permit only preference‐based sex selection we should seek to identify parents’ motives. If we wish to justify a family balancing policy, other arguments are needed.     

Nonparametric adjustment techniques for binary covariates

Though a variety of reasons are often articulated for adjusting analyses for covariates, these reasons often fall into one of two general objectives, specifically to increase precision or to decrease bias. In practice, one does not generally choose between these objectives, because the methods that address one tend to address the other, as well. Because of this, no distinction is made in the methods used to correct for a baseline imbalance with respect to a prognostic covariate versus to ensure a fair comparison across treatment groups by making the comparisons within the levels of a prognostic covariate. Yet the literal translation of these two uses of covariate adjustment will lead to two distinct adjustment methods. We illustrate this divergence in the simplest case of a single binary covariate, a binary outcome, and two treatments, and we note that it is possible to combine the two approaches to derive yet a third approach. Each of these approaches is nonparametric and exact, and so it is the precise reason for adjusting that should dictate which would be used in any given situation.     

Mesoscopic Segregation of Excitation and Inhibition in a Brain Network Model

Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime. The topology of the network is heterogeneous, with a few of the nodes acting as connector hubs while the rest are relatively poorly connected. Our results show that in this type of mesoscopic network excitation and inhibition spontaneously segregate, with some columns acting mainly in an excitatory manner while some others have predominantly an inhibitory effect on their neighbors. We characterize the conditions under which this segregation arises, and relate the character of the different columns with their topological role within the network. In particular, we show that the connector hubs are preferentially inhibitory, the more so the larger the node''s connectivity. These results suggest a potential mesoscale organization of the excitation-inhibition balance in brain networks.     

A genome-wide comparison of the functional properties of rare and common genetic variants in humans

One of the longest running debates in evolutionary biology concerns the kind of genetic variation that is primarily responsible for phenotypic variation in species. Here, we address this question for humans specifically from the perspective of population allele frequency of variants across the complete genome, including both coding and noncoding regions. We establish simple criteria to assess the likelihood that variants are functional based on their genomic locations and then use whole-genome sequence data from 29 subjects of European origin to assess the relationship between the functional properties of variants and their population allele frequencies. We find that for all criteria used to assess the likelihood that a variant is functional, the rarer variants are significantly more likely to be functional than the more common variants. Strikingly, these patterns disappear when we focus on only those variants in which the major alleles are derived. These analyses indicate that the majority of the genetic variation in terms of phenotypic consequence may result from a mutation-selection balance, as opposed to balancing selection, and have direct relevance to the study of human disease.     

Extending eco-evolutionary theory with oligomorphic dynamics

Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. ‘Oligomorphic dynamics’ can be used to analyse eco-evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feedbacks, and the non-normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency- and density-dependent selection, on the dynamics of multi-modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco-evolutionary theory and empirical data.     

Single-molecule tracking of tau reveals fast kiss-and-hop interaction with microtubules in living neurons

The microtubule-associated phosphoprotein tau regulates microtubule dynamics and is involved in neurodegenerative diseases collectively called tauopathies. It is generally believed that the vast majority of tau molecules decorate axonal microtubules, thereby stabilizing them. However, it is an open question how tau can regulate microtubule dynamics without impeding microtubule-dependent transport and how tau is also available for interactions other than those with microtubules. Here we address this apparent paradox by fast single-molecule tracking of tau in living neurons and Monte Carlo simulations of tau dynamics. We find that tau dwells on a single microtubule for an unexpectedly short time of ∼40 ms before it hops to the next. This dwell time is 100-fold shorter than previously reported by ensemble measurements. Furthermore, we observed by quantitative imaging using fluorescence decay after photoactivation recordings of photoactivatable GFP–tagged tubulin that, despite this rapid dynamics, tau is capable of regulating the tubulin–microtubule balance. This indicates that tau''s dwell time on microtubules is sufficiently long to influence the lifetime of a tubulin subunit in a GTP cap. Our data imply a novel kiss-and-hop mechanism by which tau promotes neuronal microtubule assembly. The rapid kiss-and-hop interaction explains why tau, although binding to microtubules, does not interfere with axonal transport.