Response of Spiking Neurons to Correlated Inputs

The effect of a temporally correlated afferent current on the firing rate of a leaky integrate-and-fire (LIF) neuron is studied. This current is characterized in terms of rates, auto and cross-correlations, and correlation time scale $\tau_c$ of excitatory and inhibitory inputs. The output rate $\nu_{out}$ is calculated in the Fokker-Planck (FP) formalism in the limit of both small and large $\tau_c$ compared to the membrane time constant $\tau$ of the neuron. By simulations we check the analytical results, provide an interpolation valid for all $\tau_c$ and study the neuron's response to rapid changes in the correlation magnitude.Comment: 4 pages, 3 figure

The Complexity of Reasoning for Fragments of Default Logic

Default logic was introduced by Reiter in 1980. In 1992, Gottlob classified the complexity of the extension existence problem for propositional default logic as \SigmaPtwo-complete, and the complexity of the credulous and skeptical reasoning problem as SigmaP2-complete, resp. PiP2-complete. Additionally, he investigated restrictions on the default rules, i.e., semi-normal default rules. Selman made in 1992 a similar approach with disjunction-free and unary default rules. In this paper we systematically restrict the set of allowed propositional connectives. We give a complete complexity classification for all sets of Boolean functions in the meaning of Post's lattice for all three common decision problems for propositional default logic. We show that the complexity is a hexachotomy (SigmaP2-, DeltaP2-, NP-, P-, NL-complete, trivial) for the extension existence problem, while for the credulous and skeptical reasoning problem we obtain similar classifications without trivial cases.Comment: Corrected versio

Rachis brittleness in a hybrid–parent barley (Hordeum vulgare) breeding germplasm with different combinations at the non‐brittle rachis genes

Two dominant, closely linked and complementary genes, Btr1 and Btr2, control rachis brittleness in barley. Recessive mutations in any of these genes turn the fragile rachis (brittle) into a tough rachis phenotype (non‐brittle). The cross of parents with alternative mutations in the btr genes leads to a brittle F1 hybrid that presents grain retention problems. We evaluated rachis fragility through a mechanical test and under natural conditions, in F1 crosses with different compositions at the btr genes. Brittleness was significantly higher in Btr1btr1Btr2btr2 crosses compared to hybrids and inbred parents carrying one of the mutations (btr1btr1Btr2Btr2/Btr1Btr1btr2btr2). This fact could jeopardize the efficient harvest of hybrids bearing alternative mutations, reducing the choice of possible crosses for hybrid barley breeding and hindering the exploitation of potential heterotic patterns. Furthermore, non‐brittle hybrids showed higher brittleness than inbreds, suggesting the presence of other dominant factors affecting the trait. In conclusion, this work encourages a deeper study of the genetic control of the rachis brittleness trait and urges the consideration of rachis tenacity as a target for hybrid barley breeding.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness grants RFP2015 00006‐00‐00, and RTA2012‐00033‐C03‐02, and by the contract “Iberia region hybrid barley variety development and understanding effects of adaptation genes in hybrids,” between CSIC and Syngenta Crop Protection AG, which included funding for MFC PhD scholarship

An early Little Ice Age brackish water invasion along the south coast of the Caspian Sea (sediment of Langarud wetland) and its wider impacts on environment and people

Caspian Sea level has undergone significant changes through time with major impacts not only on the surrounding coasts, but also offshore. This study reports a brackish water invasion on the southern coast of the Caspian Sea constructed from a multi-proxy analysis of sediment retrieved from the Langarud wetland. The ground surface level of wetland is >6 m higher than the current Caspian Sea level (at -27.41 m in 2014) and located >11 km far from the coast. A sequence covering the last millennium was dated by three radiocarbon dates. The results from this new study suggest that Caspian Sea level rose up to at least -21.44 m (i.e. >6 m above the present water level) during the early Little Ice Age. Although previous studies in the southern coast of the Caspian Sea have detected a high-stand during the Little Ice Age period, this study presents the first evidence that this high-stand reached so far inland and at such a high altitude. Moreover, it confirms one of the very few earlier estimates of a high-stand at -21 m for the second half of the 14th century. The effects of this large-scale brackish water invasion on soil properties would have caused severe disruption to regional agriculture, thereby destabilizing local dynasties and facilitating a rapid Turko-Mongol expansion of Tamerlane’s armies from the east.N Ghasemi (INIOAS), V Jahani (Gilan Province Cultural Heritage and Tourism Organisation) and A Naqinezhad (University of Mazandaran), INQUA QuickLakeH project (no. 1227) and to the European project Marie Curie, CLIMSEAS-PIRSES-GA-2009-24751

Domestication as innovation : the entanglement of techniques, technology and chance in the domestication of cereal crops

The origins of agriculture involved pathways of domestication in which human behaviours and plant genetic adaptations were entangled. These changes resulted in consequences that were unintended at the start of the process. This paper highlights some of the key innovations in human behaviours, such as soil preparation, harvesting and threshing, and how these were coupled with genetic ‘innovations’ within plant populations. We identify a number of ‘traps’ for early cultivators, including the needs for extra labour expenditure on crop-processing and soil fertility maintenance, but also linked gains in terms of potential crop yields. Compilations of quantitative data across a few different crops for the traits of nonshattering and seed size are discussed in terms of the apparently slow process of domestication, and parallels and differences between different regional pathways are identified. We highlight the need to bridge the gap between a Neolithic archaeobotanical focus on domestication and a focus of later periods on crop-processing activities and labour organization. In addition, archaeobotanical data provide a basis for rethinking previous assumptions about how plant genetic data should be related to the origins of agriculture and we contrast two alternative hypotheses: gradual evolution with low selection pressure versus metastable equilibrium that prolonged the persistence of ‘semi-domesticated’ populations. Our revised understanding of the innovations involved in plant domestication highlight the need for new approaches to collecting, modelling and integrating genetic data and archaeobotanical evidence

Status and Potential of Single-cell Transcriptomics for Understanding Plant Development and Functional Biology

Funding Information University of Western Australia Acknowledgments The authors would like to extend sincere thanks to Robert Salomon for inspiring to write this manuscript. Resources were provided by The University of Western Australia.Peer reviewedPostprin

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Weak pairwise correlations imply strongly correlated network states in a neural population

Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah (http://cosyne.org

Effects of growing conditions and source habitat on plant traits and functional group definition

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72499/1/j.1365-2435.2001.00487.x.pd