Instability to a heterogeneous oscillatory state in randomly connected recurrent networks with delayed interactions

Oscillatory dynamics are ubiquitous in biological networks. Possible sources of oscillations are well understood in low-dimensional systems but have not been fully explored in high-dimensional networks. Here we study large networks consisting of randomly coupled rate units. We identify a type of bifurcation in which a continuous part of the eigenvalue spectrum of the linear stability matrix crosses the instability line at nonzero frequency. This bifurcation occurs when the interactions are delayed and partially antisymmetric and leads to a heterogeneous oscillatory state in which oscillations are apparent in the activity of individual units but not on the population-average level

A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons

Inbreeding of Some Populations in the Genus Agrostis L.

This work presents some properties on produced inbred lines of the 5 best populations of 4 species of Agrostis for breeding purposes. The cultivars of forage grasses should meet a given aim for selection, which is the increasing dry matter and quality. The populations used in this research were the best ones for morphological and productive properties from a previous study. By the process of selfing there have been produced S1 offsprings which appeared to be quite superior in relation to open pollinated offspring from the mother plants. The obtained results were processed by multiple range regression analyses. According to the regression coefficient the inheritance of phenotype value of parental plants and the obtained offsprings after the selfing, and the properties like height of plant, tillering, number of vegetative and generative shoots, and dry matter yield for each plant has been determined. The selfed seed setting was quite satisfactory and it did not exppressed a very significant depression. Since the results relating to the inbred plants do not show inbreeding depression, process of selection and creating new cultivars should be continued for these species

Possibilities of more efficient usage of genetic potential of broilers breeders

During the last ten years, poultry production in the world and particularly, EU has been specific due to new trends, whereby special attention is focused on regulations and procedures that are adopted by European Union (EU), Food and Agriculture Organization of the United Nations (FAO), International Organization for Standardization (ISO) and the World Trade Organization. Legal regulations in EU (standards, directions) are focused on all species and categories of poultry. Based on this, breeding of broiler (meat-type line strains) and production of hatching eggs and day old chickens (incubation) as well as other issues are the focus of this review. This paper presented results of studies carried out using different technological procedures in the breeding of broiler and production of one day old chickens. These showed a full expression of genetic potential of strains, along with preservation of poultry welfare

A Geometrical Analysis of Global Stability in Trained Feedback Networks

Recurrent neural networks have been extensively studied in the context of neuroscience and machine learning due to their ability to implement complex computations. While substantial progress in designing effective learning algorithms has been achieved in the last years, a full understanding of trained recurrent networks is still lacking. Specifically, the mechanisms that allow computations to emerge from the underlying recurrent dynamics are largely unknown. Here we focus on a simple, yet underexplored computational setup: a feedback architecture trained to associate a stationary output to a stationary input. As a starting point, we derive an approximate analytical description of global dynamics in trained networks which assumes uncorrelated connectivity weights in the feedback and in the random bulk. The resulting mean-field theory suggests that the task admits several classes of solutions, which imply different stability properties. Different classes are characterized in terms of the geometrical arrangement of the readout with respect to the input vectors, defined in the high-dimensional space spanned by the network population. We find that such approximate theoretical approach can be used to understand how standard training techniques implement the input-output task in finite-size feedback networks. In particular, our simplified description captures the local and the global stability properties of the target solution, and thus predicts training performance

The interplay between randomness and structure during learning in RNNs

Recurrent neural networks (RNNs) trained on low-dimensional tasks have been widely used to model functional biological networks. However, the solutions found by learning and the effect of initial connectivity are not well understood. Here, we examine RNNs trained using gradient descent on different tasks inspired by the neuroscience literature. We find that the changes in recurrent connectivity can be described by low-rank matrices, despite the unconstrained nature of the learning algorithm. To identify the origin of the low-rank structure, we turn to an analytically tractable setting: training a linear RNN on a simplified task. We show how the low-dimensional task structure leads to low-rank changes to connectivity. This low-rank structure allows us to explain and quantify the phenomenon of accelerated learning in the presence of random initial connectivity. Altogether, our study opens a new perspective to understanding trained RNNs in terms of both the learning process and the resulting network structure

Stability of 1-D Excitons in Carbon Nanotubes under High Laser Excitations

Through ultrafast pump-probe spectroscopy with intense pump pulses and a wide continuum probe, we show that interband exciton peaks in single-walled carbon nanotubes (SWNTs) are extremely stable under high laser excitations. Estimates of the initial densities of excitons from the excitation conditions, combined with recent theoretical calculations of exciton Bohr radii for SWNTs, suggest that their positions do not change at all even near the Mott density. In addition, we found that the presence of lowest-subband excitons broadens all absorption peaks, including those in the second-subband range, which provides a consistent explanation for the complex spectral dependence of pump-probe signals reported for SWNTs.Comment: 4 pages, 4 figure

Influence of Energy and Protein Level in Lactating Sows Diet on the Mobilization of Reserves from Internal Organs

The aim of this paper was to investigate the effect of feeding sows in lactation, in diets with different levels of energy and protein to mobilization of reserves from internal organs. The experiment was conducted on 240 sows divided into two groups of 120 sows each, with two sub-groups of 60 sows. Experimental period lasted 65 days, and during the last 30 days of pregnancy and lactation, until weaning. For sows feeding a mixture of the standard composition and the quality, with the 15% crude protein, and 14 to 12 MJ ME / kg and 19% crude protein, and 14 to 12 MJ ME / kg of the mixture were used. Sows were divided into two groups and four subgroups consumed 2.20 and 3.30 kg of food per day. Based on the statistical analysis of the data it can be concluded that the different amounts of feeds with different levels of protein and energy in the diet resulted in the mobilization of the reserves from the internal organs of lactating sows. When we talk about the content of protein in the liver of lactating sows, statistically significant effect (P>0.05) to diets with 15% crude protein (19.04%) and 19% crude protein (19%) was not recorded. The same trend in the content of crude protein in the kidney (14.61 and 14.84%) was also observed, but with no statistically significant difference (P>0.05). Analysis of variance was established as statistically significant effects (P<0.01) protein content in the diet on protein content in the muscle of the heart muscle (17.59 and 18.71%). Effect of different energy level in mixture used for the diet of lactating sows, the protein content in the liver, kidneys and heart was missing

Design and Fabrication of a 1 m Model of the 70 mm Bore Twin Aperture Superconducting Quadrupole for the LHC Insertions

For reasons of geometrical acceptance, 70 mm bore twin aperture quadrupoles are required in the LHC insertions. For an operating gradient of 160 T/m at 4.5 K, a design based on a four layer coil wound from two graded 8.2 mm NbTi conductors has been developed. Three 1 m single aperture quadrupoles of this design have been built and successfully tested. Thereafter, the magnets have been disassembled and the coils re-collared using self-supporting collars. In this paper, we describe the design features of the twin aperture quadrupole, and report on the initial collaring tests and procedures for collaring and final assembly of the 1 m magnet