Amplification of asynchronous inhibition-mediated synchronization by feedback in recurrent networks

Synchronization of 30-80 Hz oscillatory activity of the principle neurons in the olfactory bulb (mitral cells) is believed to be important for odor discrimination. Previous theoretical studies of these fast rhythms in other brain areas have proposed that principle neuron synchrony can be mediated by short-latency, rapidly decaying inhibition. This phasic inhibition provides a narrow time window for the principle neurons to fire, thus promoting synchrony. However, in the olfactory bulb, the inhibitory granule cells produce long lasting, small amplitude, asynchronous and aperiodic inhibitory input and thus the narrow time window that is required to synchronize spiking does not exist. Instead, it has been suggested that correlated output of the granule cells could serve to synchronize uncoupled mitral cells through a mechanism called "stochastic synchronization", wherein the synchronization arises through correlation of inputs to two neural oscillators. Almost all work on synchrony due to correlations presumes that the correlation is imposed and fixed. Building on theory and experiments that we and others have developed, we show that increased synchrony in the mitral cells could produce an increase in granule cell activity for those granule cells that share a synchronous group of mitral cells. Common granule cell input increases the input correlation to the mitral cells and hence their synchrony by providing a positive feedback loop in correlation. Thus we demonstrate the emergence and temporal evolution of input correlation in recurrent networks with feedback. We explore several theoretical models of this idea, ranging from spiking models to an analytically tractable model. © 2010 Marella, Ermentrout

Gamma Oscillations in a Nonlinear Regime: A Minimal Model Approach Using Heterogeneous Integrate-and-Fire Networks

Fast oscillations and in particular gamma-band oscillation (20-80 Hz) are commonly observed during brain function and are at the center of several neural processing theories. In many cases, mathematical analysis of fast oscillations in neural networks has been focused on the transition between irregular and oscillatory firing viewed as an instability of the asynchronous activity. But in fact, brain slice experiments as well as detailed simulations of biological neural networks have produced a large corpus of results concerning the properties of fully developed oscillations that are far from this transition point. We propose here a mathematical approach to deal with nonlinear oscillations in a network of heterogeneous or noisy integrate-and-fire neurons connected by strong inhibition. This approach involves limited mathematical complexity and gives a good sense of the oscillation mechanism, making it an interesting tool to understand fast rhythmic activity in simulated or biological neural networks. A surprising result of our approach is that under some conditions, a change of the strength of inhibition only weakly influences the period of the oscillation. This is in contrast to standard theoretical and experimental models of interneuron network gamma oscillations (ING), where frequency tightly depends on inhibition strength, but it is similar to observations made in some in vitro preparations in the hippocampus and the olfactory bulb and in some detailed network models. This result is explained by the phenomenon of suppression that is known to occur in strongly coupled oscillating inhibitory networks but had not yet been related to the behavior of oscillation frequency

Carbon allocation to major metabolites in illuminated leaves is not just proportional to photosynthesis when gaseous conditions (CO2 and O2) vary

In gas-exchange experiments, manipulating CO2 and O2 is commonly used to change the balance between carboxylation and oxygenation. Downstream metabolism (utilization of photosynthetic and photorespiratory products) may also be affected by gaseous conditions but this is not well documented. Here, we took advantage of sunflower as a model species, which accumulates chlorogenate in addition to sugars and amino acids (glutamate, alanine, glycine and serine). We performed isotopic labelling with 13CO2 under different CO2/O2 conditions, and determined 13C contents to compute 13C-allocation patterns and build-up rates. The 13C content in major metabolites was not found to be a constant proportion of net fixed carbon but, rather, changed dramatically with CO2 and O2. Alanine typically accumulated at low O2 (hypoxic response) while photorespiratory intermediates accumulated under ambient conditions and at high photorespiration, glycerate accumulation exceeding serine and glycine build-up. Chlorogenate synthesis was relatively more important under normal conditions and at high CO2 and its synthesis was driven by phosphoenolpyruvate de novo synthesis. These findings demonstrate that carbon allocation to metabolites other than photosynthetic end products is affected by gaseous conditions and therefore the photosynthetic yield of net nitrogen assimilation varies, being minimal at high CO2 and maximal at high O2.We thank the Australian Research Council for its support via a Future Fellowship awarded to G.T. under contract FT140100645

Spike-timing prediction in cortical neurons with active dendrites

A complete single-neuron model must correctly reproduce the firing of spikes and bursts. We present a study of a simplified model of deep pyramidal cells of the cortex with active dendrites. We hypothesized that we can model the soma and its apical dendrite with only two compartments, without significant loss in the accuracy of spike-timing predictions. The model is based on experimentally measurable impulse-response functions, which transfer the effect of current injected in one compartment to current reaching the other. Each compartment was modeled with a pair of non-linear differential equations and a small number of parameters that approximate the Hodgkin-and-Huxley equations. The predictive power of this model was tested on electrophysiological experiments where noisy current was injected in both the soma and the apical dendrite simultaneously. We conclude that a simple two-compartment model can predict spike times of pyramidal cells stimulated in the soma and dendrites simultaneously. Our results support that regenerating activity in the apical dendritic is required to properly account for the dynamics of layer 5 pyramidal cells under in-vivo-like conditions

Effect of cationic chemical disorder on defect formation energies in uranium-plutonium mixed oxides

At the atomic scale, uranium-plutonium mixed oxides (U,Pu)O_2 are characterized by cationic chemical disorder, which entails that U and Pu cations are randomly distributed on the cation sublattice. In the present work, we study the impact of disorder on point-defect formation energies in (U,Pu)O_2 using interatomic-potential and Density Functional Theory (DFT+U) calculations. We focus on bound Schottky defects (BSD) that are among the most stable defects in these oxides. As a first step, we estimate the distance R_D around the BSD up to which the local chemical environment significantly affects their formation energy. To this end, we propose an original procedure in which the formation energy is computed for several supercells at varying levels of disorder. We conclude that the first three cation shells around the BSD have a non-negligible influence on their formation energy (R_{D} = 7.0 \{AA}). We apply then a systematic approach to compute the BSD formation energies for all the possible cation configurations on the first and second nearest neighbor shells around the BSD. We show that the formation energy can range in an interval of 0.97 eV, depending on the relative amount of U and Pu neighboring cations. Based on these results, we propose an interaction model that describes the effect of nominal and local composition on the BSD formation energy. Finally, the DFT+U benchmark calculations show a satisfactory agreement for configurations characterized by a U-rich local environment, and a larger mismatch in the case of a Pu-rich one. In summary, this work provides valuable insights on the properties of BSD defects in (U,Pu)O_2, and can represent a valid strategy to study point defect properties in disordered compounds.Comment: 33 pages, 20 figure

Fluid-structure interaction study of spider's hair flow-sensing system

In the present work we study the spider's hair flow-sensing system by using fluid-structure interaction (FSI) numerical simulations. We observe experimentally the morphology of Theraphosa stirmi's hairs and characterize their mechanical properties through nanotensile tests. We then use the obtained information as input for the computational model. We study the effect of a varying air velocity and a varying hair spacing on the mechanical stresses and displacements. Our results can be of interest for the design of novel bio-inspired systems and structures for smart sensors and robotics