Darwin and Huxley revisited: the origin of allometry

The relative sizes of parts of an organism frequently depend on the absolute size of the individual, a relationship that is generally described by power laws. I show here that these power laws are a consequence of the way evolution operates

Generalized coupled wake boundary layer model: applications and comparisons with field and LES data for two wind-farms

We describe a generalization of the Coupled Wake Boundary Layer (CWBL) model for wind-farms that can be used to evaluate the performance of wind-farms under arbitrary wind inflow directions whereas the original CWBL model (Stevens et al., J. Renewable and Sustainable Energy 7, 023115 (2015)) focused on aligned or staggered wind-farms. The generalized CWBL approach combines an analytical Jensen wake model with a "top-down" boundary layer model coupled through an iterative determination of the wake expansion coefficient and an effective wake coverage area for which the velocity at hub-height obtained using both models converges in the "deep-array" portion (fully developed region) of the wind-farm. The approach accounts for the effect of the wind direction by enforcing the coupling for each wind direction. Here we present detailed comparisons of model predictions with LES results and field measurements for the Horns Rev and Nysted wind-farms operating over a wide range of wind inflow directions. Our results demonstrate that two-way coupling between the Jensen wake model and a "top-down" model enables the generalized CWBL model to predict the "deep-array" performance of a wind-farm better than the Jensen wake model alone. The results also show that the new generalization allows us to study a much larger class of wind-farms than the original CWBL model, which increases the utility of the approach for wind-farm designers.Comment: 17 pages, 11 figure

Augmentation Is a Potentiation of the Exocytotic Process

AbstractShort-term synaptic enhancement is caused by an increase in the probability with which synaptic terminals release transmitter in response to presynaptic action potentials. Since exocytosed vesicles are drawn from a readily releasable pool of packaged transmitter, enhancement must result either from an increase in the size of the pool or an elevation in the fraction of releasable vesicles that undergoes exocytosis with each action potential. We show here that at least one major component of enhancement, augmentation, is not caused by an increase in the size of the readily releasable pool but is instead associated with an increase in the efficiency with which action potentials induce the exocytosis of readily releasable vesicles

Heterogeneity of Release Probability, Facilitation, and Depletion at Central Synapses

AbstractPrevious studies of short-term plasticity in central nervous systems synapses have largely focused on average synaptic properties. In this study, we use recordings from putative single synaptic release sites in hippocampal slices to show that significant heterogeneity exists in facilitation and depletion among synapses. In particular, the amount of paired-pulse facilitation is inversely related to the initial release probability of the synapse. We also examined depletion at individual synapses using high frequency stimulation, and estimated the size of the readily releasable vesicle pool, which averaged 5.0 ± 3.0 quanta (n = 13 synapses). In addition, these experiments demonstrate that the release probability at a synapse is directly correlated with the size of its readily releasable vesicle pool

Excitatory and Feed-Forward Inhibitory Hippocampal Synapses Work Synergistically as an Adaptive Filter of Natural Spike Trains

Short-term synaptic plasticity (STP) is an important mechanism for modifying neural circuits during computation. Although STP is much studied, its role in the processing of complex natural spike patterns is unknown. Here we analyze the responses of excitatory and inhibitory hippocampal synapses to natural spike trains at near-physiological temperatures. Our results show that excitatory and inhibitory synapses express complementary sets of STP components that selectively change synaptic strength during epochs of high-frequency discharge associated with hippocampal place fields. In both types of synapses, synaptic strength rapidly alternates between a near-constant level during low activity and another near-constant, but elevated (for excitatory synapses) or reduced (for inhibitory synapses) level during high-frequency epochs. These history-dependent changes in synaptic strength are largely independent of the particular temporal pattern within the discharges, and occur concomitantly in the two types of synapses. When excitatory and feed-forward inhibitory synapses are co-activated within the hippocampal feed-forward circuit unit, the net effect of their complementary STP is an additional increase in the gain of excitatory synapses during high-frequency discharges via selective disinhibition. Thus, excitatory and feed-forward inhibitory hippocampal synapses in vitro act synergistically as an adaptive filter that operates in a switch-like manner and is selective for high-frequency epochs

A Universal Property of Axonal and Dendritic Arbors

SummaryAxonal and dendritic arbors can be characterized statistically by their spatial density function, a function that specifies the probability of finding a branch of a particular arbor at each point in a neural circuit. Based on an analysis of over a thousand arbors from many neuron types in various species, we have discovered an unexpected simplicity in arbor structure: all of the arbors we have examined, both axonal and dendritic, can be described by a Gaussian density function truncated at about two standard deviations. Because all arbors are characterized by density functions with this single functional form, only four parameters are required to specify an arbor's size and shape: the total length of its branches and the standard deviations of the Gaussian in three orthogonal directions. This simplicity in arbor structure can have implications for the developmental wiring of neural circuits

Using the coupled wake boundary layer model to evaluate the effect of turbulence intensity on wind farm performance

We use the recently introduced coupled wake boundary layer (CWBL) model to predict the e ect of turbulence intensity on the performance of a wind farm. The CWBL model combines a standard wake model with a \top-down" approach to get improved predictions for the power output compared to a stand-alone wake model. Here we compare the CWBL model results for di erent turbulence intensities with the Horns Rev eld measurements by Hansen et al., Wind Energy 15, 183196 (2012). We show that the main trends as function of the turbulence intensity are captured very well by the model and discuss di erences between the eld measurements and model results based on comparisons with LES results from Wu and Port e-Agel, Renewable Energy 75, 945-955 (2015)

Effect of turbine alignment on the average power output of wind-farms

Using Large Eddy Simulation (LES), we investigate the influence of the alignment of successive turbine rows on the average power output of a finite length wind-farm with a stream-wise spacing between the turbines of Sx = 7:85D and a span-wise spacing of Sy = 5:23D, where D is the turbine diameter. Different turbine alignments affect the extent to which wakes from upstream turbines interact with downstream turbines. We consider 13 turbine rows in the stream-wise direction and change the layout of the wind-farm by adjusting the angle y = arctan Sdy Sx with respect to the incoming flow direction, where Sdy indicates the span-wise offset from one turbine row to the next. For the case considered here, y = 0 degrees corresponds to an aligned windfarm, while a perfectly staggered configuration occurs at y =arctan[(5:23D=2)=7:85D]=18:43 degrees. We simulate the interaction between each wind-farm and the atmospheric boundary layer using a LES that uses a newly developed concurrent-precursor inflow method. For an aligned configuration we observe a nearly constant average turbine power output for the second and subsequent turbine rows, which is about 60% of the average power produced by the turbines in the first row. With increasing y the power loss in subsequent turbine rows is more gradual. We find that the highest average power output is not obtained for a staggered wind-farm (y = 18:43 degrees), but for an intermediate alignment of around y = 12 degrees. Such an intermediate alignment allows more turbines to be outside the wake of upstream turbines than in the staggered configuration in which turbines are directly in the wake of turbines placed two rows upstream