The differing magnitude distributions of the two Jupiter Trojan color populations

The Jupiter Trojans are a significant population of minor bodies in the middle Solar System that have garnered substantial interest in recent years. Several spectroscopic studies of these objects have revealed notable bimodalities with respect to near-infrared spectra, infrared albedo, and color, which suggest the existence of two distinct groups among the Trojan population. In this paper, we analyze the magnitude distributions of these two groups, which we refer to as the red and less red color populations. By compiling spectral and photometric data from several previous works, we show that the observed bimodalities are self-consistent and categorize 221 of the 842 Trojans with absolute magnitudes in the range H<12.3 into the two color populations. We demonstrate that the magnitude distributions of the two color populations are distinct to a high confidence level (>95%) and fit them individually to a broken power law, with special attention given to evaluating and correcting for incompleteness in the Trojan catalog as well as incompleteness in our categorization of objects. A comparison of the best-fit curves shows that the faint-end power-law slopes are markedly different for the two color populations, which indicates that the red and less red Trojans likely formed in different locations. We propose a few hypotheses for the origin and evolution of the Trojan population based on the analyzed data.Comment: Published in AJ; 26 pages, 7 figure

A comparison of spike time prediction and receptive field mapping with point process generalized linear models, Wiener-Voltera kernels, and spike-triggered averaging methods

Poster presentation: Characterizing neuronal encoding is essential for understanding information processing in the brain. Three methods are commonly used to characterize the relationship between neural spiking activity and the features of putative stimuli. These methods include: Wiener-Volterra kernel methods (WVK), the spike-triggered average (STA), and more recently, the point process generalized linear model (GLM). We compared the performance of these three approaches in estimating receptive field properties and orientation tuning of 251 V1 neurons recorded from 2 monkeys during a fixation period in response to a moving bar. The GLM consisted of two formulations of the conditional intensity function for a point process characterization of the spiking activity: one with a stimulus only component and one with the stimulus and spike history. We fit the GLMs by maximum likelihood using GLMfit in Matlab. Goodness-of-fit was assessed using cross-validation with Kolmogorov-Smirnov (KS) tests based on the time-rescaling theorem to evaluate the accuracy with which each model predicts the spiking activity of individual neurons and for each movement direction (4016 models in total, for 251 neurons and 16 different directions). The GLMs that considered spike history of up to 35 ms, accurately predicted neuronal spiking activity (95% confidence intervals for KS test) with a performance of 97.0% (3895/4016) for the training data, and 96.5% (3876/4016) for the test data. If spike history was not considered, performance dropped to 73,1% in the training and 71.3% in the testing data. In contrast, the WVF and the STA predicted spiking accurately for 24.2% and 44.5% of the test data examples respectively. The receptive field size estimates obtained from the GLM (with and without history), WVF and STA were comparable. Relative to the GLM orientation tuning was underestimated on average by a factor of 0.45 by the WVF and the STA. The main reason for using the STA and WVF approaches is their apparent simplicity. However, our analyses suggest that more accurate spike prediction as well as more credible estimates of receptive field size and orientation tuning can be computed easily using GLMs implemented in Matlab with standard functions such as GLMfit

Controversy in statistical analysis of functional magnetic resonance imaging data

To test the validity of statistical methods for fMRI data analysis, Eklund et al. (1) used, for the first time, large-scale experimental data rather than simulated data. Using resting-state fMRI measurements to represent a null hypothesis of no task-induced activation, the authors compare familywise error rates for voxel-based and cluster-based inferences for both parametric and nonparametric methods. Eklund et al.’s study used three fMRI statistical analysis packages. They found that, for a target familywise error rate of 5%, the parametric methods gave invalid cluster-based inferences and conservative voxel-based inferences

The effect of different spectro-temporal representations of an input auditory stimulus on the fitting of a point process model of auditory neurons

We compare the effect of the use of three different spectro-temporal representations of an input auditory stimulus on the fitting of a point process model of auditory neuron firing. The three spectro-temporal representations considered are the spectrogram, a gammatone filterbank and the Hilbert spectrum. We firstly investigate how the model fits the recorded neuronal data when using either one of the three representations and secondly how well do the estimated parameters of the model correspond to their experimentally measured counterparts. It is observed that all three representations yield a model that fits well the recorded data. However, the characteristic frequencies obtained with the spectro-temporal parameters of the model using the gammatone filterbank corresponds better to the experimentally measured characteristic frequency than the characteristic frequency obtained with the models using the other two spectro-temporal representations. Therefore, it is concluded that the quality of the fitted parameters can be affected by the choice of the spectro-temporal representation and that, as could have been expected, the gammatone filterbank seems to more accurately extract the relevant spectro-temporal characteristics of the input auditory stimulus.Fonds quebecois de la recherche sur la nature et les technologiesNational Institutes of Health (U.S.) (Grant DP1-OD003646

Kinetics of acute hepatitis B virus infection in humans

Using patient data from a unique single source outbreak of hepatitis B virus (HBV) infection, we have characterized the kinetics of acute HBV infection by monitoring viral turnover in the serum during the late incubation and clinical phases of the disease in humans. HBV replicates rapidly with minimally estimated doubling times ranging between 2.2 and 5.8 d (mean 3.7 ± 1.5 d). After a peak viral load in serum of nearly 1010 HBV DNA copies/ml is attained, clearance of HBV DNA follows a two or three phase decay pattern with an initial rapid decline characterized by mean half-life (t1/2) of 3.7 ± 1.2 d, similar to the t1/2 observed in the noncytolytic clearance of covalently closed circular DNA for other hepadnaviruses. The final phase of virion clearance occurs at a variable rate (t1/2 of 4.8 to 284 d) and may relate to the rate of loss of infected hepatocytes. Free virus has a mean t1/2 of at most 1.2 ± 0.6 d. We estimate a peak HBV production rate of at least 1013 virions/day and a maximum production rate of an infected hepatocyte of 200–1,000 virions/day, on average. At this peak rate of virion production we estimate that every possible single and most double mutations would be created each day

Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models

Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) models cannot represent simultaneous occurrences of spike events at an arbitrarily small time resolution. Solo recently reported a simultaneous-event multivariate point process (SEMPP) model to correct this key limitation. In this paper, we show how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM). Unlike existing approximate procedures for fitting the discrete-time SEMPP model, the mGLM is an exact algorithm. The MPP time-rescaling theorem can be used to assess model goodness-of-fit. We also derive a new marked point-process (MkPP) representation of the SEMPP model that leads to new thinning and time-rescaling algorithms for simulating an SEMPP stochastic process. These algorithms are much simpler than multivariate extensions of algorithms for simulating a univariate point process, and could not be arrived at without the MkPP representation. We illustrate the versatility of the SEMPP model by analyzing neural spiking activity from pairs of simultaneously-recorded rat thalamic neurons stimulated by periodic whisker deflections, and by simulating SEMPP data. In the data analysis example, the SEMPP model demonstrates that whisker motion significantly modulates simultaneous spiking activity at the 1 ms time scale and that the stimulus effect is more than one order of magnitude greater for simultaneous activity compared with non-simultaneous activity. Together, the mGLM, the MPP time-rescaling theorem and the MkPP representation of the SEMPP model offer a theoretically sound, practical tool for measuring joint spiking propensity in a neuronal ensemble

Reply to Grace: Role of cholinergic neurons in rapid eye movement (REM) sleep control

We thank Grace (1) for the opportunity to discuss the role of cholinergic neurons in rapid eye movement (REM) sleep further. Grace suggests that optogenetic activation of a population of neurons does not necessarily demonstrate their role in the endogenous system when interrogating complex neural circuitry. We agree that we do not prove necessity of cholinergic neurons in REM sleep generation, as we point out in our discussion, “Future studies that selectively inhibit cholinergic neurons in the PPT [pedunculopontine tegmentum] and LDT [laterodorsal tegmentum] of nonhypercholinergic mice are needed to determine if cholinergic neurons are necessary for REM sleep generation” (2). However, in our report we do demonstrate the sufficiency of PPT/LDT neurons to influence REM sleep initiation but not influence REM sleep duration, thus distinguishing the role of cholinergic neurons on these properties of REM sleep. In addition, activation of cholinergic PPT neurons during non-REM sleep induced REM sleep versus wakefulness. Our data are consistent with the role of cholinergic neurons in generating an activated brain state and many studies pointing to the role of cholinergic neurons in REM sleep regulation (reviewed in ref. 3)

A Point Process Model for Auditory Neurons Considering Both Their Intrinsic Dynamics and the Spectrotemporal Properties of an Extrinsic Signal

We propose a point process model of spiking activity from auditory neurons. The model takes account of the neuron's intrinsic dynamics as well as the spectrotemporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function. The Volterra expansion models the neuron's baseline spike rate, its intrinsic dynamics-spiking history-and the stimulus effect which in this case is the analog of the spectrotemporal receptive field (STRF). We performed the model fitting efficiently in a generalized linear model framework using ridge regression to address properly this ill-posed maximum likelihood estimation problem. The model provides an excellent fit to spiking activity from 55 auditory nerve neurons. The STRF-like representation estimated jointly with the neuron's intrinsic dynamics may offer more accurate characterizations of neural activity in the auditory system than current ones based solely on the STRF