A common goodness-of-fit framework for neural population models using marked point process time-rescaling

A critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem and assess model quality using rescaled spike times. Recently, there has been increasing interest in statistical models that describe the simultaneous spiking activity of neuron populations, either in a single brain region or across brain regions. Classically, such models have used spike sorted data to describe relationships between the identified neurons, but more recently clusterless modeling methods have been used to describe population activity using a single model. Here we develop a generalization of the time-rescaling theorem that enables comprehensive goodness-of-fit analysis for either of these classes of population models. We use the theory of marked point processes to model population spiking activity, and show that under the correct model, each spike can be rescaled individually to generate a uniformly distributed set of events in time and the space of spike marks. After rescaling, multiple well-established goodness-of-fit procedures and statistical tests are available. We demonstrate the application of these methods both to simulated data and real population spiking in rat hippocampus. We have made the MATLAB and Python code used for the analyses in this paper publicly available through our Github repository at https://github.com/Eden-Kramer-Lab/popTRT.This work was supported by grants from the NIH (MH105174, NS094288) and the Simons Foundation (542971). (MH105174 - NIH; NS094288 - NIH; 542971 - Simons Foundation)Published versio

Decaying axinolike dark matter: Discriminative solution to small-scale issues

The latest Lyman-$\alpha$ forest data severely constrain the conventional warm dark matter solution to small-scale issues in the cold dark matter paradigm. It has been also reported that unconstrained astrophysical processes may address the issues. In response to this situation, we revisit the decaying dark matter solution to the issues, discussing possible signatures to discriminate decaying dark matter from astrophysical processes as a solution to small-scale issues. We consider an axinolike particle (ALPino) decaying into an axionlike particle (ALP) and gravitino with the lifetime around the age of the Universe. The ALPino mass is sub-PeV and slightly ($\Delta m/m\sim 10^{-4}$) larger than the gravitino mass, and thus the dark matter abundance does not alter virtually after the ALPino decays. On the other hand, the gravitino produced from the ALPino decay obtains a kick velocity of $\sim 30 \,{\rm km / s}$, which is sufficiently larger than a circular velocity of dwarf galaxies to impact their dark matter distributions. The Lyman-$\alpha$ forest constraints are relieved since only a small fraction ($\sim10$%) of dark matter experiences the decay at that time. Decaying dark matter is thus promoted to a viable solution to small-scale issues. The ALPino relic abundance is determined predominantly by the decay of the lightest ordinary supersymmetric particle. The monochromatic ALP emission from the ALPino decay is converted to $\sim 50 \,{\rm GeV}$ photon under the Galactic magnetic field. The morphology of the gamma-ray flux shows a distinctive feature of the model when compared to decaying dark matter that directly decays into photons. Once detected, such distinctive signals discriminate the decaying dark matter solution to small-scale issues from unconstrained astrophysical processes.Comment: 6 pages, 3 figures; discussions improved, version accepted in PR

A Bayesian Method for Detecting and Characterizing Allelic Heterogeneity and Boosting Signals in Genome-Wide Association Studies

The standard paradigm for the analysis of genome-wide association studies involves carrying out association tests at both typed and imputed SNPs. These methods will not be optimal for detecting the signal of association at SNPs that are not currently known or in regions where allelic heterogeneity occurs. We propose a novel association test, complementary to the SNP-based approaches, that attempts to extract further signals of association by explicitly modeling and estimating both unknown SNPs and allelic heterogeneity at a locus. At each site we estimate the genealogy of the case-control sample by taking advantage of the HapMap haplotypes across the genome. Allelic heterogeneity is modeled by allowing more than one mutation on the branches of the genealogy. Our use of Bayesian methods allows us to assess directly the evidence for a causative SNP not well correlated with known SNPs and for allelic heterogeneity at each locus. Using simulated data and real data from the WTCCC project, we show that our method (i) produces a significant boost in signal and accurately identifies the form of the allelic heterogeneity in regions where it is known to exist, (ii) can suggest new signals that are not found by testing typed or imputed SNPs and (iii) can provide more accurate estimates of effect sizes in regions of association.Comment: Published in at http://dx.doi.org/10.1214/09-STS311 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Knowledge-based machine vision systems for space station automation

Computer vision techniques which have the potential for use on the space station and related applications are assessed. A knowledge-based vision system (expert vision system) and the development of a demonstration system for it are described. This system implements some of the capabilities that would be necessary in a machine vision system for the robot arm of the laboratory module in the space station. A Perceptics 9200e image processor, on a host VAXstation, was used to develop the demonstration system. In order to use realistic test images, photographs of actual space shuttle simulator panels were used. The system's capabilities of scene identification and scene matching are discussed

Increased motor cortex excitability for concealed visual information

Deceptive behaviour involves complex neural processes involving the primary motor cortex. The dynamics of this motor cortex excitability prior to lying are still not well understood. We sought to examine whether corticospinal excitability can be used to suggest the presence of deliberately concealed information in a modified version of the Guilty Knowledge Test (GKT). Participants pressed keys to either truthfully or deceitfully indicate their familiarity with a series of faces. Motor-evoked-potentials (MEPs) were recorded during response preparation to measure muscle-specific neural excitability. We hypothesised that MEPs would increase during the deceptive condition not only in the lie-telling finger but also in the suppressed truth-telling finger. We report a group-level increase in overall corticospinal excitability 300 ms following stimulus onset during the deceptive condition, without specific activation of the neural representation of the truth-telling finger. We discuss cognitive processes, particularly response conflict and/or automated responses to familiar stimuli, which may drive the observed non-specific increase of motor excitability in deception

Abundance of intrinsic disorder in SV-IV, a multifunctional androgen-dependent protein secreted from rat seminal vesicle

The potent immunomodulatory, anti-inflammatory and procoagulant properties of the
protein no. 4 secreted from the rat seminal vesicle epithelium (SV-IV) have been
previously found to be modulated by a supramolecular monomer-trimer equilibrium.
More structural details that integrate experimental data into a predictive framework
have recently been reported. Unfortunately, homology modelling and fold-recognition
strategies were not successful in creating a theoretical model of the structural
organization of SV-IV. It was inferred that the global structure of SV-IV is not similar
to any protein of known three-dimensional structure. Reversing the classical approach
to the sequence-structure-function paradigm, in this paper we report on novel
information obtained by comparing physicochemical parameters of SV-IV with two
datasets made of intrinsically unfolded and ideally globular proteins. In addition, we
have analysed the SV-IV sequence by several publicly available disorder-oriented
predictors. Overall, disorder predictions and a re-examination of existing experimental
data strongly suggest that SV-IV needs large plasticity to efficiently interact with the
different targets that characterize its multifaceted biological function and should be
therefore better classified as an intrinsically disordered protein

Generalized Spectral Signatures of Electron Fractionalization in Quasi-One and -Two Dimensional Molybdenum Bronzes and Superconducting Cuprates

We establish the quasi-one-dimensional Li purple bronze as a photoemission paradigm of Luttinger liquid behavior. We also show that generalized signatures of electron fractionalization are present in the angle resolved photoemission spectra for quasi-two-dimensional purple bronzes and certain cuprates. An important component of our analysis for the quasi-two-dimensional systems is the proposal of a ``melted holon'' scenario for the k-independent background that accompanies but does not interact with the peaks that disperse to define the Fermi surface.Comment: 7 pages, 8 figure