Frequency selectivity measured both psychophysically and physiologically

The ability to resolve the individual frequency components of a complex sound is known as frequency selectivity. The auditory system seems to act as a series of overlapping band-pass filters or auditory filters (AF), the width of which describe frequency selectivity. It is a fundamental and extensively studied property of the auditory system, yet its neural basis is not fully understood. This is due, in part, to the fact that two distinct approaches are taken to explore it; psychophysics and physiology, the results of which are difficult to reconcile. AF are measured in quite different ways in the two sub-fields. Psychophysics measures the ability of the system as a whole by using masking paradigms to measure the bandwidth of AF from behavioural data. A preferred method in human psychophysics is notched noise (NN) masking using forward masking with fixed signal level, since it does not suffer from confounds like suppression and off-frequency listening. In physiology bandwidth can be measured for single cells, or a population of cells, at various levels of the auditory system, and is traditionally done by observing the number of action potentials elicited in response to pure tone stimuli. The auditory system is known to be highly non-linear and so comparing the results of such vastly different approaches is problematic. Also, psychophysics measures the detection threshold of 'signal' sounds; how this compares to mean spike rates used in physiology is not clear. Attempts have been made in the past to apply the same method in animals to measure both psychophysical and physiological bandwidths, with varying degree of success. No successful attempt has been made to use an up-to-date method used in human psychophysics. In this thesis I take a step towards comparing psychophysical and physiological results by 1) developing a novel method that allows forward masked NN bandwidths to be measured behaviourally in the ferret, and 2) applying the same psychophysical paradigm to measuring bandwidth in guinea pig inferior colliculus (IC) and primary auditory cortex (A1) neurons. In addition a signal detection theory (SDT) approach is used on the physiological data to make results more comparable to psychophysical ones. Results from the behavioural method show that it can be used to successfully measure both forward and simultaneously masked NN bandwidths in the same animal, and that these measurements are in close agreement with one another and with bandwidths measured using previous methods. Results from the guinea pig physiology study show that bandwidths measured from IC neurons using the psychophysical NN paradigm are narrower than pure tone estimates of bandwidth, in the same neurons. However, the NN estimates are in close agreement with auditory peripheral and perceptual bandwidths, a finding which differs substantially from previous studies. Unexpectedly, however, bandwidths estimated from A1 neurons using masking show much finer tuning at high frequencies than seen further down the auditory system. This tuning is not only narrower than pure tone tuning in these neurons, but also finer than psychophysically measured estimates, which represent the auditory system as a whole. However, this may be related to the greater non-linearity of cortical neurons compared with those in the midbrain and lower. This work demonstrates that it is possible to reconcile different measurements of tuning in the auditory system by using appropriate methods. It also highlights the complex nature of auditory neurons and how care must be taken when measuring frequency selectivity using different approaches. In addition it provides a method for measuring auditory bandwidths psychophysically and physiologically in the same animal, allowing a direct comparison between the two; a vital step in investigating the neural basis of perceptual frequency selectivity

Frequency selectivity measured both psychophysically and physiologically

The ability to resolve the individual frequency components of a complex sound is known as frequency selectivity. The auditory system seems to act as a series of overlapping band-pass filters or auditory filters (AF), the width of which describe frequency selectivity. It is a fundamental and extensively studied property of the auditory system, yet its neural basis is not fully understood. This is due, in part, to the fact that two distinct approaches are taken to explore it; psychophysics and physiology, the results of which are difficult to reconcile. AF are measured in quite different ways in the two sub-fields. Psychophysics measures the ability of the system as a whole by using masking paradigms to measure the bandwidth of AF from behavioural data. A preferred method in human psychophysics is notched noise (NN) masking using forward masking with fixed signal level, since it does not suffer from confounds like suppression and off-frequency listening. In physiology bandwidth can be measured for single cells, or a population of cells, at various levels of the auditory system, and is traditionally done by observing the number of action potentials elicited in response to pure tone stimuli. The auditory system is known to be highly non-linear and so comparing the results of such vastly different approaches is problematic. Also, psychophysics measures the detection threshold of 'signal' sounds; how this compares to mean spike rates used in physiology is not clear. Attempts have been made in the past to apply the same method in animals to measure both psychophysical and physiological bandwidths, with varying degree of success. No successful attempt has been made to use an up-to-date method used in human psychophysics. In this thesis I take a step towards comparing psychophysical and physiological results by 1) developing a novel method that allows forward masked NN bandwidths to be measured behaviourally in the ferret, and 2) applying the same psychophysical paradigm to measuring bandwidth in guinea pig inferior colliculus (IC) and primary auditory cortex (A1) neurons. In addition a signal detection theory (SDT) approach is used on the physiological data to make results more comparable to psychophysical ones. Results from the behavioural method show that it can be used to successfully measure both forward and simultaneously masked NN bandwidths in the same animal, and that these measurements are in close agreement with one another and with bandwidths measured using previous methods. Results from the guinea pig physiology study show that bandwidths measured from IC neurons using the psychophysical NN paradigm are narrower than pure tone estimates of bandwidth, in the same neurons. However, the NN estimates are in close agreement with auditory peripheral and perceptual bandwidths, a finding which differs substantially from previous studies. Unexpectedly, however, bandwidths estimated from A1 neurons using masking show much finer tuning at high frequencies than seen further down the auditory system. This tuning is not only narrower than pure tone tuning in these neurons, but also finer than psychophysically measured estimates, which represent the auditory system as a whole. However, this may be related to the greater non-linearity of cortical neurons compared with those in the midbrain and lower. This work demonstrates that it is possible to reconcile different measurements of tuning in the auditory system by using appropriate methods. It also highlights the complex nature of auditory neurons and how care must be taken when measuring frequency selectivity using different approaches. In addition it provides a method for measuring auditory bandwidths psychophysically and physiologically in the same animal, allowing a direct comparison between the two; a vital step in investigating the neural basis of perceptual frequency selectivity

A pair of temperate sub-Neptunes transiting the star EPIC 212737443

We report the validation of a new planetary system around the K3 star EPIC 212737443 using a combination of K2 photometry, follow-up high resolution imaging and spectroscopy. The system consists of two sub-Neptune sized transiting planets with radii of 2.6R⊕, and 2.7R⊕, with orbital periods of 13.6 days and 65.5 days, equilibrium temperatures of 536 K and 316 K respectively. In the context of validated K2 systems, the outer planet has the longest precisely measured orbital period, as well as the lowest equilibrium temperature for a planet orbiting a star of spectral type earlier than M. The two planets in this system have a mutual Hill radius of ΔRH = 36, larger than most other known transiting multi-planet systems, suggesting the existence of another (possibly non-transiting) planet, or that the system is not maximally packed

Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC

The uncertainty on the calorimeter energy response to jets of particles is derived for the ATLAS experiment at the Large Hadron Collider (LHC). First, the calorimeter response to single isolated charged hadrons is measured and compared to the Monte Carlo simulation using proton-proton collisions at centre-of-mass energies of sqrt(s) = 900 GeV and 7 TeV collected during 2009 and 2010. Then, using the decay of K_s and Lambda particles, the calorimeter response to specific types of particles (positively and negatively charged pions, protons, and anti-protons) is measured and compared to the Monte Carlo predictions. Finally, the jet energy scale uncertainty is determined by propagating the response uncertainty for single charged and neutral particles to jets. The response uncertainty is 2-5% for central isolated hadrons and 1-3% for the final calorimeter jet energy scale.Comment: 24 pages plus author list (36 pages total), 23 figures, 1 table, submitted to European Physical Journal

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011

Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC

Measurements are presented of production properties and couplings of the recently discovered Higgs boson using the decays into boson pairs, H →γ γ, H → Z Z∗ →4l and H →W W∗ →lνlν. The results are based on the complete pp collision data sample recorded by the ATLAS experiment at the CERN Large Hadron Collider at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV, corresponding to an integrated luminosity of about 25 fb−1. Evidence for Higgs boson production through vector-boson fusion is reported. Results of combined fits probing Higgs boson couplings to fermions and bosons, as well as anomalous contributions to loop-induced production and decay modes, are presented. All measurements are consistent with expectations for the Standard Model Higgs boson

Measurement of the top quark pair cross section with ATLAS in pp collisions at √s=7 TeV using final states with an electron or a muon and a hadronically decaying τ lepton

A measurement of the cross section of top quark pair production in proton-proton collisions recorded with the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy of 7 TeV is reported. The data sample used corresponds to an integrated luminosity of 2.05 fb -1. Events with an isolated electron or muon and a τ lepton decaying hadronically are used. In addition, a large missing transverse momentum and two or more energetic jets are required. At least one of the jets must be identified as originating from a b quark. The measured cross section, σtt-=186±13(stat.)±20(syst.)±7(lumi.) pb, is in good agreement with the Standard Model prediction