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How does the brain efficiently process such a wide variety of sound environments? Sound environments can be described probabilistically by a change in sound levels across frequency (spectro-) and time (-temporal). To efficiently process sensory signals despite large changes in background noise the brain adapts neural response properties to spectrotemporal sound level statistics. With an increased range of sound levels, spectrotemporal contrast, the slope of neural input-output functions decreases. This gain reduction, termed contrast gain control (CGC), maintains dynamic range enabling cells to discriminate a wider input range. CGC is conserved among species, evident in the midbrain and throughout cortex. CGC accounts for changes in perceptual judgements in humans and mice, however, the underlying cortical neuromodulatory mechanisms remain poorly understood. Furthermore, neural sensory response properties change with internal state factors, such as wakefulness, that can be indexed by pupil-size. However, it is unknown how pupil-indexed internal state changes affect adaptation to sound contrast and whether these cortical adaptations are needed for sound evoked changes in pupil size. To address these questions, we combined pupillometry with 2-photon calcium imaging of layer 2/3 neurons of mouse primary auditory cortex (A1), along with pharmacological and genetic knockout approaches. Our work reveals that contrast-dependent neuromodulatory effects of synaptic zinc are necessary for CGC in A1 and that there are optimal pupil-indexed states for CGC. Further, we report contrast-dependent changes in pupil size that, like CGC, rely upon neuromodulatory zinc signaling in auditory cortex. Thus, we reveal cortical neuromodulatory sensory processing that influences pupil size. In addition to its reported use as a marker for Alzheimer’s disease and schizophrenia, pupil size may therefore serve as a non-invasive clinical marker for disrupted neuromodulatory cortical signaling dur

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2007., Includes bibliographical references (p. 93-95)., Understanding the states in the vicinity of the Mott insulator is crucial to understanding both the physics of the transition between a Mott insulating phase and a metallic phase and the physics of the cuprate high-temperature superconductors. In this thesis, we start from the standard Mott insulating regime of the two dimensional Hubbard model. We then study the physics of nearby states where transport has been restored. First we consider doping of the Hubbard model in the strong coupling limit, i.e. the t-J model. Using the variational Monte Carlo technique, we study Gutzwiller projected states. In particular, studying the projected BCS quasiparticles, we calculate the renormalization of the quasipaticle current and the spectral weight. Both are investigated as a function of momentum and doping. Finally, we discuss the relation between this model and the cuprate superconductors. In the second half of this thesis, we return to the half-filled Hubbard model but now at intermediate values of U/t. In this regime, we study the spin liquid phase, a state that possibly lives between the Mott insulator and the normal metal. Motivated by the recently created organic compound r-(BEDT-TTF)2- Cu2(CN)3, we study a particular spin liquid where there is a spinon Fermi surface coupled to a U(1) gauge field. While still a charge insulator, this model has many metallic-like properties. We first develop a quantum Boltzmann equation for this model from which we calculate the spin resistivity and the more experimentally accessible thermal conductivity. We then proceed to consider spinon pairing and calculate the gauge field contribution to the spin susceptibility. We find that the theoretical result is consistent with experiments giving further evidence that at low temperatures this compound is described by this particular U(1) spin liquid., by Cody Patrick Nave., Ph.D.

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003., Includes bibliographical references (p. 52-53)., Information routinely collected by health care organizations is used by researchers to analyze the causes of illness and evaluate the effectiveness of potential cures. Medical information sharing systems are built to encourage hospitals to contribute patient data for use in clinical studies. These organizations possess a wide variety of environments and risk assessments, and require sufficient assurances of patient privacy. This thesis introduces mechanisms to dynamically generate an applicable security policy for medical information sharing systems. We present implementation-independent mechanisms that are capable of interoperating with different security settings at different sites to produce security configurations with significantly different characteristics and vulnerabilities. We also present a rules-based agent to assist in the selection process. This approach gives maximum freedom to generate the appropriate system according to the tradeoffs between cost, patient privacy, and data accessibility., by Patrick M. Cody., M.Eng.