INVESTIGATION OF OCEAN ACOUSTICS USING AUTONOMOUS INSTRUMENTATION TO QUANTIFY THE WATER-SEDIMENT BOUNDARY PROPERTIES

Sound propagation in shallow water is characterized by interaction with the oceans surface, volume, and bottom. In many coastal margin regions, including the Eastern U.S. continental shelf and the coastal seas of China, the bottom is composed of a depositional sandy-silty top layer. Previous measurements of narrow and broadband sound transmission at frequencies from 100 Hz to 1 kHz in these regions are consistent with waveguide calculations based on depth and frequency dependent sound speed, attenuation and density profiles. Theoretical predictions for the frequency dependence of attenuation vary from quadratic for the porous media model of M.A. Biot to linear for various competing models. Results from experiments performed under known conditions with sandy bottoms, however, have agreed with attenuation proportional to f1.84, which is slightly less than the theoretical value of f2 [Zhou and Zhang, J. Acoust. Soc. Am. 117, 2494]. This dissertation presents a reexamination of the fundamental considerations in the Biot derivation and leads to a simplification of the theory that can be coupled with site-specific, depth dependent attenuation and sound speed profiles to explain the observed frequency dependence. Long-range sound transmission measurements in a known waveguide can be used to estimate the site-specific sediment attenuation properties, but the costs and time associated with such at-sea experiments using traditional measurement techniques can be prohibitive. Here a new measurement tool consisting of an autonomous underwater vehicle and a small, low noise, towed hydrophone array was developed and used to obtain accurate long-range sound transmission measurements efficiently and cost effectively. To demonstrate this capability and to determine the modal and intrinsic attenuation characteristics, experiments were conducted in a carefully surveyed area in Nantucket Sound. A best-fit comparison between measured results and calculated results, while varying attenuation parameters, revealed the estimated power law exponent to be 1.87 between 220.5 and 1228 Hz. These results demonstrate the utility of this new cost effective and accurate measurement system. The sound transmission results, when compared with calculations based on the modified Biot theory, are shown to explain the observed frequency dependence.

Dopaminergic and Non-Dopaminergic Value Systems in Conditioning and Outcome-Specific Revaluation

Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal Values Triggers Option Revaluations) neural model. MOTIVATOR describes cognitiveemotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.