Browsing by Subject "Neural"
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Item Open Access Component Neural Networks of Morality(2015) Ngo, LawrenceMoral cognition represents a foundational faculty of the human species. Our sense of morality develops beginning at a very young age, and its dysfunction can lead to devastating mental disorders. Given its central importance, it has fittingly garnered the attention of thinkers throughout the ages. For millennia, philosophers have pondered what it is to be right or wrong, good or bad, virtuous or vicious. For centuries, psychologists have elucidated how people acquire and act upon a sense of morality. More recently in the last decade, neuroscientists have embarked on a project to study how morality arises from computations in the brain. However, this latest project has been fragmented: researchers have largely studied various neural components of morality - including emotion, value, and mentalizing - in isolation. This has resulted in an informal and disjointed model for the neural mechanisms of morality. This dissertation is concerned with more formally identifying neural components and their influences on each other in the context of moral cognition.
In Chapter 2, I study how the component neural networks of moral cognition may be involved in distinct aspects of a single decision by employing a complex clinical decision making task involving the disclosure of conflicts of interest. I show that for a given decision, the magnitude of conflict of interest is tracked by mentalizing networks, while the degree of disclosure-induced behavioral change exhibited by participants is predicted by value networks. In Chapter 3, I move beyond the informal model of morality used in Chapter 2 and previous literature by devising a methodology to identify hierarchical ontologies of neural circuits; such an approach can have implications on further discussions of morality, and more generally, on other aspects of cognitive neuroscience. From this, I present the 50 elemental neural circuits that are fundamental to human cognition and explore how these elements can differentially combine to form emergent neural circuits. In Chapter 4, I use these advances to address morality, uncovering its relevant component neural networks in a data-driven way. I show that neural circuits important in supporting higher-level moral computations include mentalizing and taste. In Chapter 5, I demonstrate an important complexity in a compositional model of morality. I show that one of the components of moral cognition, mentalizing, can paradoxically be influenced by moral judgments themselves. To conclude, I highlight the implications of both theoretical and methodological advances. The hierarchical ontologies of neural circuits may be a profitable framework for the future characterization and study of mental disorders; and to effectively study these circuits, the use of moral judgment and decision-making paradigms will be effective experimental tasks, considering the centrality of moral cognition to who we are, whether in health or illness.
Item Open Access Electrical Interfaces to Implanted Neural Medical Devices(2016) Jochum, ThomasThe electrical interface to neural medical devices is researched from three perspectives, namely, the electronics within the device, the electrodes on the device, and the electromagnetic fields around the device.
A Brain-Machine Interface may allow paralyzed patients to control robotic limbs with neural signals sensed by fine wires inserted into the brain. The neural signals have an amplitude under one millivolt and must be amplified. A totally integrated amplifier is designed, manufactured, and characterized. The amplifier is fabricated in a standard half-micron CMOS process without capacitors or resistors. Two application issues not previously addressed are solved. First, the topology of the amplifier is shown to be less sensitive to long-term drift of transistor parameters than the standard topology. Second, a neural signal corrupted by 10 millivolts of powerline interference can be recovered. The amplifier has a gain of 58 dB, a bandwidth of 750 to 14k Hz, power consumption of 180 uW, and noise of 1.5 uV RMS. The design techniques proven in this amplifier are suitable for clinical Brain Machine Interfaces.
An implanted electroencephalogram (EEG) recorder may aid the diagnosis of infrequent seizure-like events that are currently diagnosed, without proof, as epilepsy. A proof-of-concept study quantifies the electrical characteristics of the electrodes planned for the recorder. The electrodes are implanted in an ovine model for eight weeks. Electrode impedance is less than 800 Ohm throughout the study. A frequency-domain determination of sedation performs similarly for surface versus implanted electrodes throughout the study. The time-domain correlation between an implanted electrode and a surface electrode is almost as high as between two surface electrodes (0.86 versus 0.92). EEG-certified clinicians judge that the implanted electrode quality is at least adequate and that the implanted electrodes provide the same clinical information as surface electrodes except for a noticeable amplitude difference. No significant issues are found that invalidate the concept of an implanted EEG recorder.
Transcranial stimulation may treat a multitude of neural and psychological illnesses. The stimulation may have higher repeatability and lower patient effort if an implanted device provides the stimulation. The shape of the device, 300 mm long by 1 mm in diameter, is unlike any present implanted device. Five techniques that deliver energy to the device are analyzed using computer simulations. The electrode for the techniques that employ an electric field to deliver the energy is a new design that exploits the anatomy of the scalp and skull. The electric field techniques deliver energy that is likely suitable for some stimulation protocols but not for all. The techniques that employ a magnetic field deliver more than the energy required, especially if the shape of the coil that creates the magnetic field is automatically optimized. However, the magnetic-field techniques heat the brain; the electric-field techniques do not heat the brain. This research validates the new delivery concepts and justifies future research.
Item Open Access Modeling Archimedean, Extreme-Value and Archimax Copulas with Neural Networks(2023) Ng, YutingCopulas are popular in high-dimensional statistical applications as they allow for dependence modeling with arbitrary margins. They are also used in rare event analysis where tail behaviours are important. Current successful applications of copulas however, generally focus on simplified assumptions, a risk in areas such as healthcare, safety and finance where model misspecification may lead to potential catastrophes. Furthermore, for rare event simulation and prediction, likelihood-based estimation may lead to dependencies in the tail being overlooked by dependencies in the bulk of the data. Moreover, there are still unsolved challenges related to the parameterization, estimation and sampling of copulas.
We propose neural representations to better fit data, and solve challenges related to parameterization, estimation and sampling. We focus on the classes of Archimedean, hierarchical Archimedean, extreme-value and Archimax copulas. Notably, these copulas have stochastic representations as mixture models of latent random variables. In particular, the functions that characterize these copulas may be described by expectations of the latent variables. The first neural representation is motivated by the ability of neural networks to represent expectations and function compositions. The second neural representation is motivated by the impressive ability of generative networks at sampling and the convergence of empirical expectations. To avoid repeated differentiation in computing the likelihood of observations during training, we make use of data transforms to collapse d-dimensional observations into multiple one-dimensional transformed observations. To avoid repeated differentiation in computing the conditional distributions in conditional sampling, we use the sampling frameworks that come naturally with the stochastic representations.
We consider the probabilistic construction of Archimedean copulas as mixture models with the completely monotone Archimedean generator given by the Laplace transform of a latent random variable. We model the latent variable with a generative network and consider the empirical Laplace transform given samples of the latent variable. We compute higher-order derivatives using the properties of the Laplace transform. We modify existing Marshall-Olkin type sampling to our parameterization with generative networks. We extend our training and sampling method to an existing network representation. We also extend our method to hierarchical Archimedean copulas, subsequently recovering a richer class of copulas.
We consider the spectral decomposition of the stable tail dependence function in extreme-value copulas. We propose both network and generative representations for the latent spectral variable. Motivated by the data transform in the Pickands estimator, we transform d-dimensional observations into one-dimensional exponentially distributed transformed observations, then perform maximum likelihood estimation using the transformed observations. We sample using the connection to the spectral representation of max-stable processes.
We build on the stochastic representation of Archimax copulas with latent radial and simplex components. The challenges are estimating a flexible Archimedean generator and sampling the simplex component. We consider the more general d-monotone Archimedean generator given as the Williamson d-transform of the radial component, with connection to the decomposition of the Kendall function. We also consider the correspondence between the spectral component, simplex component and generalized Pareto copulas. We empirically validate our algorithms on high dimensional data, extrapolation to extreme and out-of-distribution detection.