Thesis: S.M., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 66-75). / Precise regulation of glutamate homeostasis is critical for normal brain function, as its disruption can impair excitatory transmission and result in neurodegenerative and neuropsychiatric disorders. Critical to maintaining glutamate homeostasis is a family of sodium-dependent glutamate transporters. GLT-1, the major glutamate transporter, is responsible for >90% of brain glutamate uptake. While previously thought to exist solely on astrocytes, the Rosenberg lab has identified GLT-1 as the major, if not only, glutamate transporter associated with excitatory terminals, particularly in CA3 pyramidal neuron axon terminals within CA3 and CA1 as well as in cortical layer V pyramidal neuron axon terminals within striatum. The specific functions of GLT-1 in axon terminals in regulating glutamate homeostasis and synaptic transmission are unknown; in order to investigate these functions, the Rosenberg lab has generated a conditional GLT- 1 KO mouse line where GLT-1 can be specifically deleted from neurons. The aim of this project was to investigate the transcriptome profiles resultant from knockout of neuronal GLT-1 (nGLT-1), within regions known to express GLT-1 on neurons, and to identify and characterize alterations in known biological pathways. I report that deletion of nGLT-1 results in a high degree of differential gene expression within CA3 (1509), CAl (322), and Striatum (1268). Furthermore, these alterations in gene expression were enriched in annotated biological pathways related to energy metabolism and neurotransmission. These findings challenge the long-held assumption that, because GLT-1 expression on neurons is significantly lower than on astrocytes, nGLT-1 contributes little to the regulation of synaptic glutamate homeostasis. / by Alexander Cory Wright Houston. / S.M.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1993. / Includes bibliographical references (leaves 169-171). / by John J. Kim. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1992. / Includes bibliographical references (p. 234-249). / by Alice Weaver Flaherty. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009. / Includes bibliographical references (p. 122-133). / In this thesis, rational Bayesian models and the Theory-theory are bridged to explore ways in which children can be described as Bayesian scientists. I investigate what it means for children to take a rational approach to processes that support learning. In particular, I present empirical studies that show children making rational predictions, exploration, and explanations. I test the claim that differences in prior beliefs or changes in the observed evidence should affect these behaviors. The studies presented in this thesis encompass two manipulations: in some conditions, children's prior beliefs are equal, but the patterns of evidence are varied; in other conditions, children observe identical evidence but children's prior beliefs are varied. I incorporate an additional approach in this thesis, testing children within a variety of domains, tapping into their intuitive theories of biological kinds, psychosomatic illness, balance, and physical systems. Chapter One introduces the problem. Chapter Two explores how evidence and children's strong beliefs about biological events and psychosomatic illness influence their forced-choice explanations in a story-book task. Chapter Three presents a training study to further investigate the developmental differences discussed in Chapter Two. Chapter Four looks at how children's strong differential beliefs of balance interact with evidence to affect their predictions, play, explanations, and learning. / (cont.) Chapter Five looks at children's exploratory play with a jack-in-the-box, (where children don't have strong, differential beliefs), given different patterns of evidence. Chapter Six investigates children's explanations following theory-neutral evidence about a mechanical toy. Chapter Seven concludes the thesis. The following chapters will suggest that frameworks combining evidence and theories capture children's causal learning about the world. / by Elizabeth R. Bonawitz. / Ph.D.

Thesis: Ph. D. in Neuroscience, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 177-192). / Memory is a central function of the brain and is essential to everyday life. Memory disorders range from those of memory transience, such as Alzheimer's disease, to those of memory persistence, such as post-traumatic stress disorder. To treat memory disorders, a thorough understanding of memory formation and retrieval is critical. To date, most research has focused on memory formation, with the neurobiological basis of memory retrieval largely ignored due to experimental limitations. Here, I present our recent advances in the study of memory retrieval using technologies to engineer the representation of a specific memory, memory engram cells, in the brain. First, using animal models of retrograde amnesia, we demonstrated that direct activation of amnesic engram cells in the hippocampus resulted in robust memory retrieval, indicating the persistence of the original memory. Subsequent experiments identified retained engram cell-specific connectivity in amnesic mice although these engram cells lacked augmented synaptic strength and dendritic spine density. We proposed that a specific pattern of connectivity of engram cells may be the crucial substrate for memory information storage and that augmented synaptic strength and spine density critically contribute to the memory retrieval process. Second, we examined memory engrams in transgenic mouse models of early Alzheimer's disease, which required the development of a novel two-virus approach. We demonstrated that optical induction of long-term potentiation at input synapses on engram cells restored both spine density and long-term memory in early Alzheimer mice, providing causal evidence for the crucial role of augmented spine density in memory retrieval. Third, using activity-dependent labeling, we found that dorsal subiculum had enhanced neuronal activity during memory retrieval as compared memory encoding. Taking advantage of a novel transgenic mouse line that permitted specific genetic access to dorsal subiculum neurons, we demonstrated that the hippocampal output circuits are functionally segregated for memory formation and memory retrieval processes. We suggested that the dorsal subiculum-containing output circuit is dedicated to meet the requirements associated with memory retrieval, such as rapid memory updating and retrieval-driven instinctive emotional responses. Together, these three related thesis projects have important implications for elucidating cellular and circuit mechanisms supporting episodic memory retrieval. / by Dheeraj Roy. / Ph. D. in Neuroscience

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2003. / Includes bibliographical references (leaves 87-95). / Interpreting the results of visual object perception experiments is too often ill-posed due to the disparate and sparse choice of stimuli. To address this problem, we have developed a flexible new technique called Random Image Structure Evolution (RISE). In the simplest case, RISE involves the presentation of image sequences depicting the evolution of a coherent image from a seemingly random field, along with the reverse sequences depicting the transformation back into randomness. As it samples a subset of the space of possible stimulus images, RISE image processing strictly preserves low-level attributes such as frequency spectra and luminance, and RISE experiments are designed to provide objectively verifiable measures of the onset and offset of subjects' conscious percepts. In turn, these onset and offset measures can serve as quantitative markers for characterizing a number of intriguing perceptual phenomena. Here I describe the basic RISE paradigm and discuss experimental applications of this technique which, it is hoped, may contribute greatly to the study of key aspects of high-level vision. Building on results from psychophysical studies of perceptual onset, priming, and hysteresis, as well as findings from a magnetoencephalographic study using RISE, this thesis explores the use of RISE in characterizing the perceptual markers and neural substrates of object and face perception. In addition, this thesis examines the issue of object perception and, in particular, robust face perception, within the context of the cortical representations that may underly them, presenting and evaluating a simple, well-motivated image coding scheme based on ordinal relations. / by Javid Sadr. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1991. / Includes bibliographical references. / by Anna Wang Roe. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1989. / Includes bibliographical references (leaves 123-129). / by Deborah Zaitchik-Samet. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 107-111). / The brain interprets ambiguous sensory information faster and more reliably than modern computers, using neurons that are slower and less reliable than logic gates. But Bayesian inference, which is at the heart of many models for sensory information processing and cognition, as well as many machine intelligence systems, appears computationally challenging, even given modern transistor speeds and energy budgets. The computational principles and structures needed to narrow this gap are unknown. Here I show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude. By connecting stochastic digital components according to simple mathematical rules, it is possible to rapidly, reliably and accurately solve many Bayesian inference problems using massively parallel, low precision circuits. I show that our circuits can solve problems of depth and motion perception, perceptual learning and causal reasoning via inference over 10,000+ latent variables in real time - a 1,000x speed advantage over commodity microprocessors - by exploiting stochasticity. I will show how this natively stochastic approach follows naturally from the probability algebra, giving rise to easy-to-understand rules for abstraction and composition. I have developed a compiler that automatically generate circuits for a wide variety of problems fixed-structure problems. I then present stochastic computing architectures for models that are viable even when constrained by silicon area and dynamic creation and destruction of random variables. These results thus expose a new role for randomness and Bayesian inference in the engineering and reverse-engineering of computing machines. / by Eric Jonas. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2007. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Page 95 blank. / Includes bibliographical references (p. 83-94). / Any given object in the world can cast an effectively infinite number of different images onto the retina, depending on its position relative to the viewer, the configuration of light sources, and the presence of other objects in the visual field. In spite of this, primates can robustly recognize a multitude of objects in a fraction of a second, with no apparent effort. The computational mechanisms underlying these amazing abilities are poorly understood. This thesis presents a collection of work from human psychophysics, monkey electrophysiology, and computational modelling in an effort to reverse-engineer the key computational components that enable this amazing ability in the primate visual system. / by David Daniel Cox. / Ph.D.