Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009. / Includes bibliographical references (leaves 129-135). / I introduce a new set of natively probabilistic computing abstractions, including probabilistic generalizations of Boolean circuits, backtracking search and pure Lisp. I show how these tools let one compactly specify probabilistic generative models, generalize and parallelize widely used sampling algorithms like rejection sampling and Markov chain Monte Carlo, and solve difficult Bayesian inference problems. I first introduce Church, a probabilistic programming language for describing probabilistic generative processes that induce distributions, which generalizes Lisp, a language for describing deterministic procedures that induce functions. I highlight the ways randomness meshes with the reflectiveness of Lisp to support the representation of structured, uncertain knowledge, including nonparametric Bayesian models from the current literature, programs for decision making under uncertainty, and programs that learn very simple programs from data. I then introduce systematic stochastic search, a recursive algorithm for exact and approximate sampling that generalizes a popular form of backtracking search to the broader setting of stochastic simulation and recovers widely used particle filters as a special case. I use it to solve probabilistic reasoning problems from statistical physics, causal reasoning and stereo vision. Finally, I introduce stochastic digital circuits that model the probability algebra just as traditional Boolean circuits model the Boolean algebra. / (cont.) I show how these circuits can be used to build massively parallel, fault-tolerant machines for sampling and allow one to efficiently run Markov chain Monte Carlo methods on models with hundreds of thousands of variables in real time. I emphasize the ways in which these ideas fit together into a coherent software and hardware stack for natively probabilistic computing, organized around distributions and samplers rather than deterministic functions. I argue that by building uncertainty and randomness into the foundations of our programming languages and computing machines, we may arrive at ones that are more powerful, flexible and efficient than deterministic designs, and are in better alignment with the needs of computational science, statistics and artificial intelligence. / by Vikash Kumar Mansinghka. / Ph.D.

Ng Ho Keung. / Thesis (M.D.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 241-276). / Declaration of originality --- p.4 / Acknowledgement --- p.5 / Publication of study materials --- p.6 / Summary of thesis --- p.8 / Chapter Chapter1 --- Meningiomas and arachnoid cells - an introduction --- p.15 / Chapter Chapter2 --- Mesenchymal and epithelial differen- tiation in meningiomas --- p.25 / Chapter Chapter3 --- The extracellular matrix proteins - their properties and significance in tumor biology --- p.55 / Chapter Chapter4 --- Objectives and methodology of present study --- p.73 / Chapter Chapter5 --- Comparative immunohistology of meningiomas with paraffin and cryostat sections --- p.79 / Chapter Chapter6 --- Immunoelectron microscopy of meningiomas for cytokeratin filaments --- p.93 / Chapter Chapter7 --- Cryostat section immunohistochemistry of meningiomas --- p.102 / Chapter Chapter8 --- Immunohistochemistry of meningiomas on cytologic smear preparations --- p.180 / Chapter Chapter9 --- In vitro immunohistochemical analysis of meningioma cultures --- p.194 / Chapter Chapter10 --- Overall discussion and conclusion List of tables --- p.240 / References --- p.241

by Shi-chung Ng. / Thesis (M.Phil.) - Chinese University of Hong Kong. / Bibliography: leaves 91-105.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2006. / Includes bibliographical references (p. 259-276). / I studied organizational principles that may subserve the control and learning of forelimb movements. Among these principles, I focused on muscular coordination patterns, motor cortical excitability, and sensorimotor interactions. I found that muscle activity in grasping and reaching behaviors could be reconstructed by linear combinations of a small number of time-varying muscle synergies, each fit with coefficients unique to the behavior. However, the generalization of these synergies between behavioral conditions was limited, in part by the sensitivity of the extraction algorithm to stereotyped muscular relations within contrasted conditions. In reaching studies designed to assist or resist different movement directions, I found a gradual change in the structure, as well as recruitment, of synergies. When a perturbation was targeted to the activity within a single muscle, I found a transient, relative suppression of this muscle in response to descending motor commands. In other motor cortical microstimulation experiments, I confirmed that long-train microstimulation is able to evoke complex, convergent movements. Even during highly-trained reaching movements, I found that there was relatively little invariance of the muscular patterns in relation to kinematic variables coding for the hand's displacement and velocity. / (cont.) In two studies examining the kinematic consequences of modulating cortical excitability, I either infused tissue plasminogen activator into monkey cortex or applied transcranial magnetic stimulation to human cortex, either while or before each adapted to a clockwise force field. In both cases basal motor performance was spared, but each manipulation appeared to be associated with disruptions of subjects' ability to retain, improve, or recall recent adaptations. Among other human studies, I investigated the interaction of dynamic adaptation and sequence learning, and found that simultaneous acquisition of a force field and a sequence does not impair performance on either but may have enabled subjects to tune in to, and chunk, their movements. I found that motor consolidation may be dependent on the more effortful learning enabled by catch-trial interruptions of practice on a novel condition. Finally, I used functional imaging and manual cutaneous stimulation to show that the hemodynamic response was biased according to receptor density but generally non-somatotopic and distributed throughout sensorimotor cortex. / by Simon Alexander Overduin. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 109-122). / How do children learn their first words? Do they do it by gradually accumulating information about the co-occurrence of words and their referents over time, or are words learned via quick social inferences linking what speakers are looking at, pointing to, and talking about? Both of these conceptions of early word learning are supported by empirical data. This thesis presents a computational and theoretical framework for unifying these two different ideas by suggesting that early word learning can best be described as a process of joint inferences about speakers' referential intentions and the meanings of words. Chapter 1 describes previous empirical and computational research on "statistical learning"--the ability of learners to use distributional patterns in their language input to learn about the elements and structure of language-and argues that capturing this abifity requires models of learning that describe inferences over structured representations, not just simple statistics. Chapter 2 argues that social signals of speakers' intentions, even eye-gaze and pointing, are at best noisy markers of reference and that in order to take advantage of these signals fully, learners must integrate information across time. Chapter 3 describes the kinds of inferences that learners can make by assuming that speakers are informative with respect to their intended meaning, introducing and testing a formalization of how Grice's pragmatic maxims can be used for word learning. Chapter 4 presents a model of cross-situational intentional word learning that both learns words and infers speakers' referential intentions from labeled corpus data. / by Michael C. Frank. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009. / Vita. / Includes bibliographical references. / Within the visual cortex a vast assortment of molecules work in concert to sharpen and refine neuronal circuits throughout development. With the advent of genetic mouse models it is now possible to probe the individual contributions of single molecules implicated in this process. The Arc (activity-regulated cytoskeletal associated) gene is an effector immediate early gene that has been suggested to play a critical role in synaptic plasticity. The goal of this thesis is to understand the workings of Arc within the visual cortex. Specifically, we ask how genetic deletion of Arc influences plasticity, and how visual response properties differ between cells types containing, and not containing Arc. To elucidate a role for Arc in visual cortical plasticity we took advantage of knockin mice expressing GFP in place of Arc protein (referred to as KO mice for simplicity). We combined intrinsic signal imaging, visually evoked potentials, and two-photon in vivo calcium imaging to assess plasticity in juvenile and adult wild-type (WT), heterozygote, and KO mice. We find that plasticity is disrupted in the visual cortex of Arc KO mice in the absence of obvious deficits at the level of basal response properties. In addition, this work has revealed that: 1) Arc is necessary for the establishment of normal ocular dominance during development and critical for deprived eye depression in the visual cortex of juvenile animals 2) Loss of Arc impairs AMPA receptor internalization in visual cortex- a necessary requirement for synaptic weakening after lid suture. / (cont.) 3) Open eye potentiation fails to occur after extended deprivation in the absence of Arc 4) Arc is required for stimulus response potentiation in juvenile animals. 5) Arc is not required for the synaptic scaling up of response suggesting a specific role in Hebbian plasticity. 6) Single cell analysis within the binocular zone of Arc-GFP homozygotes reveals that the distribution of Arc lacking GFP-positive cells does not display a contralateral-bias as compared to controls, and the majority of Arc-lacking GFP-positive cells receive equal input from each eye, suggesting that Arc is critical for synaptic weakening during development. Together, these experiments illustrate the essential role for Arc in experience-dependent plasticity within the visual system. / by Cortina McCurry. / Ph.D.

Thesis: Ph. D. in Neuroscience, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 243-252). / Complex motor behaviors in humans, such as speech, are not innate, but instead are learned. How does the brain construct neural circuits that generate these motor behaviors during learning? To understand the neural mechanisms underlying learned motor skills, I use vocal learning in songbirds as a model. While previous studies have shown that a premotor area in the songbird brain, HVC, is important for stereotyped adult song, the role of HVC in juvenile song is less known. This thesis characterizes how activity in HVC develops during song learning in juvenile birds. Early in song learning, temporal structure emerged in HVC. During the earliest vocalization of juvenile birds (subsong), HVC neurons exhibit bursts of action potentials. However, only half of the neurons show bursts that are temporally aligned to syllables, and most of these bursts are clustered around onsets of subsong syllables. Over several days, as the bird starts producing the earliest stereotyped vocalization called protosyllables, HVC neurons start exhibiting rhythmic bursts at 5-10 Hz. These rhythmic bursts are aligned to protosyllables, and bursts from different neurons are active at different latencies relative to protosyllables. Thus, as a population, HVC neurons start forming a rhythmic neural sequence. As the bird matures, multiple distinct syllable types emerge from a protosyllable. During this process, some neurons are active only during a specific syllable type ('specific neurons') while others are active during both syllable types ('shared neurons'). These shared neurons are active at similar latencies for both syllable types, and therefore form a shared neural sequence. Over development, fraction of shared neurons decrease and more neurons become specific. These results demonstrate that splitting of a neural sequence into multiple sequences underlies the emergence of a multiple syllable types. Moreover, this sequence splitting is observed during different song learning strategies, suggesting that this is a fundamental neural mechanism for song learning. This work demonstrates how the growth of a rhythmic neural sequence and its subsequence splitting gives rise to complex vocalization in songbirds. This may be a general neural mechanism in which the brain constructs neural circuits during learning of a complex motor behavior. / by Tatsuo Okubo. / Ph. D. in Neuroscience

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 177-203). / Every person has a unique set of individual experiences that make up their memories. Yet surprisingly, recent work has shown that people tend to remember and forget the same images. This is because these images differ in their memorability - a predictive value of whether an image is likely to be later remembered or not. However, the properties of memorability and its effects in the brain are unexplored. Here, I describe the first characterization of the neural and psychophysical bases of memorability. First, I show that memorability is highly consistent in the domain of face images, despite their similar perceptual features and same basic-level category, and examine what facial attributes are predictive of memorability (Chapter 2). I extend these findings to demonstrate that memorability is not only consistent across images, but across different images of the same face identity (Chapter 3); thus, memorability can be conceptualized as an intrinsic property to whole entities. I then compare memorability to several phenomena shown to influence memory - bottom-up attention, top-down attention, and priming - and find that memorability effects remain independent of these phenomena (Chapter 4). Lastly, I investigate the neural correlates of memorability in a human functional magnetic resonance imaging experiment (Chapter 5). I find sensitivity to memorability in the medial temporal lobe and ventral visual stream, with a memorability-centric representational geometry in the neural patterns in these regions. Importantly, this sensitivity is dissociable from classical individual subsequent memory effects that I show to be localized in the prefrontal cortex. These results also indicate that until now, memory work has largely confounded the effects of the participant (individual memory) and the stimulus (memorability), and I propose a re-examination of past memory findings through the lens of memorability. In whole, this work presents memorability as a novel phenomenon, easily quantified for images and entities, with its own dedicated neural signatures at the intersection of perception and memory. / by Wilma A. Bainbridge. / Ph. D.