Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Includes bibliographical references (leaves 89-95). / Previous studies using reduced or intact animal preparations suggested that coordinated movements can be generated by appropriate combinations of muscle synergies controlled by the nervous system. However, which areas of the central nervous system are responsible for structuring and combining muscle synergies remains an open question. In my thesis, I have addressed the question whether the brainstem and spinal cord are involved in structuring and combining muscle synergies in order to execute a range of natural movements. The strategy to investigate this question was to analyze the electromyogram (EMG) data recorded from the leg muscles during frog motor behaviors before and after neuronal transection. In my two sets of experiments, EMGs were recorded before and after transection at the level of the caudal end of the third ventricle and at the level of the caudal end of the pons in two groups of frogs. When the section was at the level of rostral midbrain, movements such as jumps, swims, kicks, and walks could be performed by the animals. In contrast, when the transection was at the level of rostral medulla, only a partial repertoire of natural movements could be evoked. Systematic analysis of muscle synergies in these preparations found two different types of synergies: (1) synergies shared by intact animals and animals with transection, and (2) synergies specific to individual motor behaviors. In addition, almost all synergies utilized in the execution of natural motor behaviors remain invariant after transection at the level of the caudal end of the third ventricle or at the level of the caudal end of the pons. The results suggest the following: / (cont.) (1) the neural network within the brainstem and spinal cord are necessary and sufficient in combining muscle synergies in the organization of natural movements, and (2) the neural circuitries within the medulla and spinal cord are sufficient to structure the repertoire of muscle synergies in natural motor behaviors. Overall, the major findings of this study indicate how the neural divisions in the CNS are functionally differentiated for structuring and combining modules in execution of natural movements. / by Jinsook Roh. / Ph.D.

Thesis: S.B. in Science, Technology and Society, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2015. / Cataloged from PDF version of thesis. / Although graduate science education does an excellent job training students in the technical and career aspects of science, there is too little attention paid to teaching the wisdom of "good" science that encourages riskier path-breaking work over fluff, with the highest goal of research being discovery rather than scholarly publication. In an attempt to help fill this gap, I interviewed fifteen senior life scientists over the past year. These interviews were filmed and edited into four topic videos: The Allure of Science, I-ow to Do Good Science, On Mentorship, and Where Science Is Headed. Geared towards graduate students in the life sciences, these videos are designed to start a conversation between students and their advisors on important but currently ignored aspects of doing good science. / by Brianna Jones. / S.B. in Science, Technology and Society

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2006. / Includes bibliographical references (leaves 189-193). / Causal relationships are all around us: wine causes stains; matches cause flames; foods cause allergic reactions. Next to language, it is hard to imagine a cognitive process more indicative of human intelligence than causal reasoning. To understand how people accomplish these feats, two major questions must be addressed: how do people acquire knowledge of causal relationships (causal learning), and how do people use that knowledge to make predictions and draw inferences (causal judgment)? The first part of this thesis is concerned with causal learning, and draws on the foundation of Bayesian inferential frameworks (e.g., Tenenbaum, Griffiths, & Kemp, 2006) to explain how observable data can be used to infer causal relationships between events. I will argue that rapid causal learning from small samples can be understood as rational inference over a representation of causality that includes a temporal delay between cause and effect. Experimentally, I show that people learn causal relationships faster when the temporal delay between cause and effect is less variable, just as is predicted by a rational statistical model of event causation. I argue that people's tendency to learn better from short delays is an artifact of the fact that short delays are inherently less variable. / (cont.) The second part of this thesis is concerned with causal judgment, and draws on the foundation of knowledge-based Bayesian networks to show that it is often more rational to make judgments using causal frameworks than purely statistical frameworks. Deviations from traditional norms of judgment, such as "base-rate neglect" (Tversky & Kahneman, 1974), can be explained in terms of a mismatch between the statistics given to people and the causal models they intuitively construct to support probabilistic reasoning. Experimentally, I provide evidence that base-rate neglect may be an artifact of applying causal reasoning to purely statistical problems. Six experiments show that when a clear mapping can be established from given statistics to the parameters of an intuitive causal model, people are more likely to use the statistics appropriately, and that when the classical and causal Bayesian norms differ in their prescriptions, people's judgments are more consistent with causal Bayesian norms. / by Tevye Rachelson Krynski. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011. / Cataloged from PDF version of thesis. / Includes bibliographical references. / Visual object recognition (categorization and identification) is one of the most fundamental cognitive functions for our survival. Our visual system has the remarkable ability to convey to us visual object and category information in a manner that is largely tolerant ("invariant") to the exact position, size, pose of the object, illumination, and clutter. The ventral visual stream in non-human primate has solved this problem. At the highest stage of the visual hierarchy, the inferior temporal cortex (IT), neurons have selectivity for objects and maintain that selectivity across variations in the images. A reasonably sized population of these tolerant neurons can support object recognition. However, we do not yet understand how IT neurons construct this neuronal tolerance. The aim of this thesis is to tackle this question and to examine the hypothesis that the ventral visual stream may leverage experience to build its neuronal tolerance. One potentially powerful idea is that time can act as an implicit teacher, in that each object's identity tends to remain temporally stable, thus different retinal images of the same object are temporally contiguous. In theory, the ventral stream could take advantage of this natural tendency and learn to associate together the neuronal representations of temporally contiguous retinal images to yield tolerant object selectivity in IT cortex. In this thesis, I report neuronal support for this hypothesis in IT of non-human primates. First, targeted alteration of temporally contiguous experience with object images at different retinal positions rapidly reshaped IT neurons' position tolerance. Second, similar temporal contiguity manipulation of experience with object images at different sizes similarly reshaped IT size tolerance. These instances of experience-induced effect were similar in magnitude, grew gradually stronger with increasing visual experience, and the size of the effect was large. Taken together, these studies show that unsupervised, temporally contiguous experience can reshape and build at least two types of IT tolerance, and that they can do so under a wide range of spatiotemporal regimes encountered during natural visual exploration. These results suggest that the ventral visual stream uses temporal contiguity visual experience with a general unsupervised tolerance learning (UTL) mechanism to build its invariant object representation. / by Nuo Li. / Ph.D.

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.

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.