Adaptive Processes in Speech Perception: Contributions from Cerebral and Cerebellar Cortices

In the sensorimotor domain, adaptation to distorted sensory input has been well-characterized and is largely attributed to learning mechanisms in the cerebellum that adjust motor output to achieve the same desired sensory outcome. Our interest in the role of the cerebellum in cognitive processes has led us to question whether it also contributes to adaptation in tasks that do not require voluntary motor output. Speech perception is a domain where there exist many examples of adaptation that are guided by both sensory and cognitive processes, without intentional motor involvement. Thus, we investigated behavioral and neural characteristics of speech perception adaptation to spectrally distorted words using a sophisticated noise-vocoded speech manipulation that mimics cochlear implants. We demonstrated that adaptation to spectrally distorted words can be achieved without explicit feedback by eithergradually increasing the severity of the distortion or by using an intermediate distortion during training. We identified regions in both the cerebellar and cerebral cortex that showed differences in neural responses before and after training. In the cerebellum, this included regions in lobes V and VI, and Crus I. In the cerebrum, this included regions in the inferior frontal gyrus, the superior temporal sulcus, and the posterior inferior/middle temporal gyrus. In some of these regions, we further found changes in the magnitude of the neural responses that corresponded to the degree of behavioral improvements in performance. To gain some insight into the nature of the interactions between cerebral and cerebellar cortices and the types of representations involved in speech perception adaptation, we conducted a simple functional connectivity analysis using cerebellar seed regions of interest. We found interactions between the cerebellum and cerebral cortex that were dependent on the location of the cerebellar region. Overall, our behavioral and functional neuroimaging results point to cerebellar involvement in speech perception adaptation, and we conclude with a discussion of the learning mechanisms and neuroanatomical pathways that may support such plasticity

Neurons against Noise : Neural adaptations for dim light vision in hawkmoths

All animals perceive the world through their senses, which form the basis for their decisions and motor actions. However, when these all-important senses reach their limit and cease to provide reliable information, the animal’s survival is threatened. Among the senses, vision is brought to its limits on a daily basis, because its signal strength is diminished as night falls, and increases again as the sun rises. In this thesis, I investigated adaptations that enable the visual system of hawkmoths, a group of insects, to cope with the low light intensities they face at night. I have focused on neural adaptations, manifested in the processing of visual neurons, in contrast to anatomical adaptations, such as modifications of the eye. I showed that neural adaptations exist in the motion vision system of hawkmoths, in the form of integration of visual information in space and time. Furthermore, I demonstrated that a combination of such spatial and temporal summation increased sensitivity and information content in dim light (Paper I). The amount of spatial and temporal summation matched the ecological needs of different hawkmoth species, as well as their anatomical adaptations for visual sensitivity: night active species, and species with less sensitive eyes had more extensive spatial and temporal summation than day-active species and species with very sensitive optics (Paper II). Furthermore, I identified and characterised candidate neurons that carry out spatial and temporal summation in the brain of hawkmoths (Paper III). Finally, I quantified the effects of temporal summation on the ability of hawkmoths to track flowers in hovering flight at different light levels, and showed that a subset of the observed behavioural phenomena could be explained by temporal processing in the nervous system (Paper IV). Taken together, this work has provided detailed insight into how neural processing can increase visual reliability in dim light. The results presented are not only relevant to hawkmoths, since neural summation is also expected to increase visual sensitivity in other species of nocturnal insects, and can be compared to similar mechanisms in vertebrates. Furthermore, this work is instructive for the development of artificial visual systems, for which insect brains have proven to be a successful biomimetic model

Glutamatergic Metabolites and Gray Matter Losses in Schizophrenia: A Longitudinal Study Using In Vivo Proton Magnetic Resonance Spectroscopy

Approximately one in hundred people suffer from schizophrenia. Current medications partially improve the symptoms. There is no cure. Glutamate, an excitatory neurotransmitter, is a possible cause of the schizophrenia symptoms. Excessive glutamate release eventually leads to neurodegeneration. Longitudinal studies are necessary to observe the neurodegenerative process. Seventeen schizophrenia patients and 17 healthy volunteers underwent proton magnetic resonance spectroscopy (MRS) and imaging to measure neurochemical and structural changes in vivo. Metabolite levels were measured from a 1.5cm3 voxel in the anterior cingulate and thalamus using the stimulated echo acquisition mode sequence. Gray matter (GM) was assessed with voxel-based morphometry and ANALYZE. Total glutamatergic metabolite (tGL), N-acetylaspartate (NAA), and GM were significantly decreased in schizophrenia over 80 months. Reduced tGL and NAA levels were significantly correlated with GM changes. tGL loss was negatively correlated with social functioning. Significantly decreased tGL levels were possibly associated with GM loss in the spectroscopy voxel. Metabolite signal-to-noise ratio, but not quantification, was decreased as a function of MR system age. These findings demonstrate the feasibility of long-term MRS studies and implications for the pathophysiology of schizophrenia. tGL and GM losses were consistent with neurodegeneration but the effects of an early neurodevelopmental lesion or the effects of chronic medication cannot be ruled out. Structural and metabolite changes in these patients implicate glutamate as a possible target of medication in this disorder. The association between tGL loss and social functioning suggests it might be possible to arrest deterioration with pharmaceuticals that target glutamate

Inference processing and error recovery in sentence understanding

Solving the mysteries of human language understanding inevitably requires an answer to the question of how the language understander resolves ambiguity, for human language is certainly ambiguous. But ambiguity leads to choices between possible explanations, and choice opens the door for mistakes. Unless we are willing to believe that the human language understander always makes the correct choice, any explanation of ambiguity resolution must be considered incomplete if it does not also account for recovery from an incorrect decision.This dissertation describes a new approach to lexical ambiguity resolution during sentence understanding which is implemented in a program called ATLAST. Many computational models of natural language understanding have dealt with lexical ambiguity resolution, but ATLAST is one of the few models to address the associated problem of error recovery. ATLAST's ability to recover from an incorrect lexical inference decision stems from its ability to retain unchosen word meanings for a period of time after it selects the apparently context-appropriate meaning of an ambiguous word. The short-term retention of possible lexical inferences permits ATLAST to recover from incorrect decisions without backtracking and reprocessing text, and without keeping a record of possible choices indefinitely.The principle of retention provides a solution to the problem of error recovery which is compatible with current psycholinguistic theories of lexical disambiguation. Furthermore, the existence of some form of retention in lexical disambiguation is supported by the results of experiments with human subjects. This dissertation includes a discussion of these results and speculation on how the principle of retention might be extended to account for recovery from erroneous higher-level inference decisions

Visual and linguistic processes in deep neural networks:A cognitive perspective

When people describe an image, there are complex visual and linguistic processes at work. For instance, speakers tend to look at an object right before mentioning it, but not every time. Similarly, during a conversation, speakers can refer to an entity multiple times, using expressions evolving in the common ground. In this thesis, I develop computational models of such visual and linguistic processes, drawing inspiration from theories and findings from cognitive science and psycholinguistics. This work, where I aim to capture the intricate relationship between non-linguistic modalities and language within deep artificial neural networks, contributes to the line of research into multimodal Natural Language Processing. This thesis consists of two parts: (1) modeling human gaze in language use (production and comprehension), and (2) modeling communication strategies in referential tasks in visually grounded dialogue. In the first part, I delve into enhancing image description generation models using eye-tracking data; evaluating the variation in human signals while describing images; and predicting human reading behavior in the form of eye movements. In the second part, I build models quantifying, generating, resolving, and adapting utterances in referential tasks situated within visual and conversational contexts. The outcomes advance our understanding of human visuo-linguistic processes by revealing intricate strategies at play in such processes, and point to the importance of accounting for them when developing and utilizing multimodal models. The findings shed light on how the advancements in artificial intelligence could contribute to advancing the research on crossmodal processes in humans and vice versa

Visual and linguistic processes in deep neural networks:A cognitive perspective

When people describe an image, there are complex visual and linguistic processes at work. For instance, speakers tend to look at an object right before mentioning it, but not every time. Similarly, during a conversation, speakers can refer to an entity multiple times, using expressions evolving in the common ground. In this thesis, I develop computational models of such visual and linguistic processes, drawing inspiration from theories and findings from cognitive science and psycholinguistics. This work, where I aim to capture the intricate relationship between non-linguistic modalities and language within deep artificial neural networks, contributes to the line of research into multimodal Natural Language Processing. This thesis consists of two parts: (1) modeling human gaze in language use (production and comprehension), and (2) modeling communication strategies in referential tasks in visually grounded dialogue. In the first part, I delve into enhancing image description generation models using eye-tracking data; evaluating the variation in human signals while describing images; and predicting human reading behavior in the form of eye movements. In the second part, I build models quantifying, generating, resolving, and adapting utterances in referential tasks situated within visual and conversational contexts. The outcomes advance our understanding of human visuo-linguistic processes by revealing intricate strategies at play in such processes, and point to the importance of accounting for them when developing and utilizing multimodal models. The findings shed light on how the advancements in artificial intelligence could contribute to advancing the research on crossmodal processes in humans and vice versa

Evolutionary, developmental neural networks for robust robotic control

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 136-143).The use of artificial evolution to synthesize controllers for physical robots is still in its infancy. Most applications are on very simple robots in artificial environments, and even these examples struggle to span the "reality gap," a name given to the difference between the performance of a simulated robot and the performance of a.real robot using the same evolved controller. This dissertation describes three methods for improving the use of artificial evolution as a tool for generating controllers for physical robots. First, the evolutionary process must incorporate testing on the physical robot. Second, repeated structure on the robot should be exploited. Finally, prior knowledge about the robot and task should be meaningfully incorporated. The impact of these three methods, both in simulation and on physical robots, is demonstrated, quantified, and compared to hand-designed controllers.by Bryan Adams.Ph.D

Nanobody-aided structural study of the activity- regulated cytoskeleton-associated protein (Arc) using synchrotron radiation and cryo-EM

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Scheduled routing for the NuMesh

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 66-68).by Milan Singh Minsky.M.S

Development and evaluation of a novel framework for subcortical gray matter segmentation using quantitative magnetic susceptibility and R2* mapping

Quantitative susceptibility mapping (QSM) and effective relaxation rate (R∗2) mapping are promising magnetic resonance imaging (MRI) techniques to study iron content in the human brain in vivo. The ability to quantify iron content in subcortical gray matter (SGM) is important to better understand its role in neurodegenerative diseases as well as during normal brain aging. However, accurate determination of tissue magnetic susceptibility and R∗2 in brain structures, such as SGM, may be challenging due to potential segmentation inaccuracies, specifically when performed automatically. The present thesis introduces a robust framework to automatically segment and characterize SGM using quantitative susceptibility maps and exemplarily applies it to investigate iron-related susceptibility and R∗2 changes in patients with multiple sclerosis (MS) in comparison to controls