WorldCat Identities

Newsome, William T.

Overview
Works: 25 works in 26 publications in 1 language and 45 library holdings
Roles: Thesis advisor, Author
Publication Timeline
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Most widely held works about William T Newsome
 
Most widely held works by William T Newsome
Charlie Rose brain series( Visual )

2 editions published in 2010 in English and held by 11 WorldCat member libraries worldwide

A conversation about decision making with a roundtable of brain researchers
Neural instructive signals in the cerebellum by Michael Chinwen Ke( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

An understanding of the neural patterns available to guide plasticity in vivo is needed to bridge our knowledge of synaptic plasticity to its function in learning. I investigated the patterns of neural activity that trigger plasticity in vivo in a simple cerebellum-dependent motor learning task, adaptation of the vestibulo-ocular reflex (VOR), with the specific goal of determining which neurons carry the instructive signals that trigger plasticity in the circuit for the VOR. The VOR stabilizes images on the retina during head turns by using vestibular signals to generate compensatory smooth eye movements in the opposite direction of head motion. Motor learning maintains the accuracy of the VOR by modifying the gain and timing of the reflex whenever retinal image motion is persistently associated with head movements. In the laboratory, motor learning in the VOR can be acutely induced by pairing head movements with motion of a visual stimulus. Two specific hypotheses have been proposed regarding the neural signals that guide motor learning in the VOR. One suggests that learning is guided by the activity of Purkinje cells, the output neurons of the cerebellum[1]. The other hypothesis suggests that learning is guided by climbing fiber input to the Purkinje cells[2-4]. Previous experiments addressing which neurons carry instructive signals have typically used a single training condition for increasing VOR gain and a single training condition for decreasing VOR gain[5, 6]. These two training conditions each elicited Purkinje cell and climbing fiber signals that carried information about the required direction of learning, and since the patterns of neural activity were consistent with both hypotheses, data are needed to provide constraints that could discriminate between the hypotheses. The goal of my research is to provide such constraints by recording the patterns of neural activity present in Purkinje cells and climbing fibers during a broader range of visual-vestibular stimuli that induce motor learning in the VOR. I induced motor learning in the VOR by pairing head movements with complex visual stimuli. These novel behavioral manipulations elicited many different combinations of Purkinje cell and climbing fiber signals, allowing us to evaluate how each of these neural signals contributes to learning. My data demonstrated that neither instructive signals in the climbing fibers nor Purkinje cells are necessary for learning, although either signals appear to be sufficient to support learning. Additionally, the largest changes in VOR gain occurred when both signals were present, suggesting that the changes mediated by Purkinje cell-triggered mechanisms and climbing-fiber triggered mechanisms are additive in their effects at the behavioral level. These findings are evidence that motor learning in the VOR is accomplished by parallel and independent operation of climbing fiber-triggered and Purkinje cell-triggered plasticity mechanisms. If cerebellum dependent motor learning is supported by the parallel and independent operation of plasticity mechanisms, similar motor learning need not be accomplished in a stereotyped fashion, but rather similar motor learning can be achieved by engaging distinct subsets of plasticity mechanisms each under the control of a unique instructive signal
Uncovering the neural correlates of intersubjective avoidance in a novel rat model by Jana Schaich Borg( )

1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide

Over half of all violent crimes are committed by only about 5% of offenders. While most healthy people feel strong aversion to seeing other people in pain, fear, or sadness, a phenomenon I define as "negative intersubjectivity", these persistent violent offenders (PVOs) have blunted reactions to other people's distress and the strength of their negative intersubjectivity deficit correlates with how much violence they ultimately perform. This suggests that if we could learn how to enhance PVOs aversion to other people's distress, we could decrease their violent behavior. In this dissertation, I describe a new rat model that can be used to study the neural mechanisms underlying negative intersubjectivity. I demonstrate that Observer rats will overcome their innate aversion of bright light to consistently avoid a dark, safe space if entering that dark space is paired with another Receiver rat getting shocked, a behavior called "intersubjective avoidance". In Chapter 2, I describe the relatively poor intersubjective avoidance behavior of mice. In Chapter 3, I describe the comparatively strong intersubjective avoidance behavior of rats, and show that rats' intersubjective avoidance is enhanced by personal experience with shock. In Chapter 4, I use patterns of expression of the immediate early gene c-Fos to determine whether activity in candidate brain regions correlates with intersubjective avoidance. I provide evidence that many brain regions--including the anterior cingulate, anterior insula, infralimbic cortex, and central amygdala--are active both when observing shock in another and when receiving shock to oneself, similar to what has been shown by fMRI studies of humans observing others in pain. However, within these areas, only activity in the anterior cingulate robustly correlates with how much intersubjective avoidance each individual Observer rat performs, perhaps analogous to how fMRI studies in humans have show that anterior cingulate activity correlates with humans' self-reports of "empathy". These results validate the rat model of intersubjective avoidance as model of negative intersubjectivity in humans, and highlight the anterior cingulate as a potential target for negative intersubjectivity interventions. In Chapter 5, I describe future experiments and discuss how research using this new behavioral paradigm may help develop treatments for not only PVOs, but all anti-social behavior
The role of the frontal eye field in gating and maintaining object signals in short-term memory by Kelsey Lynne Clark( )

1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide

Spatial attention is known to gate entry into short-term memory, and some evidence suggests that spatial signals may also play a role in binding features or protecting object representations during memory maintenance. To examine a potential role for spatial signals in maintaining object short-term memory, the activity of neurons in the Frontal Eye Field (FEF) of macaque monkeys was recorded during an object-based delayed match-to-sample (DMS) task. In this task monkeys were trained to remember an object identity over a brief delay, irrespective of the locations of the sample or target presentation. FEF neurons exhibited visual, delay, and target period activity, including selectivity for sample location and target location. Delay period activity represented the sample location throughout the delay, despite the irrelevance of spatial information for successful task completion. Furthermore, neurons continued to encode sample position in a variant of the task in which the matching stimulus never appeared in their response field. FEF neurons also exhibited target-position-dependent anticipatory activity immediately prior to target onset, suggesting that the monkeys can predict target position within blocks. These results show that FEF neurons maintain spatial information during short-term memory, even when that information is irrelevant for task performance. Despite the robust delay period activity we observed in FEF during the DMS task, we found little further evidence to support the theory that this activity contributes to object memory maintenance. Noise correlations were present between pairs of simultaneously recorded FEF and IT neurons during the sample and early delay periods, but did not persist into the second half of the delay period, despite the continued elevation of firing rates in both regions throughout the delay. The most direct method of assessing the contribution of the FEF delay period activity observed during the DMS task to object memory was the pharmacological elimination of that activity and evaluation of the impact on task performance. Inactivation of FEF with muscimol produced spatially localized deficits on the memory guided saccade task, but did not selectively impair object memory performance for sample stimuli appearing in the mnemonic scotoma
Integration of sensory and reward information during perceptual decision-making in lateral intraparietal cortex (LIP) by Alan E Rorie( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

The work presented in this dissertation primarily focuses on decision-related activity in the lateral intraparietal area (LIP) and, secondarily, the dorsolateral prefrontal cortex (DLPFC). In Chapter 1 we review the previous independent investigations indicating that these areas are separately modulated by sensory information, value information and choice appropriate to represent decisions. We argue that when both sensory and value information must be simultaneously integrated to make choices, it is unknown, if, how and when these areas integrate these factors. We present a behavioral paradigm in which animal subjects must combine sensory and value information, on a trial-to-trial basis, to make optimal choices. This paradigm is based on a well-known motion discrimination task; however, in our task the magnitude of the reward associated with each option varies from trial to trial. On some trials both options are worth equally large or small rewards. On other trials one option's reward is greater than that of the other. In Chapter 2, we demonstrate that in the unequal reward conditions subjects' choices are consistently biased towards the greater magnitude option. Additionally, we will show that this bias is independent of the motion stimulus strength and its magnitude is nearly optimal. In Chapter 3, we observe that single neurons in cortical area LIP consistently, simultaneously and dynamically represent both sensory and value information. We will argue that this representation supports an integrator model of decision making, in which sensory information is accumulated until the decision is resolved by a threshold crossing. Our results support an interpretation of this model in which value information adjusts the likelihood of a threshold crossing by iv raising or lowering the accumulator's initial state. In Chapter 4, we present a preliminary comparison between LIP and DLPFC activity, under identical conditions, suggesting they play fundamentally different roles in decision making. In Chapter 5, we discuss future lines of research
Visual cortical circuitry for building word representations by Andreas Maximilian Rauschecker( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

Reading is the remarkable human ability to decode the sounds and meaning of language from an intricate combination of strokes of ink. This perceptual capacity is tolerant to changes in size, font, and other visual features of text. Transformations of the neural representation between many neural modules, including primary visual cortex (V1), other retinotopically organized areas, and language regions, are necessary for successful reading. An important focus of previous work has been on characterizing word representations in ventral occipito-temporal cortex, in particular in a left-hemisphere region known as the visual word form area (VWFA). To better understand the transformations occurring between V1 and the VWFA, we measured (using functional magnetic resonance imaging, fMRI) and perturbed (using transcranial magnetic stimulation) neural responses in several visual areas while subjects read words defined by atypical visual features (Chapter 2). We show that VWFA responses are invariant to the visual features that define word stimuli, and we show how flexible neural circuitry accounts for these abstract representations. While these studies contribute to understanding the VWFA's inputs, the inner organization of the VWFA remains unexplored. We describe experiments that use fMRI and electrocorticography (ECoG) in the human brain to show that the VWFA is sensitive to visual field position, and that together with a homologous right-hemisphere region, its inner organization encompasses a retinotopic map (Chapter 3). Information about abstract word forms is in turn transferred from the VWFA to language areas of the brain. This transfer occurs via large white-matter bundles that can be measured with diffusion imaging (DTI). We compare data from a severely dyslexic individual to a group of control subjects and show that the individual's deficits are due to a missing arcuate fasciculus, one of the major pathways important for reading and language (Chapter 4). This set of experiments ties together several fields of neuroscientific inquiry, including early visual processing, complex visual object representations, and language. Ultimately, if we are to understand how spots of light are transformed into sounds and meaning, we must unravel the smaller transformations that occur within all these components of the reading circuitry
Low-dimensional neural features reflect central features of muscle activation by Zuley Rivera Alvidrez( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

Any time we move, our brains solve the difficult problem of translating our motor intentions to muscle commands. Understanding how this computation takes place, and in particular, what role the motor cortex plays in movement generation, has been a central issue in systems neuroscience that remains unresolved. In this thesis, we took an unconventional approach to the analysis of cortical neural activity and its relationship to executed movements. We used dimensionality reduction to extract the salient patterns of neural population activity, and related those to the muscle activity patterns generated during arm reaches to a grid of targets. We found that salient neural activity patterns appeared to tightly reflect muscle activity patterns with a biologically-plausible lag. We also applied our analyses to movements that were planned before being executed, and found that a muscle-framework view of the cortical activity was consistent with previously-described predictions of movement kinematics based on the state of the cortical population activity. Overall, our results elucidate remarkable simplicity of the motor-cortical activity at the population level, despite the complexity and heterogeneity of individual cell's activities
Measurements and technology for long-term neural prosthetic systems by Cynthia Anne Chestek( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Cortical brain-machine interfaces, or BMIs, is a relatively new field with the potential to provide many different clinical treatments, particularly for fully paralyzed patients. In these applications, multichannel electrode arrays are implanted into motor cortical areas in order to extract useful control signals. My research focuses on taking proof-of-concept academic BMI systems, and solving the engineering challenges that currently prevent them from being used in a clinical setting. These challenges include running a BMI for more than a few hours or a single day, and finding ways to minimize the size, cost, and operational complexity of the complete system. This dissertation includes an analysis of neuron stability over long timescales. I will show that the relationship between neurons in motor cortices and behavior remains stationary over time despite substantial noise, which could mitigate some concerns about long-term BMI performance. I will also discuss the development of HermesC, a wireless system for recording multichannel neural data from freely moving primates. This device dramatically reduces the size and cost of current recording technology for real-time neural prosthetic systems, and could be useful for human clinical trials. It may also enable neural prosthetic studies with animals in a less constrained setting. Combining traditional neural recordings with overnight wireless neural recordings, I will also show that there are substantial changes in neural waveforms from single neurons across days. However, the quality of neural decodes (the extraction of useful control signals) is only slightly improved by sorting individual units rather than using simple threshold crossings. This may enable long term BMI operation because multiunit neural "hash" on electrode arrays tends to persist for a long time, perhaps years, after single neuron signals have declined due to various tissue responses. In fact, other recent work from this project has demonstrated high performance neural decodes using only threshold crossings on arrays ~2.5 years after implantation
Neural basis of value based decision making by Daniel L Kimmel( )

1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide

For many decisions, we must explicitly compare the value of two or more goods being offered. However, often decisions are not between multiple goods, but rather between a single offer and the choice to pass on that offer, such as when deciding to buy a new car, marry a significant other, or read this abstract! For these decisions the relevant comparison is between the expected benefit of the offer and its associated cost. We studied cost-benefit decisions in the macaque monkey while recording from single neurons in the orbitofrontal cortex (OFC), which has been implicated previously in decisions between competing goods. We found that the animal was sensitive to the balance of cost and benefit. That is, his willingness to accept an offer increased monotonically as we increased the benefit while keeping the cost constant. We found that the OFC represented task-relevant information--such as benefit, choice, and expected outcome--in a complex manner. These signals were mixed at the level of single neurons, but by examining the population response, we found separable ensembles of neurons that represented each of these task relevant variables. Moreover, different sets of neurons appeared to represent these signals for discrete temporal epochs within and between trials, which may correspond to distinct functional processes revealed by behavior. Taken together, we offer a novel view of how a population of neurons may collectively represent value and choice information and how that population may transform the representation dynamically over time
Moving through the brain : a study of movement preparation in the oculomotor and reach systems by Rachel Stern Kalmar( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Movement preparation allows the rapid and accurate execution of voluntary move- ments, and can be influenced by factors that may change from moment to moment, such as attention and differences in stimulus properties. Consequently, movement preparation unfolds differently across many repetitions of the same movement. Averaging neural responses across many repetitions is necessary to interpret single-cell recordings, but diminishes our ability to characterize the dynamics of the underlying process. A central question in neuroscience, and also of fundamental clinical importance, is to understand how these plans develop in the brain. Several research groups are starting to build prosthetic devices that are controlled directly by neural activity in motor areas of the brain (Nicolelis, 2001; Donoghue, 2002; Musallam et al., 2004; Schwartz, 2004; Santhanam et al., 2006; Hochberg et al., 2006; Mulliken et al., 2008; Andersen et al., 2010), but the extent to which these can be developed may hinge critically upon our understanding of the neural basis of motor preparation. Simultaneous recording from populations of neurons allows dynamics of movement preparation to be estimated on single trials. Our goal is to characterize these dynamics, to gain insight into the process underlying movement preparation. Here, we recorded peri-saccadic activity from ensembles of neurons in an oculomotor area, prearcuate cortex, in two monkeys. While monkeys performed visually-guided eye movements, we measured firing rates of a population of neurons using a 96-electrode array. We studied (1) the responses from a heterogeneous population of prearcuate cortex neurons involved in decision-making and movement preparation, (2) the relationship between saccade initiation times and responses from the neural population, and (3) how these responses compared to those recorded in PMd, a cortical area involved in arm movements. The array data from prearcuate allowed us to compare responses from individual neurons with previous findings, but also allowed us to analyze the population dynamics of movement planning, by using techniques applied to the reach system. We found that ensemble responses from diverse populations of prearcuate neurons (1) can be visualized as trajectories moving through a low-dimensional neural state space, (2) reflect visual, decision-, and movement-related aspects of the task, and (3) correlate with a monkey's reaction time on a trial-by-trial basis. Further, the single-trial relationship between ensemble activity in prearcuate cortex and saccadic reaction times was qualitatively and quantitatively very similar to the relationship between ensemble activity in PMd and corresponding reach reaction times. This framework for analyzing neural population activity and dynamics should permit new extensions of single-neuron-level models, and may offer further insight into general mechanisms of movement preparation across motor systems
Ultrasound induced neurostimulation by Randy L King( )

1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide

Ultrasound-induced neurostimulation has recently gained increasing attention. Developments in the use of ultrasound to stimulate and modulate neural activity have raised the possibility of using ultrasound as a new investigative and therapeutic tool in brain research. Little is known about the mechanisms by which it affects neural activity or about the range of acoustic parameters and stimulation protocols that elicit responses. In this thesis, conditions are established for transcranial stimulation of the nervous system in vivo, using the mouse somatomotor response. It is reported that (1) continuous-wave stimuli are as effective as or more effective than pulsed stimuli in eliciting responses, and responses are elicited with stimulus onset rather than stimulus offset; (2) stimulation success increases as a function of both acoustic intensity and acoustic duration; (3) interactions of intensity and duration suggest that successful stimulation results from the integration of stimulus amplitude over a time interval of 50 to 150 ms; (4) the motor response elicited appears to be an all-or-nothing phenomenon, meaning stronger stimulus intensities and durations increase the probability of a motor response without affecting the duration or strength of the response; and (5) motor responses, measured by normalized EMG signals in the neck and tail regions, change signifcantly when sonicating rostral and caudal regions of the mouse motor cortex. Taken together our findings present good evidence for being able to target selective parts of the motor cortex with ultrasound neurostimulation in the mouse, steps that should provide encouragement for the development of new applications in larger animal models, including humans
Circuits underlying visual attention in primate neocortex by Nicholas A Steinmetz( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Humans and many other species attend to only a small portion of available visual information at any given moment. They enhance perception of the attended stimulus either overtly, making an eye or head movement to orient toward it, or covertly, without any such movements. The neural circuits that underlie these two types of attention behaviors, and the relationship between them, remain unclear. To investigate the interdependence of them we trained monkeys on a task that behaviorally dissociated the location of covert attention from the location of a saccade target. Recordings in extrastriate visual cortical area V4 surprisingly revealed that enhanced firing rates and other modulations of neural activity accompanied both covert attention and saccade preparation. These results suggested a hypothesis about the circuits that could mediate the control of both behaviors. We recorded neurons in the frontal eye field, an area involved in controlling both behaviors, and found evidence contradicting our hypothesis. Separately, we examined the circuit underlying the integration of attention-related feedback signals with visual information in visual cortex by recording from distinct neuron populations, defined by laminar depth, within V4 during the covert attention task. We found that all neuron populations were modulated indistinguishably during attention. Finally, we constructed a large-scale model of FEF and V4 on neuromorphic hardware and used it to investigate a novel hypothesis about the way feedback from FEF influences V4, namely, via NMDA synapses. This model makes predictions for future experiments that could help uncover the mechanism of attention-related modulation of visual cortex. Taken together, these results have helped to elucidate our understanding of the circuits within and between frontal and visual cortical areas underlying attention
Neural dynamics of motor preparation and tools for large scale neuroscience by Eric S Trautmann( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Current state of the art experimental methods are limited both in the capability of recording from large populations of neurons simultaneously, as well as in the complexity of the behaviors studied. In this work, I describe the development of new tools and methods for recording from large populations of neurons in rhesus macaque nonhuman primates (NHP). In addition, I describe the development of a haptic robotic interface to implement more complex motor tasks. I use this apparatus to study how short timescale adaptation to dynamic loads alters neural preparatory activity in premotor and primary motor cortices prior to movements. Chapter 1 provides an introduction and in-depth overview of the work covered in the remaining four chapters of this dissertation. Chapter 2 describes efforts to estimate neural population dynamics using multiunit threshold crossings in place of well isolated single units, which potentially eliminates a time consuming, difficult, and inexact portion of data analysis that serves as bottleneck for discovery. In Chapter 3, I describe the development of two-photon calcium imaging for rhesus macaque monkeys performing motor behaviors and the implementation of an optical brain machine interface (oBMI). In Chapter 4, I describe the development of techniques for using high-density silicon electrodes, such as the Neuropixels probe, in NHP. Lastly, in Chapter 5, I describe the development of a haptic experiment in which we introduce a simulated drag force and investigate the impact of short timescale adaptation to these dynamic loads on motor preparation
New methods and models for interrogating cell assembly, projection, and whole brain functional data during motivated behavior by Logan Grosenick( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

While it is accepted that coordinated activity among populations of neurons in three-dimensional brain structures is critical to animal behavior, our understanding of such systems and their dynamics is circumscribed by available recording and intervention technologies. In particular, the ability to optically record and perturb dynamics in long-range, connectivity- and genetically-specified projections is needed to understand the roles that such inter-regional projections play in behavior, and fast methods to record and perturb population dynamics at cellular resolution across brain volumes are necessary to understand how cell assemblies coordinate across areas to encode and generate behavior. Finally, interpretable statistical techniques able to accurately capture the trends and dynamics in these complex data are required to turn observations into comprehensible descriptions, models, and theories. This work seeks to address these needs by developing (1) fiber photometry, a minimally-invasive method for recording bulk activity in connectivity-targeted and genetically-targeted cell bodies and projections during behavior, (2) SWIFT volume imaging, a method for synchronous recording and identification of cell assemblies across large volumes of tissue at high frame rates during behavior, and (3) interpretable statistical methods appropriate for high dimensional, potentially nonlinear neuroimaging data including those that are produced by SWIFT but also applicable to other whole-brain imaging data such as those generated by functional magnetic resonance imaging (fMRI). These approaches are applied and validated in several examples of motivated behavior including social approach behavior in mice, prey approach behavior in zebrafish, reward-based learning in mice, modulation of reward-seeking behavior by prefrontal cortex in rats, and incentivized decision making in humans
A freely-moving monkey treadmill model by Justin Daniel Foster( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. In this dissertation, I present a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement. The freely-moving rhesus monkey model employs technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. This dissertation demonstrates the flexibility and utility of this new monkey model, including the first recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill. Using this monkey model, it is shown that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average firing rate of the threshold crossings covaries with the speed of individual steps. On a population level, neural state-space trajectories of walking at different speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions. Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment, and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis
Reward, value, and decision : neural mechanisms of decision-making in rhesus monkeys( Visual )

1 edition published in 2005 in English and held by 1 WorldCat member library worldwide

Specialized neural circuits supporting reinforcement learning by Ian Connors Ballard( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

A fundamental challenge facing organisms is to learn how to act in order to earn rewards. Theoretical and empirical work has characterized distinct neural systems that learn different classes of information and assist learning in distinct behavioral contexts. In this thesis, I argue that these sources of information are integrated within the striatum, a region of the basal ganglia known to be critical for associative learning. Neurons in the striatum store the motivational value of stimuli in the environment and support the expression of habitual behavioral responses to these stimuli. Based on anatomy, the striatum is also known to receive direct afferent input from diverse cortical and subcortical sites. I report the results of two studies designed to characterize the contribution of two such afferent regions, the inferior frontal cortex and the hippocampus, to reward learning. Subjects in my experiments performed tasks in which they were required to learn the relationship between sets of visual features and motor responses. These tasks were designed to emphasize different types of relationships that draw on the computational properties known to be uniquely supported by the inferior frontal sulcus and hippocampus. I developed computational models of learning adapted to the demands of the tasks and used functional magnetic resonance imaging (fMRI) to relate brain activity to the variables in these models. In Study 1, I show that interactions between the striatum and the inferior frontal cortex support the learning of abstract rules. In Study 2, I show that interactions between the striatum and the hippocampus support the learning of conjunctive relationships. These results provide novel evidence that different learning systems interact in cases where the problem draws on multiple types of representations
Understanding early vision : from white noise to natural scenes by Lane McIntosh( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

The retina constitutes the first stages of vision, an exposed part of our nervous system that transduces light into a sophisticated and efficient binary code conveying everything we can know about the visual world. This thesis details attempts to understand these neural computations. First I present theoretical work showing how the retina constructs an efficient yet diverse cell population, providing an explanation for how and why parallel inhibitory pathways generate the retinal ganglion cell classical receptive field. This first study builds on more than a century of using artificial stimuli to understand the retina. However, the normal function of the retina is to convey information about the natural visual world, and yet we currently lack methods to understand retinal computations in their native regime. In the second part I present a way forward using modern deep learning techniques to learn accurate models of the retina under natural conditions. These artificial neural network models of the retina allow unprecedented insight into the structure and function of the retina--we find that they reproduce known retinal phenomena, have internal units that are highly correlated with interneuron responses, and allow for the discovery of new retinal phenomena and mechanisms. These models provide a powerful and transparent framework for testing hypotheses and understanding sensory computations in their natural setting. The thesis concludes with how principles learned from the computations of biological vision can lead to better artificial neural networks for computer vision
Advancing motor neural prosthesis robustness and neuroscience by Sergey D Stavisky( )

1 edition published in 2016 in English and held by 1 WorldCat member library worldwide

The frontier challenges that must be solved before brain-machine interfaces (BMIs) can be used as clinically useful motor prostheses differ depending on the degree of function being restored. Two-dimensional cursor control (i.e., for communication) has recently reached high levels of peak performance in pre-clinical studies, but translation is hampered by less than reliable performance due to unstable neural signals. Meanwhile, control of robotic arms remains poor, despite some impressive glimpses at what the future could be, because we lack fundamental understanding of how the brain incorporates the BMI into its motor schema. This hampers our ability to accurately decode intended arm movements. My dissertation focused on both sets of problems in pre-clinical macaque BMI studies. Chapters 2 and 3 provide solutions for improving BMI robustness. I first describe a machine learning approach to building decoder algorithms that are robust to the changing neural-to-kinematic mappings that plague translational BMI efforts. We developed a multiplicative recurrent neural network decoder that could exploit the large quantities of data generated by a chronic BMI -- data that has heretofore gone unused. I then describe a neural engineering approach for increasing the device lifespan by providing high performance control even after losing spike signals. I developed a method for decoding local field potentials (LFPs) as a longer-lasting alternative or complimentary BMI control signal. This led to the highest-performing LFP-driven BMI and the first 'hybrid' BMI which decoded kinematics from spikes and LFPs together. Chapter 4 looks ahead to challenges that will be encountered when BMI-controlled limbs operate in the physical world by describing how error signals impact ongoing BMI control. I perturbing the kinematics of monkeys performing a BMI cursor task and found that visual feedback drove responses starting 70 ms later in the same motor cortical population driving the BMI. However, this initial response did not cause unwanted BMI output because it was limited to a decoder null space in which activity does not affect the BMI. When activity changed in output-potent dimensions starting 115 ms after perturbation, it caused corrective BMI movement. This elegant arrangement may hint at a broader computational strategy by which error processing is separated from output
Neural dynamics of reaching following incorrect, absent, or last-moment preparation by Katherine Cora Ames( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Moving is thought to require separate preparation and execution steps. While preparing, neural activity in primary motor and dorsal premotor cortices achieves a state specific to an upcoming action, but movements are not performed until the execution phase. In this work, we investigated the interactions between motor preparation and motor execution. We first investigated whether the preparatory state (more precisely, prepare-and-hold state) is required for movement execution using two complementary experiments. We compared monkeys' neural activity during delayed and non-delayed reaches, and in a delayed reaching task in which the target switched locations on a small percentage of trials. Neural population activity bypassed the prepare-and-hold state both in the absence of a delay and if the wrong reach was prepared. However, the initial neural response to the target was similar across behavioral conditions, regardless of whether there was a delay period. This means that there are consistent neural preparatory steps which are performed prior to movement even in the absence of a delay. This suggests that the prepare-and-hold state can be bypassed if needed, but there is a short-latency preparatory step which is performed prior to movement even without a delay. We suggest that this preparatory step may be best understood as a dynamical process rather than simply a particular, static neural state. We next examined whether motor preparation and motor generation can be run in parallel. We instructed monkeys to reach to a particular target, and occasionally switched that target to a new location shortly before the monkey began initiating his reach. We found that the amount of time required to change a reach goal tends to remain constant regardless of whether that computation is being performed online (during the execution of the initially-cued reach), or offline (prior to reach initiation). Examining neural activity during this task, we found that neural activity following a switch tends to explore dimensions which are not well-represented during the course of normal reaching. Furthermore, reaches can be initiated correctly even if their neural activity has not fully recovered from the switch, as long as neural activity in dimensions which are relevant to movement output has been corrected. This work reveals a potentially important way in which neural activity can simultaneously prepare one reach while executing another, again underscoring the view that motor preparation is itself a dynamical process which is independent of but complimentary to movement generation
 
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Alternative Names
William Newsome Amerikaans neurowetenschapper

William Newsome neurocientífico estadounidense

William Newsome neuroscientifique américain

Ньюсом, Уильям

ویلیام نیوزومه

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English (23)