Steven M. Chase, Ph.D. - US grants

DESCRIPTION (provided by applicant): Under this fellowship, we propose to use information theoretic techniques to quantify the flow of information about complex acoustic stimuli through the cochlear nucleus (CN) and the central nucleus of the inferior colliculus (ICC) of decerebrate cats. These structures are obligatory stops in the auditory pathway to the cortex, and each contains several different response types that process the auditory signal in parallel. Our hypothesis is that these different response types have arisen to encode separately the various aspects of acoustic stimuli. For example, some response types may be encoding information about the temporal envelope of the acoustic pressure wave, others may be sensitive to the local spectral structure of the sound, and still others may be involved in processing the sound's location. We will test the relative sensitivity of various response types to the temporal and spectral envelopes of sound with speech-like characteristics, as well as to location cues embedded in interaural differences.

Movements are inherently variable: one never throws a dart or a basketball in exactly the same way twice. On the face of it, this variability in behavior is detrimental to performance, preventing one from consistently hitting the bull's-eye or making the basket. However, computational theories posit that motor variability may also serve a functional role, enabling exploration and learning of more efficient movements. This creates an intriguing duality: while variability should be minimized for short-term motor performance (to act reliably), it should be maximized for long-term performance (to promote learning). During practice, variability might be useful for developing motor skill. When it's game time, however, variability should be suppressed to the greatest extent possible. Might the central nervous system set the amount of variability in a context-appropriate fashion? This study will investigate the neural correlates of motor variability and establish the connections between neural variability, behavioral performance, and learning.

Neural variability lies at the heart of several theoretical computational models, from implementations of probabilistic computation to Hebbian learning rules. Although the importance of variability has been well recognized, the structure and regulation of neural variability within the central nervous system is not well understood. This project coordinates a program of experiments and new analytical techniques to examine the structure of neural variability in the motor system. It seeks to establish, first, how variability depends on behavioral demands, and second, how variability impacts learning. To achieve this, many neurons of the motor and premotor cortices will be studied simultaneously during performance of demanding behaviors. By studying two distinct areas in the motor pathway, the impacts of noise on motor planning and execution can be examined separately. Furthermore, population recordings can be leveraged to decompose variability into three conceptually distinct components: (1) variability that is related to the task (signal variability), (2) trial-to-trial variability shared among neurons, and (3) private variability within each neuron. The investigators will explore how variability of each type is modulated by task context and learning. These decompositions will yield insight into the mechanisms of variability generation during performance.

Practice makes perfect. While most people can swing a hammer, it takes thousands of hours of practice to become a master carpenter who can swing a hammer with fluid speed and precision. This process of long-term improvement in movement accuracy is known as skill acquisition, and is presumably driven by coordinated changes in our brain's neural representation of the movement. Yet despite the bedrock importance of skill learning in our daily lives, the link between neural reorganization and skill acquisition is still largely unknown. This CAREER proposal lays the foundation for a long-term integrated research and educational program that will discover the links between cortical reorganization and skill acquisition and determine the behavioral factors that drive skill learning. An improved understanding of the science behind skill learning will have long-term impact on our clinical understanding of the progression of various motor control disorders, such as Parkinson's disease and stroke, and may inform the design of targeted rehabilitation paradigms and brain-computer interface systems for those patient groups.

Learning is a fundamental principle of brain operation that impacts every aspect of neural function. Long-term practice has been accompanied by a reorganization of cortical activity in a number of brain areas, but it is typically only imaged at the level of millions of neurons. The research proposed here will explicitly probe how changes in the properties of individual neurons are linked to skill acquisition. To accomplish this goal, this proposal leverages a brain-computer interface (BCI) skill learning task that enables precise investigation into the links between neural activity and behavioral improvement. With a BCI, it is possible to create a mapping between the activity of neurons and the movement of a computer cursor. To achieve dexterous control of the cursor, BCI subjects must practice with a particular neuron-to-cursor mapping to learn how to structure their neural activity to achieve desired movements. We will train Rhesus macaques to operate BCIs and give them new skills to master by presenting them with new BCI mappings. We will then track the reorganization in neural activity that occurs over days and weeks as they gain skilled control of the device. Critically, these mappings will be tailored to test various hypotheses about the behavioral drivers (i.e., visuomotor error, visuomotor bias, and movement efficiency) of cortical reorganization. The results of this study will thus provide an unparalleled view into the neural instantiation of skill development over its entire time course.

Project Summary Behavior and cognition emerge from the coordinated activity of populations of interacting neurons. To change behavior we must change the architecture of the network that drives behavior. What are the rules whereby the activity of populations of neurons can change during learning? If we could observe the neural population dynamics that underlie skill learning, we might be able to facilitate learning. This ability may eventually lead to improvements to neurally- based rehabilitation strategies that can facilitate the recovery from stroke. We will use a neurofeedback paradigm to shape the neural population dynamics that underlie skill learning. To do this, we will record the activity of dozens of neurons in the motor cortex of Rhesus monkey subjects. Animals will control a cursor on a computer screen by generating neural command signals. This is neurofeedback because the animal directly observes a projection of his neural activity, in the form of the movement of the onscreen cursor. This paradigm allows us to study learning simply by perturbing the mapping from neural activity to cursor movement on the screen. Following such a perturbation the animal must discover how to generate new patterns of neural activity that are now appropriate to restore good control of the onscreen cursor. We will ask two linked questions. First, how does the animal learn to generate new patterns of neural activity? And second, can we facilitate that process? Neurofeedback-based learning offers complementary advantages to arm movements for studying the neural population dynamics that accompany learning. Chie?y, only in a neurofeedback paradigm can we be certain that the learning-induced changes in neural activity we observe matter directly for behavior, because only the neurons we record directly impact behavior in this paradigm. We will test classic theories of skill learning, converted into speci?c hypotheses about how neural activity patterns will change throughout the multi-day course of skill learning. If our neurofeedback-based incremental training schemes do facilitate learning, then this research will inform the work of rehabilitation specialists who work with stroke patients. We (and others) believe that if neurofeedback-based therapies are coupled with standard behavioral therapies for stroke, rehabilitation outcomes will improve.

Humans are not, by nature, logical creatures. It takes focus to maintain our composure and not let emotions color our judgement. When we can control our emotional state, we can get “in the zone” and perform well. Failing to do so, we’ll “have a bad day”, or just be “off”. Why does this happen? And, in terms of neurobiological mechanisms, how does this happen? Our emotions are regulated by internal states, such as arousal, attention, and motivation, brain-wide modulatory processes that impact neural function related to perception, decision making, and action. What are the neural mechanisms of those interactions? This project will explore the interactions between internal states and cognitive processing in the cerebral cortex. The investigators will leverage their expertise in “brain training” by giving subjects visual feedback about their neural activity so that they are directly aware of their internal states. In this way, they will study whether subjects are able to better regulate their internal states so that they are able to make perceptual judgments and perform motor skills more consistently at a high level of performance. The investigators will also organize workshops to bring together experts in areas related to this project, train researchers to become well-versed in experimental and computational neuroscience, and enhance the participation of undergraduates, women, and underrepresented minorities in the research.

This project involves three integrated research threads. First, the investigators will use multi-electrode recordings in several regions of the cerebral cortex simultaneously to identify brain-wide signatures of internal states and their effect on the communication between cortical areas. Second, they will train subjects to volitionally control their internal states using neurofeedback. Third, they will examine whether subjects can harness their internal states to accelerate learning and improve performance on challenging perceptual and motor tasks. In these studies, they will focus on three types of internal states -- one that guides us in the spatial world around us (spatial attention), one that manages our alertness throughout the day (arousal), and one that aids our effort in focusing on what lies ahead (motivation). They will study how these internal states interact and to what extent they can be volitionally controlled in three areas across the brain: visual area V4, prefrontal cortex, and motor cortex. Together, their work will provide i) a unified account of the impact of multiple internal states on brain-wide neural computations spanning perception and action, and ii) neurofeedback paradigms to enable subjects to harness their internal states for improved performance.

This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Biology (BIO), Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.