Gregory A. Clark - US grants

DESCRIPTION (provided by applicant): We propose a series of coordinated computational and electrophysiological studies to identify, analyze, and implement contextual spike-timing codes in the encoding of light intensity, using a well-understood, simple visual system (the Hermissenda eye) that permits detailed cellular mechanistic analyses and biologically realistic, Hodgkin- Huxley level simulations. We further propose to examine mechanisms by which neural "noise" paradoxically improves, rather than degrades, light intensity encoding; and the interactions of contextual spike-timing codes with learning-related synaptic plasticity. Implementation experiments to restore network function by explicitly introducing contextual spike-timing relationships may provide powerful, novel evidence regarding the functional consequences of these codes. Specific Aim 1: Role of Network Architecture. Pilot simulation and physiological data indicate that network interactions (feed-forward, feedback, and lateral inhibition) alter contextual spike-timing relationships and may contribute substantially to improved information processing in the network, relative to single cells. We will examine the existence, origins, and consequences of these codes in both the simulated and biological eye. Specific Aim 2: Encoding of Light Intensity. Preliminary evidence indicates that an increase in light intensity produces an increase in both the rate and synchrony of type A photoreceptor spiking, suggesting multiple encoding schemes with potentially complementary roles. Further, the addition of synaptic and ionic neural "noise" improves both rate and synchrony encoding of light intensity. We propose to quantify and compare rate and contextual spike-timing codes across light intensities; examine how their performance is affected by the presence of neural noise; and implement relevant algorithms into a neural interface (or simulated interface) to determine whether they improve control of the system and light intensity encoding. Specific Aim 3: Contextual Spike-Timing Codes and Learning. Preliminary data indicate that synaptic facilitation at B-to-A connections alters the timing relationships between these photoreceptors, and that noise improves rather than degrades the reliability of type A cell responses to enhanced synaptic input. We propose to examine the role of learning-related synaptic plasticity in contextual spike timing encoding, including its effects on light intensity coding and mechanisms of noise-induced enhancements. Taken together, these studies will elucidate neurobiological strategies for information processing, and identify efficient bio-based solutions that may be advantageously incorporated into artificial intelligence systems, robotic systems, or clinical neuroprostheses.

Optical methods are becoming established in the fields of neuroscience, medical imaging and diagnostics, etc. Optogenetics, for example, despite being a nascent field of study, has been named the Method of the Year 2010 by Nature Methods. We propose to develop and test a novel device structure to facilitate three-dimensional deep-tissue light penetration and collection with capabilities for simultaneous spatiotemporal modulation of different wavelenghts to advance a broad range of applications in optical neural stimulation and recording. A 3D optrode array consisting of optically transparent needles can penetrate >1 mm directly into tissue, thereby creating multiple independent paths for light propagation that avoid attenuation due to tissue absorption and scattering. We will develop SiO2 arrays suitable for visible and even NIR applications.
The intellectual merits of this research lie in the addressing the barrier for nearly all modes of optical excitation where penetration depth is determined by optrode length, not by wavelength. We propose to leverage off of the extensive body of microfabrication methods developed for penetrating electrodes to achieve the same advantages for optical delivery and reception as compared to external approaches.
Broader Impact: Multiple, broadly enabling, and potentially transformative, impacts may emerge from this work. Although our focus is on optogenetic neural stimulation and recording, optrode array devices have application in basic neuroscience research, highly selective photodynamic therapy, and deep tissue imaging for diagnostics and therapy. From an applied neuroscience and neuroengineering perspective, the optrode array device will facilitate deeper access into neural tissue, such as axon bundles within the fasicles of central or peripheral nerves. Deeper access across multiple stimulation/recording sites may enable restoration of lost motor or sensory function after nervous system disorders or disease. Potential representative applications, among many, include restoration of hand grasp or stance after paralysis, and restoration of cutaneous and proprioceptive sensory feedback after limb loss. From an educational perspective, the inherently interdisciplinary and interactive nature of the proposed research will provide unique opportunities for training in biophotonics, microfabrication, neuroengineering, and basic neuroscience, and will interact synergistically with ongoing major research and educational initiatives at the University of Utah. From diversity and outreach perspectives, the established success of the Bioengineering Department (of which the PI is an adjunct-faculty member, and the co-PI a tenure-track member) in attracting and mentoring female engineers, along with recently enhanced College- and University-wide outreach and recruitment efforts, will help bring underrepresented populations into the emerging neuro-engineering growth area.

Most ongoing research to develop advanced hand and arm prostheses that allow an individual with amputation to perform highly dexterous movements and to sense features of objects in the world has separated the performance of movements from the sensing of objects. However, in individuals with normal ability, sensory and motor information are integrated. This project's goal is to create movement and sensation abilities in individuals with amputation that are more similar to that in normally enabled individuals. In particular, the goal is to understand how to best utilize all of the available motor and sensory information in the design of accurate neural controllers for future generations of forearm prostheses. This integrated approach to creating sensory percepts and interpreting motor intent will recreate the natural, minimally monitored use of an artificial hand in volunteers as they interact with their world. Successful completion of the proposed work will begin the process of creating a prosthetic hand that passes the 'Turing Test,' where the user perceives little difference between the artificial hand and their amputated biological hand.

The program of research involves implanting neural interfaces (for both stimulating and recording) in the major arm nerves of human volunteers and investigating use of these interfaces while the volunteers interact with a virtual world. In particular, the project has the goals of (a) identifying components in peripheral nerve signals that correlate with errors between the intended movement and the actual movement of the prosthetic arm; (b) developing accurate encoders and stimulation systems that provide punctate, naturalistic, multimodal, and graded sensory percepts; and (c) developing, characterizing, and evaluating advanced decoders that estimate movement intent from a combination of peripheral nerve signals and sensory information from the prosthesis. This third goal, an advanced decoder, will be evaluated with scenarios that provide a clear measure of performance and that represent activities of daily living. The integration of sensory percepts and motor intent is expected to create a natural, minimally monitored use of a prosthetic hand in order to interact more effectively with the world.

It is estimated that approximately 5.4 million people in the United States live with some form of paralysis, defined as a central nervous system disorder resulting in difficulty or inability to move the upper and/or lower extremities. Many paralyzed individuals consider restoration of lost basic motor functions such as grasping and walking as important abilities that could improve their quality of life. The goal of this project is to develop and evaluate advanced machine learning algorithms that enable quadriplegic individuals to control a robotic hand with heterologous muscles (that is, muscles not typically involved in moving the limbs; for example, muscles of the neck). To enable this research, prospective algorithms will be initially evaluated in normally-enabled individuals. The most promising algorithms will subsequently be evaluated in quadriplegic individuals. This research is a first step toward providing benefit to the paralyzed community by creating pathways toward the development and commercialization of functional motor prosthetic systems. Paralyzed individuals can be trained to use the system by planning the movements in their minds, much like moving their natural limbs. Success of this research could lead to a significant advance in improving function and quality of life for individuals affected by stroke or spinal cord injury. High-School students as well as undergraduate and graduate students will be trained on this multi-disciplinary project.

This research involves learning human intent from biological signals, extracting higher-level goals using sensors embodied in the patient, and developing controllers for motor manipulation based on estimated motor movement intent and higher-level goals. Specific sub-goals proposed to achieve the overall goal of the project include: a collaborative brain-machine learning system that trains the human brain to remap limb movement control to heterologous muscles while simultaneously training the machine to interpret the movement intent from surface electromyograms of the heterologous muscles; algorithms to extract higher-level movement goals using biologic and auxiliary sensor signals; shared brain-machine controllers of robotic hands using the extracted goal and decoded movement intent; and experimental assessment of the capabilities of the methods on individuals with paralysis of the upper limbs. In addition to the innovations in the development of motor prostheses for people with paralysis of the limbs, the proposed research will provide new insights into online learning in nonlinear and time-varying environments, collaborative brain-machine learning, and shared brain-machine control algorithms for motor prostheses.

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.