Rajesh P. Rao - US grants

EIA-0130705 -University of Washington-Guang R. Gao-Adaptive Neurally-Inspired Computing: Models, Algorithms, and Silicon-Based Architectures

Rapid advances in silicon technology over the past few decades have allowed digital
devices to achieve ultra-high speeds in numerical computation. However, a majority of
these devices are prone to catastrophic failure when confronted with circumstances
unforeseen at programming time. Endowing such devices with the ability to adapt and
learn from experience is rapidly becoming a problem of fundamental importance in
information technology. We propose a new approach to solving this problem: building
information technology systems based on neurobiological computation and learning.

We intend to achieve this goal by developing computational models of plasticity and information processing in neurons and networks of neurons in selected sensory and motor areas of the brain; testing these models and their corresponding algorithms in software-based simulations, and designing real-time implementations of these algorithms in silicon using synapse transistors and field-programmable learning arrays (FPLAs).

We expect our research to provide a better understanding of computation within neuronal
networks, and to lead to a new generation of adaptive neuromorphic devices that could be
used for a variety of information technology applications, ranging from signal processing
and pattern recognition to ubiquitous computing and robotic control.

The goal of the project is to bridge the gap between human and machine vision by designing active vision systems modeled after neurobiology. Such systems can benefit from the ability to make eye, head, and body movements for active sensing, and can learn robust models of the visual world directly from interaction with the environment. Computational models of active vision will be formulated within the context of neurobiological data, addressing the problems of object identification, detection, attention, tracking, modeling self-motion, and oculomotor learning. An anthropomorphic real-time binocular active vision system will be designed based on commercially available camera platforms and real-time image processing hardware. The performance of the approach will be evaluated on both wheeled and legged robots in tasks involving visual navigation and multi-robot collaboration. The education part of the project includes development of an interdisciplinary curriculum in computational neuroscience.

Graphical images (line graphs, bar charts, diagrams, illustrations, etc.) are prevalent in math, science, and engineering (MSE) textbooks at all educational levels. But while studies have shown that tactual perception is the best modality for comprehension of graphical images by people who are visually impaired, the graphical images found in textbooks typically aren't available in this format. Visually impaired students' lack of full access to the contents of textbooks impedes their learning, development, and success in MSE careers, areas in which individuals with disabilities are underrepresented. This project seeks to address this problem, by developing innovative ways to overcome obstacles to the timely translation of graphical images into a tactual format. The needs of two user communities will be addressed: transcribers who translate graphical images into tactual formats within low- and high-production environments; and students who are in MSE classes at the K-12 and postsecondary education levels, are blind, and read Braille. To these ends, the PI has assembled an interdisciplinary team with expertise in image processing, machine learning, IR, HCI, experiment design, and addressing the needs of students with disabilities. The PI and his team will design and develop the Tactile Graphics Assistant (TG Assistant), a set of plug-ins for Adobe Photoshop and Illustrator, which will support transcribers in transforming, as automatically and intelligently as possible, graphical images into a high-quality tactual form that can be reproduced and then used by students who are blind. Empirical studies will be conducted to better understand the perception of tactile graphics, and to inform the design of prediction models to estimate image comprehension time and comprehension accuracy, the application of machine learning techniques to classify images by their type, and the design of image processing algorithms to carry out the steps (appropriate for the image type) to translate an image into a tactual form. A user-centered design approach will be followed during the development of the TG Assistant. Project benefits will be documented by three proof-of-concept activities, wherein the TG Assistant will be used to provide access to textbook images to three students at the K-8, high school, and postsecondary education levels.

Broader Impact. Tactual access to graphical images will improve blind students' learning, performance, retention, and potential to succeed in MSE careers. Project outcomes will include invaluable data on the tactual perception of tactile graphics, new image processing and classification algorithms, and a usable tool to streamline the translation of graphical images into a tactual format. Research findings on the translation of graphical images will be summarized as a set of guidelines and distributed to transcribers. The PI will make an effort to incorporate the guidelines and TG Assistant into transcriber training programs. The research team includes individuals from under-represented groups (women, minority, and disabled individuals.

The overall goal of this project is to study learning through imitation and endow robots with the ability to learn in this manner. This cross-disciplinary project has two major goals. The first is to create models for imitation learning in robots that combines techniques from Artificial Intelligence and Bayesian machine learning with insights from cognitive and psychological studies of imitation. The project will use these models to develop a humanoid robot that can learn by watching humans perform specific tasks. The second is to determine what characteristics of a humanoid robot can cause human infants and toddlers to imitate it. This will help shed light on the question of whether infants ascribe intentions to robots. The PI will collaborate with cognitive psychologist, Dr. Andrew Meltzoff also from the University of Washington. The project will foster collaboration between students from both of their labs and provide them with training in carrying out interdisciplinary research.

One of the most outstanding problems in science today is how the activities of the ten billion or so neurons in the human brain allow a person to perceive, think, and act in an intelligent and adaptive manner. Knowing the answer to this question would allow the design of radically new technologies with adaptive capabilities that would far outstrip the capabilities of technologies existing today. Recent behavioral and neurobiological experiments have suggested that the brain may rely on probabilistic principles for perception, action, and learning.
The goal of the proposed research project is to develop a rigorous probabilistic framework for neural computation and to test the resulting models in two ways: (1) in collaborative biological experiments, and (2) in applications involving robotics and brain-machine interfaces. Our specific research goals include:
1. Probabilistic Models of Neural Computation: We will develop new models of neural computation based on treating the problems of sensory information processing and action selection as probabilistic inference problems. We will investigate how biological models such as networks of integrate-and-fire neurons can represent probability distributions and how the propagation of neural activities in such networks can implement algorithms for probabilistic (Bayesian) inference of unknown quantities. We will also explore the connections between well-known neurobiological rules governing synaptic plasticity and statistically-derived learning rules.
2. Experimental Validation using Electrocorticographic Studies: Our models of Bayesian inference will be tested by co-PI Ojemann's group in experiments involving electrocorticographic (ECoG) signals recorded from the human brain in consenting patients being monitored in the days prior to brain surgery. Experiments will focus on testing the predictions of our models in tasks involving visual discrimination, recognition, and sensorimotor integration. Results from the experiments will be used to refine existing models and develop new probabilistic models inspired by neurobiological data.
3. Applications in Probabilistic Robotics and Brain-Machine Interfaces: We will test the robustness of our probabilistic models by implementing the corresponding algorithms on an existing humanoid robot in PI Rao's laboratory. We will be focusing primarily on sensorimotor integration and inference of actions for stable control of movements. Simultaneously, we will explore the applicability of our probabilistic models to brain-machine interfaces. The specific goals are to control a cursor on a computer screen and control a 4-degrees-of-freedom robotic arm by probabilistically inferring real and imagined movements from ECoG signals in real time.
The educational component of the project involves interdisciplinary training for one graduate student, research experiences for undergraduates, and curriculum development in the form of a new graduate level course on brain-machine interfaces.

Intellectual Merit: The proposed research represents one of the first interdisciplinary efforts to develop and test a rigorous probabilistic framework for understanding neuronal computation in the brain. Also novel is the application of neurally-inspired probabilistic models to robotics and brain-machine interfaces, two areas that could benefit tremendously from the robustness and adaptability afforded by such models.
Broader Impact: If successful, this research will lead to a new understanding of computation in the brain, offering unique insights into the mechanisms underlying human behavior and cognition. The application to brain-machine interfaces could dramatically improve the quality of life of paralyzed and disabled patients. The grant will enable the training of a graduate student in a multidisciplinary environment. Promising undergraduates, including students from underrepresented groups, will be paired with graduate students, providing valuable research experience for the undergraduates and mentoring experience for graduate students preparing for industrial and academic careers.

To unravel the neural mechanisms governing cognition, one must understand how different brain areas interact with one another on time scales that range from tens to several hundreds of milliseconds. Temporal resolution in the range of milliseconds is hard to achieve through imaging techniques such as fMRI. On the other hand, electrophysiological techniques used in primates provide high temporal resolution but only record from a single or at most a few tens of neurons at a time. An alternative recording technique that overcomes many of these problems is electrocorticography (ECoG) where an array of electrodes, implanted for assessment of the brain before surgery, is used to record electrical fluctuations from the surface of the human. ECoG allows electrical signals from several different brain areas to be measured simultaneously while at the same time providing temporal resolution in the millisecond range. It is thus uniquely suited for probing the neural dynamics of cognition.

With NSF support, Drs. Rajesh Rao and Jeff Ojemann of the University of Washington will examine cortical dynamics using three tasks that represent three different levels of abstraction in cognition: a motor movement task, a working memory task, and a language task. It will analyze the data using conventional spectral techniques as well as more sophisticated statistical techniques such as Independent Component Analysis (ICA) for source separation and Bayesian techniques for signal estimation. From this, they will construct biophysical models of networks of neurons and simulating cortico-cortical and cortico-thalamic feedback loops to understand the neural genesis of ECoG. If successful, this research will allow a new understanding of brain function, leading in the long term to possible remedies for cognitive deficits involving motor control, memory, or language processing.

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Rao


Brain-machine interfaces (BMIs) are devices that allow a subject to control objects directly using brain signals. Such devices offer the potential to significantly improve the quality of life of locked-in, paralyzed, or disabled individuals by allowing them to communicate via virtual keyboards and control prosthetic robotic devices. The two dominant paradigms for brain-machine interfacing today rely on non-invasive recording from the scalp (EEG) and invasive techniques based on intracortical implants. EEG signals are extremely noisy, thereby limiting the bandwidth of control signals that can be reliably extracted. Intracortical implants on the other hand yield stronger signals but pose serious health risks.

In this proposal, the PI describes a research program for investigating BMIs based on electrocorticography (ECoG), a relatively new technique that involves recording signals subdurally from the brain surface. These signals have much higher signal-to-noise ratio than EEG signal while at the same time, pose lesser risks than techniques that penetrate the brain surface. The proposed research will address the following key issues:

(1) Exploiting high frequency ECoG signals for BMI: Recent work has shown the existence of broad-spectral ECoG changes at high frequencies during movement and imagery. The PI and his team will explore the application of such ECoG modulation for multi-dimensional control in BMIs.
(2) Neural plasticity of local cortical circuits during BMI: The PI's team will investigate the dynamic range of the spectral changes in ECoG and analyze the adaptations that occur due to brain plasticity during BMI control. This will help pave the way for controlling 3 or more degrees of freedom in a BMI from a single control electrode.

(3) Abstraction of control signals: After extended periods of BMI use, many patients report no longer imagining moving a control limb but rather concentrating on the desired result of the BMI task itself. The PI and his team will explore the creation of new cortical communication pathways underlying such abstraction and leverage these new control signals in expanding the bandwidth of the BMI.
(4) Applications of new control signals to novel BMI paradigms: The BMI techniques will be tested using virtual devices such as cursor-driven menu systems for communication as well as more complex robotic systems such as a prosthetic robotic hand and a humanoid robot.
The educational component of the project involves curriculum development, interdisciplinary training for graduate and undergraduate students, and outreach to K-12 students.

Intellectual Merit: The proposed research represents one of the first efforts to exploit ECoG and the brain's plasticity to build BMIs that can control devices with large degrees of freedom. The study of abstraction of control signals and its application to robotic BMIs is also novel.
Broader Impact: If successful, this research will lead to new ECoG-based BMI systems that will surpass the abilities of current BMIs by relying on the brain's ability to adapt to novel control scenarios and leveraging the large-scale population-level electrical activity measured by ECoG. The project will enable the training of graduate students in a multidisciplinary environment. Promising undergraduates, including students from underrepresented groups, will gain valuable research experience in preparation for industrial and academic careers. A K-12 outreach effort will enable students from local area schools to visit the laboratories of the PIs and gain hands-on experience in the emerging field of brain-machine interfaces.

Over the last decade, the field of neural engineering has demonstrated to the world that a computer cursor, a wheelchair, or a simple prosthetic limb can be controlled using direct brain-machine and brain-computer neural signals. However, technologies that allow such accomplishments do not yet enable versatile and highly complex interactions with sophisticated environments. Today's intelligent systems and robots can neither sense nor move like biological systems, and devices implanted in or interfaced with neural systems cannot process neural data robustly, safely, and in a functionally meaningful way. Doing so requires a critical missing ingredient: a novel, neural-inspired approach based on a deep understanding of how biological systems acquire and process information. This is the focus of this proposal.

The NSF ERC for Sensorimotor Neural Engineering (ERC/SNE or "Center") will become a global hub for delivering neural-inspired sensorimotor devices. Using devices that mine the rich data in neural signals available from implantable, wearable, and interactive interfaces, the ERC/SNE will build end-to-end integrated systems. Examples include: implantable neurochips that can activate paralyzed limbs by electrically stimulating muscles or nerve roots; stationary robots that extract neural signals from a user's touch to provide home-based, post-stroke therapy; neural-controlled adaptive prosthetic limbs that provide sophisticated sensory feedback, and wearable caps that control external exploration devices. Unlike traditional approaches that stress accommodation to the needs of people with neurological disabilities, the ERC/SNE will focus on proactive technologies that provide seamless and adaptive person-machine interaction. It will accomplish this mission with three core engineering thrusts: (1) communication and interface design for devices and data management, (2) reverse and forward engineering of neural systems and neural-inspired devices, and (3) control and adaptation technologies that express sensorimotor functions for individual needs.

The ERC/SNE will nurture future global multidisciplinary leaders. It will develop middle and high school project-based curricula that introduce neural engineering principles to students underrepresented in engineering. It will create multi-institution, undergraduate and graduate Neural Engineering courses with new degree structures and develop vertical research mentoring chains to build a strong research culture from faculty to K-12. It will build long-lasting and deep relationships through faculty and student exchange programs across all disciplines and partnering institutions, with a goal of removing barriers in communication across different fields, countries, and diverse backgrounds. The neural engineering field creates new pathways from the less quantitatively-based biological sciences to the more quantitatively-based engineering fields as well as pathways for people with disabilities to work in an engineering field that addresses their own experience and needs. The women and underrepresented minorities who currently account for over 40% of the Center's leadership team will serve as role models for students and starting faculty. Further, the ERC/SNE will extend its impact by identifying key technologies according to market significance and technical risk. The Center's portfolio will be constructed to deliver a steady stream of innovations over the near and long term. Its industry partnership structure includes not only small and large firms that will help shape Center IPs, but also hospitals and investment firms that will ground research activities to technologies that will truly assist people in need and steer future neural engineering market directions.

The ERC/NSE will strive to enhance the human experience both for persons with neurological disabilities and for the coming generation of global and diverse engineering innovators. The Center's seasoned, multi-disciplinary team will transform healthcare, manufacturing, and the educational infrastructure to guarantee neural engineering global leadership.

Humans are extremely adept at learning new skills by watching and imitating others. Attempts to endow robots with a similar ability have failed to generalize beyond specific tasks, partly because the focus has been on following the trajectory of an action demonstrated by an expert.

The current project investigates a new interdisciplinary approach to imitation learning that is inspired by how humans learn via goal-based imitation. The project's specific objectives include: (1) a new method for imitation based on inferring the underlying goals of human actions rather than following trajectories: actions are executed based on sequences of inferred goals and successfully executed action sequences are cached as higher level goals, leading to hierarchical goal-based imitation; (2) a new approach based on hierarchical Bayesian models (HBMs) is proposed for generalization across objects and tasks, and (3) developmental studies of goal-based imitation learning are proposed for testing predictions of the project?s models in imitation learning experiments with children.

The project represents one of the first efforts to develop rigorous probabilistic models of goal-based imitation learning based on insights from human learning. The results are expected to pave the way for a new generation of machines that can interact fluently with humans, learn new skills from human teachers, and cooperatively solve problems with human partners. The project also provides graduate and undergraduate students with multidisciplinary training in computer science and cognitive science, with K-12 outreach activities aimed at encouraging students from underrepresented groups to pursue careers in science and engineering.

a technical description
The project proposes a joint NSF-DFG workshop titled "New Perspectives on Neuroengineering and Neurotechnologies" to foster research collaborations with other researchers around the world. The NSF ERC Center for Sensorimotor Neural Engineering (CSNE) was established at the University of Washington in 2011, with the mission to forge both physical and conceptual connections between neural systems and devices to develop integrated systems that may help people with neurological and mobility deficits such as stroke, traumatic brain injury, spinal cord injury, cerebral palsy, paralysis, and limb loss. The CSNE seeks to connect a mathematical understanding of how biological systems acquire and process information with the design of effective devices that interact seamlessly with humans. These devices take input from implantable, wearable or interactive interfaces to build integrated systems that provide sensorimotor solutions for the disabled and elderly population. The CSNE research builds on expertise in computational neuroscience, brain-computer interface, robotics, control theory, and microelectronics/wireless technology. The confluence of neuroscience and engineering presents a unique set of challenges to address. To contribute to the advancement of this interdisciplinary field, the CSNE has partnered with the German Research Foundation Cluster of Excellence BrainLinks-BrainTools to advance the field of neural engineering. The CSNE and BrainLinks-BrainTools have common scientific interests especially in the design of brain controlled interfaces and have forged a working partnership including collaborative research projects and student exchange programs.
Intellectual Merit of the proposed project focuses on a collaborative scientific workshop that will bring together experts from around the world to discuss the questions, challenges and opportunities in the field of neural engineering. The mission of the researchers involved with the workshop is to develop innovative ways to connect a deep mathematical understanding of how biological systems acquire and process information with the design of effective devices that interact seamlessly with human beings. This singular approach reverse engineers the nervous system?s sensorimotor functions to develop engineering models that correct or compensate for neural deficits and augment neural capabilities. Using these mathematical and structural models, it is possible to design neural interfaces integrated with external control devices.


a non-technical explanation
Participants will gain valuable knowledge in areas of research that have significant and growing global impact on the quality of life. Publication of the workshop proceedings in leading scientific journals will help disseminate the findings of workshop attendees. This effort will likely lead to new collaborations and joint research projects that will benefit society by reducing the emotional, physical and financial toll caused by sensorimotor disorders. The workshop may identify key technologies according to market significance and technical risk thereby impacting innovations over the near and long term.

A great deal of research with adults has documented the presence of body maps in the human brain. These neural maps have an organized spatial layout. Neighboring parts of the body are connected in an orderly fashion to areas of the brain that process touch and movement. Body maps are important for many aspect of everyday life including the sense of one's own body and controlling our movements. Body maps also likely play an important role in learning from others, through allowing us to register similarities between ourselves and other people. Despite the importance of body maps, very little is currently understood about how they develop in the early months and years of life. The research supported by this award would provide significant new information on the development of body maps and their relation to early learning. The award supports a collaborative, cross-disciplinary network of investigators who will combine expertise in developmental psychology and infant learning, brain science, cognitive science, computer modelling, and robotics. The proposed network will also support the development and training of junior investigators through specific activities designed to expose them to the benefits of an interdisciplinary approach.

Advances in methods for safely measuring the brain activity of human infants are allowing new questions to be asked concerning the role of body maps in early learning. The proposed research involves using magnetoencephalography (MEG) to non-invasively measure responses of the infant brain to tactile stimulation of different parts of the body (e.g., hands vs. feet), and to relate these responses to aspects of infant learning. Another set of studies involving electroencephalography (EEG) will examine how body maps facilitate early imitation and learning from others. Insights from these studies will inform (and be informed by) a further strain of research using computer modelling that takes bodily factors into account in designing robotic systems that can learn from people. The research questions will also provide insight into the control of brain-computer interfaces that can assist disabled individuals in learning to control artificial limbs and other external devices.

Discovering the neural mechanisms of human social behavior could have a profound impact on public health, with the potential to shed light on complex disorders such as those involving paranoid, antisocial, or anxiety-related behaviors. By leveraging probabilistic computational models and model-based multimodal neuroirnaging experiments, our research will lay the foundation for a comprehensive multidisciplinary understanding of the computational, psychological, and neurobiological basis of social behavior in humans. Our main hypothesis is that when we are in an interactive social setting, our brain performs Bayesian inference of the intentions and cooperativeness of others using probabilistic representations. We propose to employ and extend the framework of partially observable Markov decision processes (POMDPs) to model prediction of hypothetical action outcomes, intentions of others, whether another human is cooperative or competitive, and to choose the best actions. This theoretical framework will be tested in parallel experiments in humans exploiting maximally the experimental advantages of model-based fMRI and intracranial recordings. The specific aims are to: 1. Develop a multi-agent social POMDP model that allows other agents to be probabilistically modeled and that prescribes how optimal actions can be selected in s.ocial contexts; 2. Test the predictions of the POMDP model regarding belief inference by using simultaneous fMRI and. intracranial local field potential (LFP) recordings in patients to characterize the neural mechanisms underlying inference of another's intended actions and beliefs; 3. Test the predictions of the model with regard to learning optimal actions in social contexts using the Public Goods Game (PGG) and fMRI in humans; 4. Investigate neural population~level implementation of the POMDP model using recordings of LFPs in patients and fMRI in healthy individuals in two social hierarchy learning tasks. RELEVANCE (See instructions): Understanding the neural mechanisms of social behavior remains an important open question in neuroscience. The proposed research will provide a comprehensive multidisciplinary understanding of the basis of human social behavior. Public health implications include achieving a better understanding of human behavior in groups as well as abnormal social behaviors related to paranoia, trust, or anxiety.

Much knowledge about how human brains process information and generate actions has been informed by carefully controlled experiments in laboratory settings. However, understanding the brain in action requires exploration of its functions outside structured tasks. The current project explores neural processing over many days using large-scale recordings of brain activity augmented with video, audio and depth camera recordings, all simultaneously and continuously monitoring a subject. Importantly, unlike the majority of existing studies, here the subjects receive no instructions but are simply behaving as they wish in their hospital room-including eating, sleeping, and conversing with family. The project will advance data-intensive science and human neuroscience, leveraging external monitoring of the subjects to interpret naturalistic neural activity. The results of this project will be catalytic in understanding of the human brain, opening the door to study of brain function outside the structured confines of laboratory experiments.

The neural decoding algorithms developed will be directly applicable to current Brain-Computer Interfacing (BCI) technologies, enabling the deployment of systems that can predict the user's needs and improve quality of life outside the laboratory. Further, ongoing collaborations with neurosurgeons focus on evaluating this novel data-intensive approach to ethological brain mapping and how it may complement existing clinical functional brain mapping. The project will support and enable the education of students at the intersection of data science and neuroscience, including training scientists at the undergraduate, graduate, and post-doctoral career stages. Results from the research will be distributed as open access publications and code repositories, supporting a commitment to reproducible science.

This proposal focuses on data-driven innovations to enable more accurate decoding and inference of actions from long-term, naturalistic neural recordings. The first aim proposes to develop algorithms for automated decoding of natural motor and speech behaviors. Unsupervised clustering will be used to discover coherent patterns in brain activity, and clusters will be annotated with behaviors automatically parsed from external monitoring streams. Motivated by the size of the dataset and substantial variety between individuals, this scalable computational approach circumvents tedious manual annotation and fine-tuning of parameters. The second aim proposes to infer networks of dynamic causality of cortical networks engaged in task-free, naturalistic behaviors. This aim focuses on testing the hypothesis that neural correlates of naturalistic behaviors differ from those of repeated, instructed behaviors. Functional networks and the dynamic causality of cortical areas will be explored using methods from nonlinear dynamical systems theory. These networks will be compared to results from clinical brain mapping. This project will improve state-of-the-art neural decoding in naturalistic contexts and uncover neural correlates of task-free behaviors in humans.