Behtash Babadi - US grants

The ability to adapt to changes in the environment and to optimize performance against undesirable stimuli is among the hallmarks of the brain function. Capturing the adaptivity and robustness of brain function in real-time is crucial not only for deciphering its underlying mechanisms, but also for designing neural prostheses and brain-computer interface devices with adaptive and robust performance. Thanks to the advances in neural data acquisition technology, the process of data collection has been substantially facilitated, resulting in abundant pools of high-dimensional, dynamic, and complex data under various modalities and conditions from the nervous systems of animals and humans. The current modeling paradigm and estimation algorithms, however, face challenges in processing these data due to their ever-growing dimensions. This research addresses these challenges by providing a unified framework to efficiently utilize the abundant pools of data in order to deliver game-changing applications in systems neuroscience.

Converging lines of evidence in theoretical and experimental neuroscience suggest that brain activity is a distributed high-dimensional spatiotemporal process emerging from sparse dynamic structures. From a computational perspective sparsity is a key ingredient in rejecting interfering signals and achieving robustness in neural computation and information representation in the brain. The main objective therefore is to develop a mathematically principled methodology that captures the dynamicity and sparsity of neural data in a scalable fashion with high accuracy. By focusing on the auditory system as a quintessential instance of sophisticated brain function, this research investigates several fundamental questions in systems neuroscience such as plasticity, attention, and stimulus decoding. The research is integrated with education and outreach activities including high school level hands-on workshops, undergraduate capstone projects, and interdisciplinary course development.

This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE). Neuroscientists have been remarkably successful in understanding the function of numerous brain regions by studying them in isolation and characterizing their individual roles in behavior. Growing evidence in recent years, however, suggests that sophisticated brain function emerges from the co-activation of multiple brain regions that exhibit networked activity. These networks organize rapidly in order to allow the brain to adapt to changes in the environment, resulting in robust behavior. Deciphering the neural mechanisms underlying these network dynamics is therefore crucial in understanding how the brain carries out cognitive processes such as attention, decision-making and learning. Recent technological advances in noninvasive neuroimaging have largely addressed the experimental challenges in studying these dynamic networks in humans and have provided abundant neural data under countless clinical and experimental conditions. However, the sheer high-dimensionality of these data together with the complexity of these networks has created various bottlenecks in data analysis, modeling, and statistical inference. In order to exploit the unique window of opportunity provided by the abundance of noninvasive neural data, this project is (1) developing a unified methodology for inferring the dynamics and statistical characteristics of these cortical networks, in a computationally efficient fashion, and (2) applying this methodology to magnetoencephalography (MEG) data from behaving human subjects to address several fundamental questions about auditory processing. This work brings new insight as to the dynamic organization of brain networks at unprecedented spatiotemporal resolutions, and can thereby affect technology in the areas of brain-computer interfacing and neuromorphic engineering. It also allows for the creation of engineering solutions for early detection and monitoring of cognitive disorders involving auditory perception and attention. The outcome of this project will be disseminated to the broader scientific community in the form of publicly accessible data analysis toolboxes accompanied with tutorials and webinars. The research plan is complemented by educational activities at the K-12, undergraduate, and graduate levels, including workshops, undergraduate projects, and course development, with an emphasis on the involvement of women and underrepresented minorities.

The existing paradigm for extracting cortical functional network dynamics faces challenges, including loss of temporal resolution due to the common sliding window processing, loss of spatial resolution due to the constraints of noninvasive recording, and statistical bias due to the heavy usage of linear estimation techniques given that network properties are intrinsically non-linear. This project provides a unified research plan for addressing these challenges, by combining high temporal resolution non-invasive recordings with high spatial resolution in a statistically robust way, using modern signal processing techniques. This methodology will specifically be applied to MEG data acquired from behaving human subjects, and will be used to decipher the neural mechanisms of adaptive auditory processing.

Project Summary To survive, organisms must both accurately represent stimuli in the outside world, and use that representation to generate beneficial behavioral actions. Historically, these two processes ? the mapping from stimuli to neural responses, and the mapping from neural activity to behavior ? have largely been treated separately. Of the two, the former has received the most attention. Often referred to as the ?neural coding problem,? its goal is to determine which features of neural activity carry information about external stimuli. This approach has led to many empirical and theoretical proposals about the spatial and temporal features of neural population activity, or ?neural codes,? that represent sensory information. However, there is still no consensus about the neural code for most sensory stimuli in most areas of the nervous system. The lack of consensus arises in part because, while it is established that certain features of neural population responses carry information about specific stimuli, it is unclear whether the brain uses (?reads?) the information in these features to form sensory perceptions. We have developed a theoretical framework, based on the intersection of coding and readout, to approach this problem. Experimentally informing this framework requires manipulating patterns of neuronal activity based on, and at the same spatiotemporal scale as, their natural firing patterns during sensory perception. This work must be done in behaving animals because it is essential to know which neural codes guide behavioral decisions. In the first phase of this project (funded by the BRAIN Initiative), we developed the technology necessary for realizing this goal. In the present proposal, we will extend our patterned neuronal stimulation technology and apply it to answer long-standing questions about neural coding and readout in the visual, olfactory, and auditory systems. We will pioneer the capacity to determine which neurons within a network are encoding behaviorally relevant information, and also to determine the extent to which temporal patterns of those neurons? activity are being used to guide behavior. Finally, we will study these neural coding principles across changes in behavioral state and during learning to determine how internal context and past experience shape coding and readout. The contributions of the proposed work will be three-fold. First, we will provide the neuroscience community with the tools needed to test theories of how neural populations encode and decode information throughout the brain. Second, we will reveal fundamental principles of spatiotemporal neural coding and readout in the visual, olfactory, and auditory systems of behaving animals. And third, our unifying theoretical framework for cracking neural codes will allow the broader neuroscience community to resolve ongoing debates regarding neural coding that have been previously stalemated by considering only half of the coding/readout problem.

Individual neurons are highly unreliable computational units in isolation, due to their drastic trial-to-trial response variability. Yet, when they act together as a network, they result in robust brain function and precise behavioral outcomes. The advent of large-scale neural recording technologies, such as two-photon calcium imaging, has created a paradigm shift by enabling scientists and engineers to study the activity of large populations of neurons in order to decipher how they collectively encode information from the external world and distill them to elicit robust behavior. In order to fully utilize these data, computationally efficient and mathematically principled techniques for robust network-level inference are required. The research objective of this proposal is to develop such methodologies to infer network-level characteristics of ensemble neuronal activity from two-photon imaging data, and to apply these methods to large-scale recordings in order to reveal the computational principles that underlie sensory processing and behavior.

The research approaches include: developing a robust framework for joint inference of the intrinsic and stimulus-driven correlations of neuronal activity, designing a functional taxonomy to characterize the relevance of neuronal activity to sensory processing and behavioral outcomes, and constructing an estimation framework for capturing the dynamics and functional relevance of higher-order synchronous neuronal activity. This project addresses several outstanding challenges faced by existing methodologies, including biased network characterization incurred by two-stage analysis pipelines, intermixing the contributions of exogenous and endogenous processes to collective neuronal activity, and studying sensory processing and behavioral elicitation as disjoint problems. By employing two-photon calcium imaging data from mice and zebrafish, the proposed modeling and estimation framework will be used to investigate several fundamental problems in systems neuroscience such as tonotopic diversity in the auditory cortex, interaction of sensory processing and decision-making, and visuo-motor coordination. The project is expected to impact technology by providing signal processing solutions to be used in neural control and neuromorphic systems. The research is also integrated with educational and outreach activities including high school level workshops, undergraduate involvement in research, and course development.

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.

Artificial intelligence is becoming ubiquitous in modern life. To build systems under the current paradigm, large amounts of energy are required for computing and sensing. This causes environmental problems, pollution, and challenges for small-sized systems, as well as privacy issues. The field of neuromorphic science and technology offers an alternative by seeking to understand principles of biological brains and build on their basis artificial systems using low-power hardware and software solutions. While its advantages have been demonstrated, further advances are necessary and will require common computational tools and principled experimental approaches. This AccelNet project, NeuroPacNet, links international experts in neuromorphic engineering with computational neuroscientists, roboticists, control theorists, and researchers of perception from seven global networks to set the foundations for building systems that can robustly process real-world signals in time and adapt to changes. This network of networks will facilitate the development of new methods and approaches for intelligent system design and prepare the next generation of leaders in neuromorphic science and technology. As different industries adopt neuromorphic hardware, society will have access to new applications, such as in computing on cell phones, neuroprostheses, intelligent hearing aids, and smart sensory systems with predictive capabilities.

NeuroPacNet will advance computational research on modeling the integration of perception, action, and cognition. The network of network will coordinate across those research thrusts and develop new approaches grounded in theoretical neuroscience for sensorimotor control, motor learning, event-based computations, and learning in spiking neural networks. NeuroPacNet will also include robotics research in the areas of drone navigation and human activity understanding for humanoids and will address social and ethical issues in humanoid robotics. The network of networks will use innovative hardware design and mixed signals computational systems to address computation for emerging and unconventional technologies. International collaboration and knowledge exchange will include an immersive research exchange program providing scholarships to students and postdoctoral researchers, an annual workshop to discuss common issues and concerns in a stimulating environment and to engage in hands-on projects, meetings to define challenges, opportunities, and actions to accelerate progress, and competitions with two challenges to be solved by teams of researchers and students. An interactive project website will become a portal for archived webinar talks, tools, and data.

The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts.

Co-funding for this project is provided by the Directorate for Social, Behavioral, and Economic Sciences.

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.