Malcolm A. MacIver - US grants

CBET-0828749
Patankar

The physical principles underlying the extraordinary mobility of swimming and flying animals have been the subject of years of effort and there is still much that is not understood. This study develops an efficient numerical method for fully resolved simulation of self-propulsion of organisms called Fully Resolved Momentum Redistribution for self Propulsion (FuRMoRP). It will be used to study swimming fish; however, it is sufficiently general to function for small flying animals as well. The motivation to develop such a tool is two-fold: first, to develop a high resolution efficient fluid simulation technology that is transformational by its potential to significantly impact many interdisciplinary areas; second, to gain insight into a number of fundamental problems in aquatic locomotion which will also lead to insights into the design of a novel, highly-maneuverable underwater vehicle being developed through a separate project in the Co-PI's lab. The maneuverability and efficiency of fish is inspiring new styles of propulsion and maneuvering in underwater vehicles for applications such as undersea exploration and environmental monitoring. The development of such vehicles will depend on the resolution of open issues in aquatic locomotion which will be studied here using FuRMoRP applied to three important swimming modes and fish morphologies. The PIs hope to answer specific questions: What is the most efficient deformation kinematics three given fish types? How do they compare with experimentally observed gaits? What are their comparative efficiencies? The education plan involves developing new graduate and undergraduate courses, fluid animations for explanation of biofluid-dynamic principles, a book project, and international outreach. For outreach, the PIs will work with the world renowned Shedd Aquarium in Chicago to help develop a more educational display of the electric eels. The display will provide real-time acoustic and visual cues to the visitors to help them appreciate some of the fluid dynamical science and beauty of the electric eels.

In the traditional view, the nervous system performs the computational "heavy lifting" in an organism. This view neglects, however, the critical role of biomaterials, passive mechanical physics, and other pre-neuronal or non-neuronal systems. Given that neurons consume forty times more energy per unit mass than structural materials such as bone, it is better, when possible, that biological systems employ relatively inexpensive structural materials rather than relying on more costly neuronal control. In this "bone-brain continuum" view, animal intelligence and behavioral control systems can only be understood using integrative modeling approaches that expose the computational roles of both neural and non-neural substrates and their close coupling in behavioral output. To this end, a group of researchers from Northwestern University, The Johns Hopkins University, and Harvard University propose to create a unique high fidelity neuromechanical model of a vertebrate. The effort is divided between the development of a general purpose computational tool set for neuromechanics research and application of these tools to an ideally suited model system, weakly electric knifefish.

The research will lead to breakthroughs in fundamental problems of how nervous systems work together with biomechanics to generate adaptive behavior. The final goal of the research is to construct an integrated neuromechanical model of a unique biological system - weakly electric knifefish - that places biomechanics and neural control on equal footing. Prior such neuromechanical models have used highly simplified models of mechanics and highly abstracted neuronal control approaches. This research advances the state of the art by incorporating high-fidelity mechanics with neuronal mechanisms motivated by direct neurophysiological evidence. Ultimately, this computational approach will help elucidate how animals distribute computations between brain and bone.

The decision to approach or avoid is a fundamental aspect of animal behavior. How this decision is made by networks of motor neurons that are located in the brainstem and spinal cord, and which trigger muscle cell contraction, is still unclear. This project will investigate the neural and mechanical basis of innate decision-making in vertebrates. Studies will be carried out using zebrafish larvae because they undergo a change in their innate decision-making ability during the first few days after hatching. Immediately after hatching, zebrafish larvae have the ability to generate escape behavior in response to threats. Three days later, they add the ability to not only avoid, but to approach and attack small objects. The goal of this project will be to determine how the neural circuitry supporting the decision to escape or approach visually detected objects is organized during development. Graduate students will be trained in the use of cutting-edge electrophysiological, imaging, computational and behavioral techniques uniquely applied in the zebrafish model system. In addition, an outreach program will be designed and implemented to introduce local high school students to basic neurobiological concepts addressed in this project. The program will involve intuitive and interactive experiments using simple robots with circuits that can be modified to create the approach or avoidance behaviors observed in fish.

The investigators will pursue several hypotheses regarding the development of approach and avoidance behaviors in larval zebrafish. Aim 1 will use high-speed videography and automated body tracking to evaluate the hypothesis that the circuitry for approach is not in place until later on in development. The expectation is that only the older larvae will be able to generate kinematically-distinct responses to attractive visual stimuli. Aim 2 will distinguish between two leading possibilities for how reticular circuitry mediates approach and avoidance, either via the addition of new components or the modification of pre-existing ones. In vivo dye labeling combined with functional calcium imaging approaches will assess changes in the morphologies and responses to visual stimuli of readily identifiable reticular neurons during development. In vivo patch clamp recordings will also be used to confirm outputs to spinal circuitry. Aim 3 will examine the likelihood that newly developed approach circuitry in the spinal cord is either overpowered or shut off by reticulospinal drive during avoidance maneuvers. The predictable read-outs of either scenario will be assessed using electrophysiological recordings of motor output, advanced computational fluids and body modeling, and targeted laser ablations followed by kinematic analysis.

Planning is centrally important for everyday life. Planning is challenging to study, as it involves an internal search through future possibilities for action, in the absence of any sign this is occurring from the outside. The project will investigate two synergistic aspects of real-time planning: its biology and technology. Current artificial intelligence methods require vast amounts of power, a large amount of training, and examination of millions of possible futures to tackle simple problems such as the next move in a turn-based board game. In contrast, mammals require very little power, little to no training, and examination of few possible futures to tackle complex problems such as where to go to hide from a stalking predator. This project will develop a new online planning agent—a robotic “predator”—that will interact with animals trained to evade it inside a complex habitat. The robot will interact with laboratory animals whose brain activity is being recorded while they are challenged—via specially-designed complex habitats—to employ strategic behaviors in avoiding the robot. This will test and advance theory of neural mechanisms underlying the everyday ability to plan in real time in an energy-efficient manner.

The ability to plan actions can produce much larger rewards than reactive, reflexive, or habitual behaviors. Whereas humans exhibit great proficiency in planning and executing daily movements, poor response to long-term threats shows its limits. Research on multi-step planning is in its infancy, constrained in part by behavioral tasks with low ecological validity. Theory has advanced due to rapid progress in artificial intelligence, but most formalizations require so much computing power that real-time planning is impossible. Animals seem likely to form real-time plans in some other way. In prior work, the PIs showed that a selective benefit of visually guided planning may have facilitated the transition onto land 380 million years ago because animals can see targets much farther in air than through water. The benefit of planning in predator-prey engagements is maximized in habitats that afford long sightlines while also providing obstacles that can hide adversaries. In these conditions, such as savanna-like habitats where hominins first emerged, planning its peak advantage. In Aim 1 of the project, this idea is modeled to identify locations of maximal planning payoff (via a network connectivity measure) and used to predict neural computation in animals. This initial algorithm is 10,000 times faster in achieving the same survival rate of simulated prey than a leading competitor in machine learning. This enables creation of a behavioral assay in which live animals are challenged by an adversary with similar planning abilities to their own. With the principle translated into hardware, a bidirectional benefit will emerge for Aim 2. First, neural activity—using Neuropixels probes in freely behaving mice—will be compared to the team’s theory predictions in real-time; they predict that boundary detection cells in the hippocampus and delay interval cells in entorhinal cortex are important for trimming the neurocomputational burden of plans. Second, during recordings, animals will engage with a robot that plans in real time.

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.