Autonomous Computing and Memory Materials

Image by Robina Weermeijer


The brain is arguably the most sophisticated and the most efficient computational machine in the universe. The human brain, for example, comprises about 100 billion neurons that form an interconnected circuit with well over 100 trillion connections. Understanding how a multitude of brain functions emerge from the underlying neuronal circuit will give insights into the operating principles of the brain. In this initiative, a multidisciplinary team of neuroscientists, data scientists, bioinformaticians, and material scientists will work synergistically to leverage the data revolution in neuroscience to answer a fundamental question: How does the brain learn, store, and process information? The team will develop and apply advanced data analysis algorithms to harness the great volume of neuronal data generated by the latest imaging and molecular profiling technologies, for elucidating the neuronal circuits driving brain functions. Computer simulations of a spin-electronic (spintronic) device will further serve as a platform to validate and emulate important operational characteristics of such neuronal circuits. The award sets the groundwork for an interdisciplinary data science research and educational program that will bring a new and powerful paradigm for studying brain functions as well as for designing transformative brain-inspired devices for information processing, data storage, computing, and decision making.


The MEMONET initiative has a specific focus on an essential function of the brain: motor-skill learning. This function emerges from the underlying circuitry of neurons that governs the activities of molecular signal transmission and neuronal firing. Importantly, the neuronal circuit in a mammalian brain is highly plastic and dynamic, features that endow animals with the ability to respond to myriad external stimulations through learning. By harnessing the latest data revolution in neuronal imaging, single neuron molecular profiling, spintronic device simulation, network inference, and machine learning, a team of multidisciplinary investigators will be supported by this award to investigate the fundamental principle of neuronal circuit rewiring that drives brain’s learning function. More specifically, the team sets out to achieve the following specific tasks: (A) Infer learning-induced rewiring of large-scale neuronal networks from two-photon calcium imaging data through the development of novel and powerful network inference algorithms; (B) Build biochemical-based models of neuronal circuits by integrating molecular profiling with neuron firing and connectome dynamics; and (C) Develop a spintronic material network model that emulates learning and memory formation by exploiting the spin dynamics in spintronic materials. The project seeks to lay the foundation for the creation of an interdisciplinary data-intensive brain-to-materials initiative that will be applied to understand and emulate the operational principles of brain neuronal circuits underlying learning, cognition, memory formation, and other behaviors. The outcomes of the initiative will have a paramount impact on the society, not only in our understanding of the brain and its functions, but also in overcoming current bottlenecks of existing computing architectures.