Synthetic neural networks for neuromorphic applications

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The human brain contains billions of neurons that exchange signals through synapses. We have unique intellectual abilities that outstrip the fastest supercomputers; e.g. high-level pattern recognition, energy-efficiency, and the ultimate skill of learning from experience. Such attributes have inspired the creation of so-called neuromorphic (brain-like) devices, highly connected electronic circuits that attempt to mimic the architectures present in the brain. Neurons and synapses form the primitive building blocks in biological neural systems. One of the biggest challenges in neural-inspired technologies is to find suitable building blocks that can emulate brain synapses in the synthetic realm. Moreover, we need to know how to integrate and to control these building blocks in order to meet the basic requirements of neuromorphic computing. These involve decentralized communication between the blocks, co-location of memory and processing, and multistate/analog memory response that supports learning and adaptation. My goal is to create a research program that will unveil new material concepts and building blocks for the development of cutting-edge neuromorphic devices. I will investigate how cognitive features emerge from nanoscale materials in which their electric conductivity is not static but changes with the amount of current/voltage set in their terminals. This is typically seen in memristive systems, nonvolatile memory materials whose resistance behaves as a dynamical quantity. I have structured an innovative computational platform that will model the memristive characteristics of self-assembled networks of nanoscale cognitive materials seen as promising candidates for neuromorphics. Examples of such a network are spaghetti-like structures made by randomly dispersed nanowires in which complex memristive phenomena take place in their wire-wire contact points. I target the theoretical description of emergent resistive mechanisms controlling the propagation and memorization of electrical signals throughout their disordered frame. Virtual circuit models of cognitive network materials integrated with electronic control systems will be built to simulate typical brain-functions, e.g. data recognition, memorization, and fault-tolerant processing. We will reveal optimal materials properties, circuit designs, and training protocols that will be tested in the laboratory of long-term collaborators that envision the fabrication of a proof-of-concept neuromorphic device inspired by the outcomes of our simulations. This program will enable young scientists to engage in a truly interdisciplinary environment connecting numerous fields, e.g. nanotechnology, computer science, and neuroscience; it will also place a Canadian institution (and Canada) as the knowledge exchange hub of a disruptive brain-inspired technology that will greatly impact artificial intelligence, a sector expected to be one of the leading economic drivers world-wide in the next decades.