Title: Implementing Neuromorphic Reservoir Computing with Self-Assembled Memristive Switching Networks

Abstract: Training and use of large nonlinear neural networks on conventional computer architectures is impeded by poor scalability and high energy penalties for sequential updates of neuron weights. Here we develop a low power and highly scalable computing paradigm of self-assembled neural network-like architectures using DNA origami, functional peptides, and inorganic components for fabrication of circuits with potential for real-time computing. We experimentally and theoretically examine circuits created by the molecular assembly of functional components capable of displaying complex, emergent electronic behaviors such as memristor based reservoir computing. Deterministic assembly at low nanometer length-scales followed by stochastic assembly at high nanoscale and up to micron scale should provide circuits with exploitable electronic behaviors. Theoretical work focuses particularly on modeling and simulation of device function and network structure/function in order to predict emergent electronic properties. Network architectures will follow neuromorphic principles and will result in trainable or learnable circuits with potential capabilities including memory, logic, and complex signal processing.