In recent years, grids of memristor devices, with their synapse-like dynamics and adaptable conductivity, have demonstrated neural-network-type implementations of analogue (non-Boolean) computing. Suhas Kumar et al. now explore the possibility of exploiting chaotic dynamics in highly nonlinear niobium dioxide memristor devices. This idea is inspired by the theory that biological neurons operate in a regime called ‘the edge of chaos’, which is thought to be key to the ability of the human brain to tackle complex information processing tasks with high efficiency. The authors demonstrate a controllable regime of chaotic self-oscillations in their devices and simulate a memristor grid that can solve a typical computationally hard task—a travelling salesman problem—with higher accuracy and efficiency than an approach that does not incorporate chaotic elements. Building artificial neural networks with chaotic oscillators based on single electronic devices provides an exciting direction for unconventional analogue computing.
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