Here the artificial neurons consist of phase-change materials, including germanium antimony telluride, which exhibit two stable states, an amorphous one and a crystalline one. In the published demonstration, the team applied a series of electrical pulses to the artificial neurons (corresponding to streams of data), which resulted in the progressive crystallization of the phase-change material, ultimately causing the neuron to fire.
In neuroscience, this function is known as the integrate-and-fire property of biological neurons (when a cumulated stimulus threshold is met). This is the foundation for event-based computation. Therefore, the artificial neurons are not used to store data as a given state, but instead are used for their analogue behaviour, just like the synapses and neurons operate in the brain.
By exploiting this integrate-and-fire property, even a single neuron can be used to detect patterns and discover correlations in real-time streams of event-based data. In a paper published in Nature Nanotechnology, the researchers describe how they organized hundreds of artificial nano-scale neurons into populations and used them to represent fast and complex signals. In a video, they demonstrate how feeding pixel data from random images to thousands of synapses connected to only two level-tuned neurons, they were able to detect recurring patterns (two distinctive logos) out of the average noise.