A big enough digital logic circuit should in principle be able to simulate more or less anything — which is what most efforts towards ‘whole brain simulation’ are counting on. But while they rest on the assumption of functionalism, which asserts a substrate-independence of computational or cognitive algorithms, there could be advantages to building the requisite computational versatility and adaptability into the material hardware of the system: the medium, you might say, becomes an aspect of the message. That is the philosophy behind a new study that describes a dynamic, neuromorphic platform for neural-network-type processing in which the devices themselves can be given different functions via electronic transformations applied to their material substrate. This flexibility of performance in a single material has previously been one of the obstacles to realizing neuromorphic hardware.
Zhang et al. have constructed arrays of devices that can act as resistors and capacitors as well as analogues of neurons and synapses, all made from the same materials and all inter-convertible using applied electric fields (Science 375, 533–539; 2022). They are not silicon-based, but fabricated instead from thin (50 nm) films of the nickelate perovskite NdNiO3 (NNO). At room temperature, this crystalline solid is a metallic conductor, albeit with electrons that move in a correlated rather than independent fashion. The conductivity can be modified, however, by doping the material with hydrogen using a catalytic process. This creates mobile hydrogen ions (protons) distributed in the lattice; electrically induced redistribution of the protons can alter the conductivity so as to switch the two-terminal devices into metastable resistive states, or capacitive (so that they can act as memory devices), or can give them a threshold-based ‘firing’ ability to discharge pulses (like a neuron), or a nonlinear current–voltage characteristic that mimics a synapse.
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