Traditional artificial intelligence implemented in software is usually executed on accurate digital computers. Nevertheless, the nanoscale devices for the implementation of neuromorphic computing may not be ideally identical, and the performance is reduced by nonuniform devices. In biological brains, information is usually encoded by a cluster of neurons such that the variability of nerve cells does not influence the accuracy of human cognition and movement. Here, we introduce the population encoding strategy in neuromorphic computing and demonstrate that this strategy can overcome the problems caused by nonuniform devices. Using magnetic memristor device based on current-induced domain-wall motion as an example, we show that imperfect storage devices can be applied in a hardware network to perform principal component analysis (PCA), and the accuracy of unsupervised classification is comparable to that of conventional PCA using ideally accurate synaptic weights. Our results pave the way for hardware implementation of neuromorphic computing and lower the criteria for the uniformity of nanoscale devices.
Developing suitable algorithms that utilize the natural advantages of the corresponding devices is a key issue in the hardware research of brain-inspired computing. Population coding is one of the computational schemes in biological neural systems and it contains the mechanisms for noise reduction, short-term memory and implementation of complex nonlinear functions. Here we show the controllable stochastic dynamical behaviors for the technically mature spintronic device, magnetic tunnel junctions, which can be used as the basis of population coding. As an example, we construct a two-layer spiking neural network, in which groups of magnetic tunnel junctions are used to code input data. After unsupervised learning, this spiking neural network successfully classifies the iris data set. Numerical simulation demonstrates that the population coding is enough robust against the nonuniform dispersion in devices, which is inevitable in fabrication and integration of hardware devices.
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