Adaptive sparse coding based on memristive neural network with applications

Memristor is a nanoscale circuit element with nonvolatile, binary, multilevel and analog states. Its conductance (resistance) plasticity is similar to biological synapses. Information sparse coding is considered as the key mechanism of biological neural systems to process mass complex perception data, which is applied in the fields of signal processing, computer vision and so on. This paper proposes a soft-threshold adaptive sparse coding algorithm named MMN-SLCA based on the memristor, neural network and sparse coding theory. Specifically, the memristor crossbar array is used to realize the dictionary set. And by leveraging its unique vector-matrix operation advantages and biological synaptic characteristic, two key compositions of the sparse coding, namely, pattern matching and lateral neuronal inhibition are realized conveniently and efficiently. Besides, threshold variability further enhances the adaptive ability of the intelligent sparse coding. Furthermore, a hardware implementation framework of the sparse coding algorithm is designed to provide feasible solutions for hardware acceleration, real-time processing and embedded applications. Finally, the application of MMN-SLCA in image super-resolution reconstruction is discussed. Experimental simulations and result analysis verify the effectiveness of the proposed scheme and show its superior potentials in large-scale low-power intelligent information coding and processing.