Self-organization of an inhomogeneous memristive hardware for sequence learning

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SUMMARY

    Biological intelligence orchestrates synaptic and neuronal learning at multiple time scales to self-organize populations of neurons for solving complex tasks. Inspired by this, the authors design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural_Network (MEMSORN). It has been shown that the combination of brain-inspired learning rules at different time scales lends themselves to the self-organization of dynamic networks for behavior control2,3. Once a subset of a cell assembly is stimulated, its neurons tend to be activated as a whole, so that the cell can be . . .

     

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