  Memristive devices are a class of circuit elements that shows great promise
as future building block for brain-inspired computing. One influential view in
theoretical neuroscience sees the brain as a function-computing device: given
input signals, the brain applies a function in order to generate new internal
states and motor outputs. Therefore, being able to approximate functions is a
fundamental axiom to build upon for future brain research and to derive more
efficient computational machines. In this work we apply a novel supervised
learning algorithm - based on controlling niobium-doped strontium titanate
memristive synapses - to learning non-trivial multidimensional functions. By
implementing our method into the spiking neural network simulator Nengo, we
show that we are able to at least match the performance obtained when using
ideal, linear synapses and - in doing so - that this kind of memristive device
can be harnessed as computational substrate to move towards more efficient,
brain-inspired computing.
