  Motivated by advantages of current-mode design, this brief contribution
explores the implementation of weight matrices in neuromemristive systems via
current-mode memristor crossbar circuits. After deriving theoretical results
for the range and distribution of weights in the current-mode design, it is
shown that any weight matrix based on voltage-mode crossbars can be mapped to a
current-mode crossbar if the voltage-mode weights are carefully bounded. Then,
a modified gradient descent rule is derived for the current-mode design that
can be used to perform backpropagation training. Behavioral simulations on the
MNIST dataset indicate that both voltage and current-mode designs are able to
achieve similar accuracy and have similar defect tolerance. However, analysis
of trained weight distributions reveals that current-mode and voltage-mode
designs may use different feature representations.
