  Memristors are low-power memory-holding resistors thought to be useful for
neuromophic computing, which can compute via spike-interactions mediated
through the device's short-term memory. Using interacting spikes, it is
possible to build an AND gate that computes OR at the same time, similarly a
full adder can be built that computes the arithmetical sum of its inputs. Here
we show how these gates can be understood by modelling the memristors as a
novel type of perceptron: one which is sensitive to input order. The
memristor's memory can change the input weights for later inputs, and thus the
memristor gates cannot be accurately described by a single perceptron,
requiring either a network of time-invarient perceptrons or a complex
time-varying self-reprogrammable perceptron. This work demonstrates the high
functionality of memristor logic gates, and also that the addition of
theasholding could enable the creation of a standard perceptron in hardware,
which may have use in building neural net chips.
