  Heterogeneous systems with analog CMOS circuits integrated with nanoscale
memristive devices enable efficient deployment of neural networks on
neuromorphic hardware. CMOS Neuron with low footprint can emulate slow temporal
dynamics by operating with extremely low current levels. Nevertheless, the
current read from the memristive synapses can be higher by several orders of
magnitude, and performing impedance matching between neurons and synapses is
mandatory. In this paper, we implement an analog leaky integrate and fire (LIF)
neuron with a voltage regulator and current attenuator for interfacing CMOS
neurons with memristive synapses. In addition, the neuron design proposes a
dual leakage that could enable the implementation of local learning rules such
as voltage-dependent synaptic plasticity. We also propose a connection scheme
to implement adaptive LIF neurons based on two-neuron interaction. The proposed
circuits can be used to interface with a variety of synaptic devices and
process signals of diverse temporal dynamics.
