Neuromorphic systems that densely integrate CMOS spiking neurons and
nano-scale memristor synapses open a new avenue of brain-inspired computing.
Existing silicon neurons have molded neural biophysical dynamics but are
incompatible with memristor synapses, or used extra training circuitry thus
eliminating much of the density advantages gained by using memristors, or were
energy inefficient. Here we describe a novel CMOS spiking leaky
integrate-and-fire neuron circuit. Building on a reconfigurable architecture
with a single opamp, the described neuron accommodates a large number of
memristor synapses, and enables online spike timing dependent plasticity (STDP)
learning with optimized power consumption. Simulation results of an 180nm CMOS
design showed 97% power efficiency metric when realizing STDP learning in
10,000 memristor synapses with a nominal 1M{\Omega} memristance, and only
13{\mu}A current consumption when integrating input spikes. Therefore, the
described CMOS neuron contributes a generalized building block for large-scale
brain-inspired neuromorphic systems.Comment: This is a preprint of an article accepted for publication in
International Joint Conference on Neural Networks (IJCNN) 201
