  Neuromorphic photonics relies on efficiently emulating analog neural networks
at high speeds. Prior work showed that transducing signals from the optical to
the electrical domain and back with transimpedance gain was an efficient
approach to implementing analog photonic neurons and scalable networks. Here,
we examine modulator-based photonic neuron circuits with passive and active
transimpedance gains, with special attention to the sources of noise
propagation. We find that a modulator nonlinear transfer function can suppress
noise, which is necessary to avoid noise propagation in hardware neural
networks. In addition, while efficient modulators can reduce power for an
individual neuron, signal-to-noise ratios must be traded off with power
consumption at a system level. Active transimpedance amplifiers may help relax
this tradeoff for conventional p-n junction silicon photonic modulators, but a
passive transimpedance circuit is sufficient when very efficient modulators
(i.e. low C and low V-pi) are employed.
