Neurons are notoriously noisy devices. Although the traditional view posits that noise degrades system performance, recent evidence suggests that noise may instead enhance neural information processing under certain conditions. Here we report that random channel and synaptic noise improve the ability of a biologically realistic computational model of the Hermissenda eye to encode light intensity. The model was created in GENESIS, and is based on a previous model used to examine effects of changes in type-B photoreceptor excitability, synaptic strength, and network architecture. The network consists of two type-A and three type-B multicompartmental photoreceptors. Each compartment contains a population of Hodgkin-Huxley type ion channels, and each cell is stimulated via artificial light currents. We found that the addition of random channel and synaptic noise yielded a significant improvement in the accuracy of the network's encoding of light intensity across eight light levels spanning 3.5 log units (p < .001, modified Levene test). The benefits of noise remained after controlling for several consequences of randomness in the model. Additionally, improvements were not confined to peri-threshold stimulus intensities. Finally, the effects of noise are not present in individual neurons, but rather are an emergent property of the synaptically connected network that is independent of stochastic resonance. These results suggest that noise plays a constructive role in neural information processing, a concept that could have important implications for understanding neural information processing or designing neural interface devices.