. 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 Ͻ 0.001, modified Levene test). The benefits of noise remained after controlling for several consequences of randomness in the model. Additionally, improvements were not confined to perithreshold 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.
I N T R O D U C T I O NNeurons are notoriously noisy devices, and problems of random variation, noise, and reliability arise almost universally in the nervous system (Perkel and Bullock 1968). The traditional view, partially influenced by decades of signal processing research, is that noise lowers the signal-to-noise ratio (SNR) and thus degrades performance. Another traditional view, more relevant to neuroscience, is that noise reduces spike-timing precision and therefore lowers the rate of information transfer. If the traditional views are true, then decreasing noise in the nervous system should improve performance. However, biological systems perform quite well in the presence of noise, often easily outperforming their human-engineered counterparts. Here we present evidence for an alternate interpretation based on a suite of computational experiments: the nervous system uses randomness to its advantage such that noise paradoxically improves, rather than degrades, performance. Our experiments demonstrate such an effect in the Hermissenda photoreceptor network. The purpose of this study is to examine the ability of the eye to encode light intensity in the presence of random noise. A companion paper takes this analysis a step further to look for mechanisms of noise-induced improvement (Butson and Clark 2008).The marine mollusk Hermissenda crassicornis has served as a prominent preparation fo...
