Effects of correlated and independent noise on signal processing in neuronal systems

Stochastic resonance has recently received considerable attention demonstrating that noise can play a constructive role in signal processing. We investigate the effects of input noise on sensory processing via numerical simulation when they are independent of each other or spatially correlated in a globally coupled neuronal network. The network exhibits a coherent behavior in the absence of stimulation. Such ongoing activity has a remarkable influence on neuronal responses to stimuli. In the presence of a subthreshold periodic signal, the activity averaged over neurons can convey precise information about the stimulus in the case of independent noise. On the other hand, when the noise is correlated among the neurons, the average response is nearly as noisy and variable as the responses of the individual neurons. Thus, the spatially correlated noise diminishes the beneficial effects of pooling, although it can evoke synchronous firings of neurons. These suggest that response variability in cortical activity may be closely related to the correlation in input noise.