Parallel processing by a homogeneous group of coupled model neurons can enhance, reduce and generate signal correlations.

Correlated activities have been proposed as correlates of flexible association and assembly coding. We addressed the basic question of how signal correlations on parallel pathways are enhanced, reduced and generated by homogeneous groups of coupled neurons, and how this depends on the input activities and their interactions with internal coupling processes. For this we simulated a fully connected group of identical impulse-coded neurons with dynamic input and threshold processes and additive or multiplicative lateral coupling. Input signals were Gaussian white noise (GWN), completely independent or partially correlated on a subgroup of the parallel inputs. We show that in states of high average spike rates input-output correlations were weak while the network could generate correlated activities of stochastic, oscillatory and rhythmic bursting types depending exclusively on lateral coupling strength. In states of low average spike rates input-output correlations were high and the network could effectively enhance or reduce differences in spatial correlation applied to its parallel inputs. The correlation differences were more pronounced with multiplicative lateral coupling than with the additive interactions commonly used. As the different modes of correlation processing emerged already by global changes in the average spike rate and lateral coupling strength, we assume that in real cortical circuits changes in correlational processing may also be induced by unspecific modulations of activation and lateral coupling.