We examined the ability of neuronal ensembles from rat motor cortex to predict behavioral performance during a reaction time task. We found that neurons that were the best individual predictors of task performance were not necessarily the neurons that contributed the most predictive information to an ensemble of neurons. To understand this result, we applied a framework for quantifying statistical relationships between neurons (Schneidman et al., 2003) to all possible combinations of neurons within our ensembles. We found that almost all neurons (96%) contributed redundant predictive information to the ensembles. This redundancy resulted in the maintenance of predictive information despite the removal of many neurons from each ensemble. Moreover, the balance of synergistic and redundant interactions depended on the number of neurons in the ensemble. Small ensembles could exhibit synergistic interactions (e.g., 23 +/- 9% of ensembles with two neurons were synergistic). In contrast, larger ensembles exhibited mostly redundant interactions (e.g., 99 +/- 0.1% of ensembles with eight neurons were redundant). We discuss these results with regard to constraints on interpreting neuronal ensemble data and with respect to motor cortex involvement in reaction time performance.