Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate/nprobability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as/ndecision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given/nmean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that/nis usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is/nindeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions./nThe greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are/napplied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the/nspontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to/nbe a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall/neven with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent/ncollateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can/nincrease the speed of operation of cortical circuitry