Noise in attractor networks in the brain produced by graded firing rate representations

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 circuitr