Burst firing improves the detection of weak signals in spike trains

The detection of weak sensory signals is an important aspect of neuronal information processing. Behaviorally relevant signals are often encoded as perturbations of on-going spiking activity in primary afferents. Here, we show that a biologically plausible model, the leaky integrate-and-fire (LIAF) neuron, is capable of efficient and reliable detection of a single spike added to baseline activity. Detection performance is dependent on the statistical properties of the spike train. For the type of statistics considered here, an LIAF neuron can distinguish between a correct detection by means of burst firing, whereas false alarms tend to result in isolated spikes. The methods are illustrated by an application to electrosensory afferents of weakly electric fish.