Spike-triggered_average
The spike-triggered averaging (STA) is a tool for characterizing the response properties of a neuron using the spikes emitted in response to a time-varying stimulus. The STA provides an estimate of a neuron's linear receptive field. It is a useful technique for the analysis of electrophysiological data.
Mathematically, the STA is the average stimulus preceding a spike.[1][2][3][4] To compute the STA, the stimulus in the time window preceding each spike is extracted, and the resulting (spike-triggered) stimuli are averaged (see diagram). The STA provides an unbiased estimate of a neuron's receptive field only if the stimulus distribution is spherically symmetric (e.g., Gaussian white noise).[3][5][6]
The STA has been used to characterize retinal ganglion cells,[7][8] neurons in the lateral geniculate nucleus and simple cells in the striate cortex (V1) .[9][10] It can be used to estimate the linear stage of the linear-nonlinear-Poisson (LNP) cascade model.[4] The approach has also been used to analyze how transcription factor dynamics control gene regulation within individual cells.[11]
Spike-triggered averaging is also commonly referred to as reverse correlation or white-noise analysis. The STA is well known as the first term in the Volterra kernel or Wiener kernel series expansion.[12] It is closely related to linear regression, and identical to it in common circumstances.