The<unk>  <unk>  is a tool for characterizing the response properties of a neuron using the<unk> s emitted in response to a time-varying stimulus. The<unk>  provides an estimate of a neuron's linear receptive field. It is a useful technique for the analysis of electrophysiological data. Diagram showing how the<unk>  is calculated. A stimulus (consisting here of a checkerboard with random pixels) is presented, and<unk> s from the neuron are recorded. The stimuli in some time window preceding each<unk>  (here consisting of 3 time bins) are selected (color boxes) and then<unk> d (here just summed for clarity) to obtain the<unk> . The<unk>  indicates that this neuron is selective for a bright spot of light just before the<unk> , located in the top left corner of the checkerboard. Mathematically, the<unk>  is the<unk>  stimulus preceding a<unk> . To compute the<unk> , the stimulus in the time window preceding each<unk>  is extracted, and the resulting<unk> -triggered) stimuli are<unk> d (see diagram). The<unk>  provides an unbiased estimate of a neuron's receptive field only if the stimulus distribution is spherically symmetric (e.g., Gaussian white noise). The<unk>  has been used to characterize retinal ganglion cells, neurons in the lateral geniculate nucleus and simple cells in the striate cortex (V1) . It can be used to estimate the linear<unk> ge of the linear-nonlinear-Poisson (LNP) cascade model. The approach has also been used to analyze how transcription factor dynamics control gene regulation within individual cells.<unk> ng is also commonly referred to as “reverse correlation″ or “white-noise analysis”. The<unk>  is well known as the first term in the Volterra kernel or Wiener kernel series expansion. It is closely related to linear regression, and identical to it in common circumstances.
Spike-triggered average
Spike-triggered covariance
Event-related potential
Divided visual field paradigm
Basal rate
Threshold potential
Antisaccade task
Amplitude integrated electroencephalography