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Auditory Nerve Laboratory: What was the Stimulus?

Auditory Nerve Laboratory: What was the Stimulus?. Bertrand Delgutte HST.723 – Neural Coding and Perception of Sound. Real and Idealized Spike Trains. Spike train from inferior colliculus of awake rabbit (Devore).

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Auditory Nerve Laboratory: What was the Stimulus?

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  1. Auditory Nerve Laboratory: What was the Stimulus? Bertrand Delgutte HST.723 – Neural Coding and Perception of Sound

  2. Real and Idealized Spike Trains Spike train from inferior colliculus of awake rabbit (Devore) Fundamental assumption: All the information is contained in the timing of spikes Idealized spike trains are modeled mathematically as point processes

  3. Peri-Stimulus Time Histogram Auditory-Nerve Fiber, 400-ms Pure Tone

  4. Interspike Interval Histogram Homogeneous Poisson process with 0.6-ms dead time 0 400 ms Dead time ISIH also useful with pure tones. ISIH modes can be used to estimate stimulus period.

  5. Period Histogram Nonhomogeneous Poisson Process, 500-Hz Pure tone 20 ms SI = 0.85 Period needs to known to compute a period histogram

  6. Spike-Train Analysis with Histograms

  7. The reverse correlation (“revcor”) method (de Boer) Evans (1977) Pickles (1988) • Determine the average (most likely) stimulus waveform preceding a spike. • Measured by “spike-triggered averaging” with a white noise stimulus. • Revcor functions of low-CF auditory-nerve fibers resemble the impulse response of a bandpass filter centered at the CF. Fourier transforms of revcor functions match the tip of pure-tone tuning curves over a wide range of noise levels. • The revcor is an estimate of the crosscorrelation between stimulus and response.

  8. Reverse correlation and Wiener filters • Given a linear system, the crosscorrelation of the response r(t) with a stationary, white noise input w(t) is proportional to the system’s impulse response h(t): • System identification: Given, two signals r(t) and s(t), the linear filter h(t) which does the best job (in a least-squares sense) of predicting r(t) from s(t) is known as the Wiener filter. In the special case when s(t) is white noise, h(t) is the crosscorrelation function between input and output (except for a scale factor). • The revcor is an estimate of the Wiener filter in the special case when r(t) consists of impulses (spikes).

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