Correlograms track spike-train periodicity over time via sliding windows

Evidence

This works so well that tiny signals can be rescued from a lot of noise, as seen in figure 4.13. A pure noise signal and its autocorrelation are shown at the bottom. A noisy signal with a smooth periodic signal embedded appears at the top. The combined signal looks as ragged as the one at the bottom, which is pure noise. However, computing the autocorrelation reveals the hidden signal (top right). — Eric J. Heller, Why You Hear What You Hear- An…, 432 (#exemplifies, via linked claim)

Categorization

• XREF: connects to: Correlograms track how spike-interval timing changes across a stimulus; Tiny periodic signals can be recovered from heavy noise via autocorrelation — Eric J. Heller, Why You Hear What You Hear- An…, 432; Summing interspike interval histograms reveals the fundamental period across all places (+1 more)

Related

• Correlograms track how interspike-interval statistics change over time in cochlear nucleus responses (similarity 0.85)
• Tiny periodic signals can be recovered from heavy noise via autocorrelation (similarity 0.85)
• Shuffled inter-spike-interval analysis captures time-varying periodicity in single neurons. (similarity 0.84)
• A finite-length burst yields a short-lived autocorrelation that vanishes beyond its duration (similarity 0.83)
• Autocorrelation measures periodic self-similarity in a signal across time delays. (similarity 0.81)
• Detection can be modeled as cross-correlation of the signal template with actual activity (similarity 0.80)
• Autocorrelation discriminates echoes from genuine distinct sounds. (similarity 0.79)
• Impulse velocity response serves as a ready-made autocorrelation (similarity 0.79)

Broader Connections

• Correlograms track how spike-interval timing changes across a stimulus #expands (0.93)
• Tiny periodic signals can be recovered from heavy noise via autocorrelation #exemplifies (0.72) — Eric J. Heller, Why You Hear What You Hear- An…, 432