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Published equation contexts

y(t)=∫0∞h(τ) n(t−τ) dτ+ε(t)y(t) = \int_{0}^{\infty} h(\tau)\, n(t-\tau)\, d\tau + \varepsilon(t)

Why this formula appears here

Nearly all standard analysis assumes that neural activity and the measured signal are related by a linear time-invariant system, so that the measured time course is a convolution of an underlying activity time course with a canonical response function plus noise: y(t)=∫0∞h(τ) n(t−τ) dτ+ε(t)y(t) = \int_{0}^{\infty} h(\tau)\, n(t-\tau)\, d\tau + \varepsilon(t). Writing it this way exposes what is being assumed rather than measured: that h is known, that it is the same across regions, subjects and states, and that the mapping from n to y is linear. Each of those is an approximation with documented exceptions.

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00

Starting index or lower bound: 0

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∞\infty

Ending index or upper bound: infty

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Published contexts (1)

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y(t)=∫0∞h(τ) n(t−τ) dτ+ε(t)y(t) = \int_{0}^{\infty} h(\tau)\, n(t-\tau)\, d\tau + \varepsilon(t)

Equation 3 · Neuroscience

What a Neural Recording Actually Records

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

Nearly all standard analysis assumes that neural activity and the measured signal are related by a linear time-invariant system, so that the measured time course is a convolution of an underlying activity time course with a canonical response function plus noise: y(t)=∫0∞h(τ) n(t−τ) dτ+ε(t)y(t) = \int_{0}^{\infty} h(\tau)\, n(t-\tau)\, d\tau + \varepsilon(t). Writing it this way exposes what is being assumed rather than measured: that h is known, that it is the same across regions, subjects and states, and that the mapping from n to y is linear. Each of those is an approximation with documented exceptions.

Meanings in this article

  • yy: linear.
  • hh: known, that it is the same across regions, subjects and states, and that the mapping from n to y is linear.
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