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Equation 2 · What a Neural Recording Actually Records

What does this equation mean?

ϕ(r,t)=14πσ∑nIn(t)∥r−rn∥\phi(\mathbf{r},t) = \frac{1}{4\pi\sigma}\sum_{n}\frac{I_n(t)}{\lVert \mathbf{r}-\mathbf{r}_n \rVert}

Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.

Start with1
Divide by4piσ
This relates tophi(r,t)
How to read the two sides of this formula. Follow the article passage for the meaning of each quantity.

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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ϕ\phi

Symbol phi

phi is part of the quantity the equation computes from the expression on the right.

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tt

Symbol t

t is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

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π\pi

Symbol pi

pi occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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σ\sigma

Symbol σ

σ occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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nn

Symbol n

n appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

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InI_n

Symbol I_n

InI_n occurs above the fraction bar. The numerator is divided by the entire denominator below it.

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=

=

The expressions on both sides represent the same quantity under the stated assumptions.

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fraction

fraction

Divide the expression above the line by the one below it.

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subtraction

subtraction

Subtract the following term or group from the preceding one. A leading minus marks a negative quantity.

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subscript

subscript

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

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11

Numerator: 1

The complete quantity above the fraction bar.

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4πσ4\pi\sigma

Denominator: 4piσ

The complete quantity below the fraction bar; it must be nonzero for this division.

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nn

Starting index or lower bound: n

This label says where the repeated addition, multiplication, or accumulation starts. Read its value or condition together with the article’s description of the index.

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In(t)I_n(t)

Numerator: I_n(t)

The complete quantity above the fraction bar.

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∥r−rn∥\lVert \mathbf{r}-\mathbf{r}_n \rVert

Denominator: lVert r-r_n rVert

The complete quantity below the fraction bar; it must be nonzero for this division.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.

What the article says around this equation

Start with the most direct measurement available. A metal or silicon electrode placed in the extracellular space senses the potential produced by transmembrane currents in the surrounding tissue. Under the standard volume-conductor treatment, with the medium approximated as homogeneous, isotropic and purely resistive, and each current source treated as a point, the potential at position r\mathbf{r} is a superposition: ϕ(r,t)=14πσ∑nIn(t)∥r−rn∥\phi(\mathbf{r},t) = \frac{1}{4\pi\sigma}\sum_{n}\frac{I_n(t)}{\lVert \mathbf{r}-\mathbf{r}_n \rVert}. Three assumptions are visible in that expression, and all three are approximations rather than facts. The medium is not homogeneous. Conductivity may be frequency-dependent. And the sum runs over every current source in range, not over the neuron of…
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Start with the most direct measurement available. A metal or silicon electrode placed in the extracellular space senses the potential produced by transmembrane currents in the surrounding tissue. Under the standard volume-conductor treatment, with the medium approximated as homogeneous, isotropic and purely resistive, and each current source treated as a point, the potential at position r\mathbf{r} is a superposition: ϕ(r,t)=14πσ∑nIn(t)∥r−rn∥\phi(\mathbf{r},t) = \frac{1}{4\pi\sigma}\sum_{n}\frac{I_n(t)}{\lVert \mathbf{r}-\mathbf{r}_n \rVert}. Three assumptions are visible in that expression, and all three are approximations rather than facts. The medium is not homogeneous. Conductivity may be frequency-dependent. And the sum runs over every current source in range, not over the neuron of interest.

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Sources cited in the article section

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