Deriving the Cable Equation (neuroscience) from Fundamental Physics Laws

  • #1
Icaro Lorran
13
3
Homework Statement
I want to derive the cable equation 12 on this link: https://en.wikipedia.org/wiki/Cable_theory

The way wikipedia derives wasn't very convincing to me, so I wanted to start from Maxwell's equations and some basic principles instead.
Relevant Equations
##\nabla \cdot D = \rho##
##\frac{\partial \rho}{\partial t} + \nabla \cdot J = 0##
##J = \sigma E##
> Note: I am using SageMath to do the manipulations, I will attach it with the post

I modeled the problem as a cylinder of height ##\Delta z## and anisotropic conductivity: the conductivity along the axis is different from the one along the radius. Using ##J = \sigma E##, where ##\sigma## is a tensor encoding both directions, I can write the total current as

$$J = \sigma_z (1 - H(r-a)) E_z \hat{z} + \sigma_r (1 - H(r-a)) E_r \hat{r} $$
.

The reason I added the heaviside function is that the conductivity should be zero outside the tube. With a similar reasoning, I also said that the potential must vanish outside as well

$$V(t, r, z) = u(t,z)(1 - H(r-a))$$

I don't include $\theta$ due to rotational symmetry.

The first Maxwell equation for a linear medium is

$$\nabla \cdot D = \rho$$

With $D = \epsilon E$, where $\epsilon$ is also a tensor:

$$\epsilon = \epsilon_0 \hat{z} \otimes \tilde{z} + \epsilon(1 - H(r-a)) \hat{r} \otimes \tilde{r} + \epsilon_0 H(r-a) \hat{r} \otimes \tilde{r}$$

Plugging that on $D$, it becomes

$$D = (\epsilon + (\epsilon_0 - \epsilon) H(r-a))E_r \hat{r} + \epsilon_0 E_z \hat{z}$$
.

Now, my initial plan was to join the first Maxwell equation with the equation for conservation of charge (consider all these integrals to be closed):

$$Q = \iint D \cdot \mathrm{d}^2 \vec{r} $$

and
$$\iint J \cdot \mathrm{d}^2 \vec{r} = -\frac{\mathrm{d}}{\mathrm{d} t} Q$$

$$\iint J \cdot \mathrm{d}^2 \vec{r} = -\frac{\mathrm{d}}{\mathrm{d} t} \iint D \cdot \mathrm{d}^2 \vec{r}$$

$$\iint \left( J + \frac{\partial}{\partial t} D\right) \cdot \mathrm{d}^2 \vec{r} = 0$$

The part that I get stuck is how to go on from here. I tried to use a cylinder as a surface to enclose everything, but what comes out of it doesn't really resemble the desired equation. I did notice that integrating the result over ##r##, does get something close, but I don't have any explanation as to why that works.

Here's the link to the notebook (I couldn't attach): https://github.com/icarosadero/cable_equation/blob/main/cable_equation.ipynb
 
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  • #2
Cable theory involves Ohmic principles more than Maxwell. You have to incorporate the electric potential ##V## into your equations. I would try to integrate it after putting in ##E## for where there is an electric field (ex. ##J=\sigma E##) in your integrals and then substitute ##E=V/d##.
 

FAQ: Deriving the Cable Equation (neuroscience) from Fundamental Physics Laws

What is the cable equation in neuroscience?

The cable equation is a mathematical model that describes how electrical signals propagate along a neuron's axon. It is derived from the principles of electrical circuits and diffusion and accounts for the passive properties of the neuron's membrane, including resistance and capacitance. The equation helps to predict the changes in voltage over time and space along the axon, which is crucial for understanding neuronal signaling.

How is the cable equation derived from fundamental physics laws?

The cable equation is derived using the principles of Ohm's law and the conservation of charge. By considering a small segment of the axon as a cylindrical conductor, we apply Kirchhoff's laws to analyze the flow of ionic currents and the resulting voltage changes. The equation incorporates the axial resistance, membrane resistance, and membrane capacitance, leading to a partial differential equation that describes voltage changes over time and distance.

What role do resistive and capacitive elements play in the cable equation?

In the cable equation, resistive elements represent the opposition to current flow along the axon (axial and membrane resistance), while capacitive elements represent the ability of the membrane to store charge. The interplay between these elements determines how quickly voltage changes can occur in response to stimuli. High resistance slows down the signal, while high capacitance can delay the voltage change, affecting the overall conduction velocity of the action potential.

Why is the cable equation important for understanding neuronal function?

The cable equation is crucial for understanding how action potentials and synaptic signals propagate in neurons. It allows researchers to model and predict how different parameters, such as membrane properties and geometry, influence signal transmission. This understanding is essential for studying various neurological processes and disorders, as well as for developing computational models of neuronal networks.

What are some limitations of the cable equation?

While the cable equation provides valuable insights into neuronal signaling, it has limitations. It assumes uniform properties along the axon and does not account for active processes like voltage-gated ion channels, which play a significant role in action potential generation and propagation. Additionally, the equation is primarily applicable to passive conduction and may not accurately describe fast or complex signaling dynamics in real neuronal systems.

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