Abstract
The first step toward understanding the brain is to learn how individual neurons process incoming signals, the vast majority of which arrive in their dendrites. Dendrites were first discovered at the beginning of the twentieth century and were characterized by great anatomical variability, both within and across species. Over the past years, a rich repertoire of active and passive dendritic mechanisms has been unveiled, which greatly influences their integrative power. Yet, our understanding of how dendrites compute remains limited, mainly because technological limitations make it difficult to record from dendrites directly and manipulate them. Computational modeling, on the other hand, is perfectly suited for this task. Biophysical models that account for the morphology as well as passive and active neuronal properties can explain a wide variety of experimental findings, shedding new light on how dendrites contribute to neuronal and circuit computations. This chapter aims to help the interested reader build biophysical models incorporating dendrites by detailing how their electrophysiological properties can be described using simple mathematical frameworks. We start by discussing the passive properties of dendrites and then give an overview of how active conductances can be incorporated, leading to realistic in silico replicas of biological neurons.
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Notes
- 1.
\( \delta (u)=\left\{\begin{array}{c}1, if\ u\ge 0\\ {}0, if\ u<0\end{array}\right. \)
- 2.
The convolution of any one function with the delta function returns the function unchanged: \( \underset{-\infty }{\overset{\infty }{\int }}f\left(x-y,t\right)\delta (y) dy=f\left(x,t\right)\ast \delta (y)=f\left(x,t\right) \)
- 3.
sinh(α − β) =  sinh (α) cosh (β) −  sinh (β) cosh (α), with α = L − X, β = L
- 4.
\( \frac{\mathrm{d}}{\mathrm{d}z}\cosh (z)=\sinh (z),\frac{\mathrm{d}}{\mathrm{d}z}\sinh (z)=\cosh (z) \)
- 5.
\( \cosh (z)=\frac{\exp \left(-z\right)+\exp (z)}{2} \)
- 6.
\( \int \frac{1}{x} dx=\ln \left|x\right|+c,c\in \mathbb{R} \)
- 7.
exp(ln(x))Â =Â x
- 8.
exp(x + y) =  exp (x) +  exp (y)
- 9.
\( \frac{\mathrm{d}}{\mathrm{d}x}\left(f(x)+g(x)\right)=\frac{\mathrm{d}}{\mathrm{d}x}f(x)+\frac{\mathrm{d}}{\mathrm{d}x}g(x),\frac{\mathrm{d}}{\mathrm{d}x}\left(\exp \left(\alpha x\right)\right)=\alpha \exp \left(\alpha x\right) \)
- 10.
∫(f(x) + g(x))dx =  ∫ f(x)dx +  ∫ g(x)dx
- 11.
\( f\left(x,t\right)\ast g\left(x,t\right)\stackrel{\scriptscriptstyle\mathrm{def}}{=}\underset{-\infty }{\overset{\infty }{\int }}f\left(x-\zeta, t\right)g\left(\zeta \right) d\zeta \)
- 12.
\( \alpha {x}^2+\beta x+\gamma =\alpha {\left(x+\frac{\beta }{2a}\right)}^2\gamma -\frac{\beta^2}{4a} \)
- 13.
\( {c}_1f(t)+{c}_2g(t)\overset{\mathcal{L}}{\to }{c}_1F(s)+{c}_2G(s) \)
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Acknowledgments
This work was supported by the FET Open grant NEUREKA (GA 863245) of the EU and the EINSTEIN Foundation Berlin. We would like to thank Dr. Athanasia Papoutsi, Stamatios Aliprantis, and Konstantinos-Evangelos Petousakis for their valuable feedback and comments.
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Appendix: Mathematical Derivations
Appendix: Mathematical Derivations
1.1 A.1 General Solution to the Linear Cable Equation in Time and Space Given a Current Injection, I(x, t), at Some Point
The initial voltage at t = 0 is given by V(x, 0) = V0(t)
To analytically solve Eq. (A.1), we will take advantage of the Fourier transforms. Recall that the Fourier transform is usually used to convert the time domain into a frequency domain. However, here we will use it to solve the cable equation. Thus, we will apply the Fourier transform in the space domain instead of time.
Generally, the Fourier transform of any function is given by the integral
The inverse Fourier transform returns us to the space domain
Applying Fourier transformation to Eq. (A.1) and using the linearity property of the Fourier, i.e.
we obtain the expression in Fourier space
Using the property of derivatives, i.e.:
Thus, Eq. (A.1) is written as
Equation (A.2) is an inhomogeneous ordinary differential equation, and we will solve it with the method of variation of a constant.
For simplifying the notation, we let \( \hat{V}\equiv \hat{V}\left(\omega, t\right),\hat{I}\equiv \hat{I}\left(\omega, t\right) \)
First, we solve the homogenous differential equation, setting the right-hand side of the equation to zero.
Then, we obtain the solution by integrating both sides and using some calculus propertiesFootnote 6,Footnote 7,Footnote 8
Let C =  ±  exp (C1)
Now, to solve the inhomogeneous equation, we plug in the solution (Eq. A.3), assuming that the constant C is some function of time, C(t)
where, using the summation and composite rules of derivativesFootnote 9
Canceling out the opposite terms (i.e., same absolute value but different signs),
Plug this into Eq. (A.3)
At t = 0, \( \hat{V}\left(\omega, 0\right)={\hat{V}}_0\left(\omega \right) \)
Thus, we have obtained the solution of the cable equation for any injected current. The next step is to return in space domain applying the inverse Fourier transformation and using the integral summation propertyFootnote 10
Thus, the complete solution of the linear cable equation is
Let
and
First, we solve J1
This is the inverse Fourier transform of a multiplication. Here, we will use the convolution property.Footnote 11
For any arbitrary functions, f(x, t) and h(x, t), according to the convolution property
where \( F\left(\omega, t\right)=\exp \left(-\frac{t}{\tau_m}\right)\exp \left(-\frac{t}{\tau_m}{\lambda}^2{\omega}^2\right) \) and \( H\left(\omega, t\right)={\hat{V}}_0\left(\omega \right) \)
By definition,
Thus, we have to find the inverse Fourier transform of f(x, t)
The first exponential does not depend on ω, thus, it can go outside of the integral, and completing the square over ω inside the second exponential:Footnote 12
Thus,
Let
Let \( \psi =\omega +\frac{x^2}{2\left(\frac{t}{\tau_m}{\lambda}^2\right)}\Longrightarrow d\psi = d\omega \)
Let \( \xi =\sqrt{\frac{t{\lambda}^2}{\tau_m}}\psi \Longrightarrow d\xi =\sqrt{\frac{t{\lambda}^2}{\tau_m}} d\psi \Longrightarrow d\psi =\sqrt{\frac{\tau_m}{t{\lambda}^2}}\ d\xi \)
This integral is the famous Gaussian integral and is equal to \( \sqrt{\pi } \), i.e.
Therefore,
Taking all together,
For the second term, we have the integral
where, as before,
Thus,
The complete solution of the linear cable equation is to any current injection, Iinj(x, t):
where
1.2 A.2 Solution to a Constant, Localized Current
The solution of the linear cable equation, when a constant current is applied at x = 0, i.e., I(x, t) = I0δ(x)/πd, and the initial voltage is zero, V0(x) = 0
Convolution of any function with the delta function returns the function itself, thus:
Let ϕ = t − s ⟹ dϕ =  − ds, and absorb the minus by inverting the integral limits
Let \( T=\frac{\phi }{\tau_m}\Longrightarrow dT=\frac{1}{\tau_m} d\phi \)
To solve this integral, we will use the Laplace transform because its properties involve integrals of this form.
The Laplace transform is defined as
Using the time-domain integration Laplace transform property, i.e.
Let \( f(T)=\frac{1}{\sqrt{T}}\exp \left(-T-{\left(\frac{x}{2\lambda}\right)}^2\frac{1}{T}\ \right) \)
Let \( u=\sqrt{T}\Longrightarrow du=\frac{1}{2}\frac{dT}{\sqrt{T}},{u}_1=0,{u}_2=\infty \)
We complete the square inside the exponential, i.e.
Thus,
Let
Also, let \( k=\sqrt{\frac{b}{s+1}}\frac{1}{u}\Longrightarrow u=\sqrt{\frac{b}{s+1}}\frac{1}{k}\Longrightarrow du=-\sqrt{\frac{b}{s+1}}\frac{1}{k^2}dk, {k}_1=\infty, {k}_2=0 \)
Multiplying both sides with \( \sqrt{s+1} \)
Let \( w=\sqrt{s+1}u-\frac{\sqrt{b}}{u}\Longrightarrow dw=\sqrt{s+1}+\frac{\sqrt{b}}{u^2},{w}_1=-\infty, {w}_2=\infty \)
Therefore,
Thus,
Let \( G(s)=\frac{1}{s}F(s) \)
Using the identity
Another important property of the Laplace transform is the shift in frequency domain
Here, ϵ =  − 1
In addition, from Bateman (1954), page 261, Table 16, we have the inverse Laplace of a function
Let
In our case, \( \gamma =-1,\eta =2\sqrt{b} \), for the first term, and \( \gamma =1,\eta =2\sqrt{b} \) for the second. Due to the linearity property of the Laplace transform,Footnote 13
Thus,
Thus, changing back the expressions for b and TÂ =Â t/Ï„m
Setting t ⟶ ∞, we obtain the steady-state solution and using the erfc properties, i.e. erfc(−∞) = 2, erfc (∞) = 0,
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Chavlis, S., Poirazi, P. (2022). Modeling Dendrites and Spatially-Distributed Neuronal Membrane Properties. In: Giugliano, M., Negrello, M., Linaro, D. (eds) Computational Modelling of the Brain. Advances in Experimental Medicine and Biology(), vol 1359. Springer, Cham. https://doi.org/10.1007/978-3-030-89439-9_2
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