Models
Below I provide a basic introduction to implementations of the models in this package.
FitzHugh-Nagumo
Description
The FitzHugh-Nagumo model is a 2D approximation of the Hodgkin-Huxley model. The general form of the dynamical equations describing this model are
$\tau \dot{u} = F(u,w) + RI$
$\tau_{w} \dot{w} = G(u,w)$
where $u$ is the membrane voltage, $I$ is the input current, and $R$ is the resistance. The three gating variables of the Hodgkin-Huxley model are summarized by the single recovery variable $w$. $F(u,w)$ and $G(u,w)$ are given by the following equations:
$F(u,w) = u - \frac{1}{3}u^3-w$
$G(u,w) = b_{0}+b_{1}u-w$
Parameters
- tau1: Time constant governing equation 1
- tau2: Time constant governing equation 2
- b0: Parameter for equation 2
- b1: Parameter for equation 2
- R: Resistance
- I: Input current
Building the model
## Default parameters
params = initializeParams("FHN")
## Change some parameters
initializeParams("FHN", tau1=1.2, b0=0.5)
## Customize all parameters
initializeParams("FHN", tau1=1.2, tau2=2.5, b0=1.1, b1=1.9, R=1.2, I=1)
Simple Feed-forward
Description
A simple feed-forward network can be described by the following dynamical equation describing network connections between excitatory neurons:
$\frac{dr_{E}}{dt} = [-r_{E}+F(w\cdot r_{E}+I_{ext})]/τ$
Here, $r_E$ describes the network activity, $w$ decribes the weight of the connectivity between neurons, $I_{ext}$ is the external input to the network, and $tau$ is the time constant for the decay of network activity. The average neuronal activity is described by a sigmoid function $F(r_E)$ of the following form:
$F(x;a,\theta)=\frac{1}{1+e^{-a(x-\theta)}}-\frac{1}{1+e^{a\theta}}$
Here, $a$ describes the gain of the function and $\theta$ the threshold.
Parameters
- tau: Time constant governing network dynamics
- a: gain
- theta: threshold
- w: connection weight
- I: Input current
Building the model
## Default parameters
params = initializeParams("SFF")
## Change some parameters
initializeParams("SFF", tau1=1.2, a=0.5)
## Customize all parameters
initializeParams("SFF", tau=1, a=0.5, theta=1.1, w=1, I=1)
Wilson-Cowan
Description
The Wilson-Cowan network describes a network of neurons with distinct excitatory and inhibitory populations that project onto each other and within themselves. The network can be described by the following coupled differential equations:
$\tau_{E} \frac{dr_{E}}{dt}=-r_{E}+F_{E}(w_{EE}r_{E}-w_{EI}r_{I}+I^{ext}_{E}; a_{E}, \theta_{E})$
$\tau_{I} \frac{dr_{I}}{dt}=-r_{I}+F_{I}(w_{IE}r_{E}-w_{II}r_{I}+I^{ext}_{I}; a_{I}, \theta_{I})$
Here, $r_E(t)$ represents the average firing rate of the excitatory population at time $t$, $r_I(t)$ represents the average firing rate of the inhibitory population at time $t$, The connection strengths are represented by the four weight terms as follows: $w_{EE} (E \rightarrow E)$, $w_{EI} (I \rightarrow E)$, $w_{IE} (E \rightarrow I)$, and $w_{II} (I \rightarrow I)$. The transfer functions for each population can be represented by distinct sigmoids of the following form:
$F(x;a,\theta)=\frac{1}{1+e^{-a(x-\theta)}}-\frac{1}{1+e^{a\theta}}$
This implementation of the model is temporally coarse grained and assumes no absolute refractory period.
Parameters
- tauE: Time constant governing the excitatory network dynamics
- aE: gain term for the excitatory transfer function
- thetaE: threshold term for the excitatory transfer function
- tauI: Time constant governing the inhibitory network dynamics
- aI: gain term for the inhibitory transfer function
- thetaI: threshold term for the inhibitory transfer function
- wEE: connection strength within the excitatory network
- wEI: connection strength from the inhibitory to the excitatory network
- wIE: connection strength from the excitatory to the inhibitory network
- wII: connection strength within the inhibitory network
- IE: external input to the excitatory network
- II: external input to the inhibitory network
Building the model
## Default parameters
params = initializeParams("WC")
## Change some parameters
initializeParams("WC", tauE=1.2, aE=0.5)
## Customize all parameters
initializeParams("WC"; tauE= 1.2, tauI= 2.5, aE=0.5, thetaE=0.8, aI=1.1, thetaI=1.2, wEE=6.4, wEI=4.8, wIE=6.0, wII=1.2, IE=0.8, II=0.2)