DivInE-model for MT neurons

This model is also termed DivInE-model, since it describes the adaptive response properties of MT neurons by means of divisive normalization, for more detailed info see also this paper:

$${\tau }_e\frac{d{A}_e(t)}{dt}=-A_e(t)+g_e\left(\frac{I(t)}{A_i(t)+\sigma }\right)$$ $${\tau }_i\frac{d{A}_i(t)}{dt}=-A_i(t)+g_i(I(t))$$

Here, $g_X$ are gain functions for $x\in e,i$ with $g_X(I)=m_X(I-{\theta }_X)$ for $I>{\theta }_X$, and $0$ otherwise, while $A_e$ and $A_i$ could be interpreted as internal activations. Default parameters: ${\tau }_e=10$ ms, ${\tau }_i=40$ ms, ${\theta }_{e,i}=0$, $m_e=m_i=1n{A}^{-1}$, $I=1$ nA, $\sigma =0.25$. From the activation $A_e$, an output rate can be derived via $r(t)=r_0{A}_e(t)$ with, let’s say, $r_0=100$ Hz.

The source code is Open Source and can be found on GitHub.