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:

\[\tau_e \frac{dA_e(t)}{dt} = -A_e(t) + g_e\left( \frac{I(t)}{A_i(t)+\sigma} \right)\] \[\tau_i \frac{dA_i(t)}{dt} = -A_i(t) + g_i\left( I(t) \right)\]

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, $\theta_{{e,i}}=0$, $m_e=m_i=1 nA^{-1}$, $I=1$ 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.