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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.