DCM-RNN is a Generalized Recurrent Neural Network accommodating the Dynamic Causal Modelling (DCM), which links the biophysical interpretability of DCM and the power of neural networks. It significantly extends the flexibility of DCM, provides unique parameter estimation methods, and offers neural network compatibility. In this abstract, we show how to incorporate neuron firing model into DCM-RNN with ease. An effective connectivity estimation experiment with simulated fMRI data shows that the influence of the firing model is substantial. Ignoring it, as the classical DCM does, can lead to degraded results.