@staticmethod<EOL><INDENT>def _build_model_eqs(traj):<DEDENT>
model_eqs = traj.model.eqs<EOL>post_eqs={}<EOL>for name_post in ['<STR_LIT:i>','<STR_LIT:e>']:<EOL><INDENT>variables_dict ={}<EOL>new_model_eqs=model_eqs.replace('<STR_LIT:POST>', name_post)<EOL>for name_pre in ['<STR_LIT:i>', '<STR_LIT:e>']:<EOL><INDENT>conn_eqs = traj.model.synaptic.eqs<EOL>new_conn_eqs = conn_eqs.replace('<STR_LIT>', name_pre)<EOL>new_model_eqs += new_conn_eqs<EOL>tau1 = traj.model.synaptic['<STR_LIT>']<EOL>tau2 = traj.model.synaptic['<STR_LIT>'+name_pre]<EOL>normalization = (tau1-tau2) / tau2<EOL>invtau1=<NUM_LIT:1.0>/tau1<EOL>invtau2 = <NUM_LIT:1.0>/tau2<EOL>variables_dict['<STR_LIT>'+name_pre] = invtau1<EOL>variables_dict['<STR_LIT>'+name_pre] = invtau2<EOL>variables_dict['<STR_LIT>'+name_pre] = normalization<EOL>variables_dict['<STR_LIT>'+name_pre] = tau1<EOL>variables_dict['<STR_LIT>'+name_pre] = tau2<EOL><DEDENT>variables_dict['<STR_LIT>'+name_post] = traj.model['<STR_LIT>'+name_post]<EOL>post_eqs[name_post] = Equations(new_model_eqs, **variables_dict)<EOL><DEDENT>return post_eqs<EOL>
Computes model equations for the excitatory and inhibitory population.

        Equation objects are created by fusing `model.eqs` and `model.synaptic.eqs`
        and replacing `PRE` by `i` (for inhibitory) or `e` (for excitatory) depending
        on the type of population.

        :return: Dictionary with 'i' equation object for inhibitory neurons and 'e' for excitatory