Is there Brian2 version of this implementation? How is the Lateral Inhibition or Winner Take All connection from Inhibitory layer to Excitatory layer achieved

How is the lateral inhibition achieved in the below code

for conn_type in recurrent_conn_names:
        connName = name+conn_type[0]+name+conn_type[1]
        weightMatrix = get_matrix_from_file(weight_path + "../random/" + connName + ending + ".npy")
        connections[connName] = b.Connection(neuron_groups[connName[0:2]], neuron_groups[connName[2:4]], structure= conn_structure, 
                                                    state = "g"+conn_type[0])
        connections[connName].connect(neuron_groups[connName[0:2]], neuron_groups[connName[2:4]], weightMatrix)

    if ee_STDP_on:
        if "ee" in recurrent_conn_names:
            stdp_methods[name+"e"+name+"e"] = b.STDP(connections[name+"e"+name+"e"], eqs=eqs_stdp_ee, pre = eqs_stdp_pre_ee, 
                                                           post = eqs_stdp_post_ee, wmin=0., wmax= wmax_ee)

peter-u-diehl / stdp-mnist

Is there Brian2 version of this implementation? How is the Lateral Inhibition or Winner Take All connection from Inhibitory layer to Excitatory layer achieved #8