Doubts regarding nengo bio connections and connectivity array

General Discussion
1

A simple implementation in nengo bio

import nengo
import nengo_bio as bio
import numpy as np

with nengo.Network() as model:
    ens_1 = bio.Ensemble(
        n_neurons=5,
        dimensions=1,
        locations=bio.NeuralSheetDist())

    ens_2 = bio.Ensemble(
        n_neurons=5,
        dimensions=1,
        locations=bio.NeuralSheetDist())
    
    conn = bio.Connection((ens_1,),
                          ens_2,
                          connectivity= bio.SpatiallyConstrainedConnectivity(divergence=5, sigma=0.25))

with nengo.Simulator(model, progress_bar=None) as sim:
    pass

p_conn = sim.data[conn]

print("p_conn", p_conn)

The above code gives the output:

BuiltConnection(weights={excitatory: array([[0. , 0.00276029, 0.04807454, 0. , 0. ],
[0. , 0.00552506, 0. , 0. , 0. ],
[0. , 0.04315878, 0. , 0. , 0. ],
[0. , 0. , 0.06970483, 0. , 0. ],
[0. , 0. , 0. , 0.17995318, 0.00024035]]), inhibitory: array([[-0.04103491, -0. , -0. , -0. , -0. ],
[-0.09083047, -0. , -0.00314321, -0. , -0. ],
[-0. , -0. , -0.03837767, -0. , -0. ],
[-0. , -0.24996182, -0. , -0.07804426, -0. ],
[-0. , -0. , -0. , -0. , -0. ]])}, connectivity=array([[[ True, True, True, False, False],
[ True, True, False, False, False],
[False, True, True, True, False],
[False, False, True, True, False],
[False, False, False, True, True]],
[[ True, True, False, False, False],
[ True, True, True, False, False],
[False, True, True, False, False],
[False, True, True, True, False],
[False, False, True, True, True]]]))

Here, I am interested in the connectivity array which is (2, 5, 5).

  1. In weights, we have excitatory weights and inhibitory weights, so I believe that the connectivity array p_conn[1][0] indicates excitatory connections and p_conn[1][1] indicates inhibitory connections. Is that correct?

  2. If the above statement is true, then how can a connection be both inhibitory and excitatory at the same time? For example:

print("p_conn[1][0]=", p_conn[1][0], "\n")
print("p_conn[1][1]=", p_conn[1][1])

p_conn[1][0]= [[ True, True, True, False, False],
[ True, True, False, False, False],
[False, True, True, True, False],
[False, False, True, True, False],
[False, False, False, True, True]],

p_conn[1][1]= [[ True, True, False, False, False],
[ True, True, True, False, False],
[False, True, True, False, False],
[False, True, True, True, False],
[False, False, True, True, True]]

If you see the highlighted rows, the first two value for both is True. Please clarify

  1. The way I understand this connectivity array is neuron 0 of ens_1 is connected to neuron 0 of ens_2 since it is True. So the rows represent neurons of ens_1 and the columns represent neurons of ens_2. Is that correct?

  2. These connections are one-way i.e. ens_1 to ens_2 and not recurrent connections. Correct?

  3. It is not possible to probe nengo bio connections. True?

  4. It is not possible to apply the learning rule in nengo bio connections. True?

  5. nengo bio connections are decoded connections or direct connections? How to make decoded and direct connections in nengo bio?

  6. What is the difference between sim.data[conn] and sim.data[p_conn] where p_conn = nengo.Probe[conn] and conn is a nengo connection and not a bio connection?