Hi,
I’ve been trying to run a network using Nengo Loihi and have had a hard time conceptualizing where the various restrictions have come from, specifically the IN_AXONS_MAX and MAX_SYNAPSE_BITS. So, a couple questions based on the below code:

I would think that given an input size and a number of neurons, the number of input axons would be n_reservoir_neurons + input_size. However, it seems that in Nengo Loihi, the calculation is (2*input_size + n_reservoir_neurons), which causes a 1000 neuron, 1600 input size to crash with:
nengo.exceptions.BuildError: Input axons (4200) exceeded max (4096).
Where do the extra axons come from? 
The total number of synapses also doesn’t make sense to me. The below network seems like maximum number of synapses should be n_reservoir_neurons^{2 }, which should be well below the ~1 million total number of synapses for a Loihi core. However for a 200 input, 200 neuron network, the program crashes with:
nengo.exceptions.BuildError: Total synapse bits (1881600) exceeded max (1048576)
Why does this break the synapses limit?
Help appreciated! Right now it seems like I have to shrink each ensemble to minuscule sizes to make networks run.
neuron_type= nengo.LIF()
gain = nengo.dists.Uniform(.05, .05)
bias = nengo.dists.Uniform(0, 0)
n_reservoir_neurons = 1000
input_size = 1600
im1 = np.random.rand(input_size)
U = np.random.rand(n_reservoir_neurons, input_size)
J = np.random.rand(n_reservoir_neurons, n_reservoir_neurons)
with nengo.Network() as model:
# link the input
f_in = nengo.Node(im1, size_out=input_size)
# create reservoir neurons
A = nengo.Ensemble(n_neurons=n_reservoir_neurons, dimensions=1, neuron_type=neuron_type, gain=gain, bias=bias)
# feedforward input connections
nengo.Connection(f_in, A.neurons, synapse=None, transform=U)
# recurrent fast connections
nengo.Connection(A.neurons, A.neurons, transform=J, synapse=.005)
with nengo_loihi.Simulator(model,progress_bar=False, target='sim', dt=.001) as sim:
sim.run(1)