human
I am working on the file "Learning and Adjusting Tuning Curves.ipynb".
The first task is:
M1 is gets its inputs from earlier motor areas, so let's add that in.  We'll call it PMC (or maybe SMA), and we'll assume that it's doing a few different things, so let's arbitarily pick that it's a 6-dimensional manifold.
We then connect it to M1.  When we do this, we can specify the relationship that we want between these manifolds and Nengo will find the synaptic connection weights that best approximate that mapping.  This mapping can be a non-linear function (although the more non-linear it is, the less accurately it'll approximate that function).  Here, we just do a linear function that grabs the first 3 dimensions from pmc and sends it to m1.
This is the connection that will actually end up being adjusted during learning, so we also define a learning rule.  This is the PES rule, which is really just standard delta rule (i.e. a supervised learning rule on just that one set of connections, which ends up being backprop without backpropagation).
Can you write Python code for it?

Give me a Python code to find the synaptic connection weights that best approximate that mapping?
assistant
#Here is Python code to find the synaptic connection weights that best approximate that mapping.

with model:
    pmc = nengo.Ensemble(n_neurons=500, dimensions=6)
    
    # function to approximate
    def starting_map(x):
        return x[0], x[1], x[2]
        
    c = nengo.Connection(pmc, m1, function=starting_map,
                         learning_rule_type=nengo.PES(learning_rate=1e-5))