Hello!

I am looking to implement a simple learning rule that goes like this: $ \Delta \omega_{ij} = \alpha(t) * \omega_{ij} $, where $\omega_[ij}$ are the connection weights between two neuron populations (say A and B) and $\alpha(t)$ is the (decoded) scalar output from a third neuron population (say C) – ignore the utility or theoretical soundness of such a rule for now.

Given that the PES rule also takes in an external signal, I was looking at how that rule is implemented in Nengo. I am somewhat confused about the part where the error signal is created in the function build_pes (see code below - nengo/builder/learning_rules.py).

```
# Create input error signal
error = Signal(shape=rule.size_in, name="PES:error")
model.add_op(Reset(error))
model.sig[rule]["in"] = error # error connection will attach here
```

I would like to use something similar for $\alpha(t)$, except it needs to be the decoded scalar value outputted by the ‘C’ population (my understanding is that the ‘error’ in PES is made up of neural activities). I am not sure how I could obtain the decoders of ‘C’ and construct the represented value.

Thank you for your time