Hi @xchoo, thank you for your reply! It was really helpful and informative and it has helped me better understand the SPA architecture. Your code examples were really helpful as well and helped clear up some confusions I had.
Can I ask some follow up questions on some of the points you mentioned?
Inputs
In your reply you mentioned about the use of the spa.Transcode module being able to use a python function to output the data to drive the model and you provided some code showing how to implement this functionality where a certain piece of data would be outputted within the first 0.5 seconds and then a different piece of data would be outputted after 0.5 seconds. If I wanted to dynamically pass data to my model would you recommend using the data argument provided by nengo_dl (done in this example) or would you recommend using the above approach where you would create a function which returns the data to be fed into the model at each timestep?
Alternative methods
You had mentioned the approach used in Spaun where a classifier was used to classify the visual inputs (would this be both the MNIST images and the task character cues (A0, A1, etc.)?) which are then mapped onto semantic pointers and how for my project I could use a DL or ML network to map the EMG signals onto semantic pointers. Would I be right in saying that for this approach I would be creating a DL network which generates the semantic pointer vectors based on the EMG input? So the neural network is in a way encoding the EMG data into a semantic pointer of predetermined dimensions.
Is this how Spaun achieved it by using this encoding approach? I had read the Spaun 2.0 thesis paper where it talks about Spauns vision module making use of a visual semantic pointer classifier to achieve its tasks, does this semantic pointer classifier implement the same logic where it uses a DL network to encode the image data into a semantic pointer vector?
Also thank you for suggesting the use of a heteroassociative memory network for my classification task. I’m going to look into implementing it as well to see how it performs with my data.
Thank you for your help and suggestions so far its greatly appreciated!