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TomGeorge1234 avatar TomGeorge1234 commented on August 21, 2024 1

Great, closing the issue :))

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colleenjg avatar colleenjg commented on August 21, 2024

@TomGeorge1234 Do you recall the rationale behind having "I" in the inputs keys?

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TomGeorge1234 avatar TomGeorge1234 commented on August 21, 2024

Hmm, a quick search suggests it is a hangover from a very early version of FeedForwardLayer (the DendriticCompartment class in this old demo on path integration. It was likely something to do with only wanting to overwrite a variable on a online cell update but not when you plot the rate map etc. offline...or something like that.

As far as I can tell, this doesn't matter and we could get rid of "I_temp" and just have "I". Does this match your understanding?

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colleenjg avatar colleenjg commented on August 21, 2024

Ah, I see. For my part, the most useful approach would be to update "I" only when .get_state() is called with evaluate_at="last". In this way, "I" would allow us to store the latest input firingrate from each input layer. If we use this approach, then I think we would only need keep "I_temp" if we also want to store the last input firingrates computed using .get_state(), even if it wasn't with evaluate_at="last".

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TomGeorge1234 avatar TomGeorge1234 commented on August 21, 2024

I can't see problem with this. I just pushed a change that should implement that for you. Want to let me know if it works? It's now:

RatInABox/ratinabox/Neurons.py

Lines 2672 to 2677 in 85f2a37

if evaluate_at == "last":
I = inputlayer["layer"].firingrate
inputlayer["I"] = I
else: # kick can down the road let input layer decide how to evaluate the firingrate. this is core to feedforward layer as this recursive call will backprop through the upstraem layers until it reaches a "core" (e.g. place cells) layer which will then evaluate the firingrate.
I = inputlayer["layer"].get_state(evaluate_at, max_recurrence=pass_max_recurrence, **kwargs)
V += np.matmul(w, I)

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colleenjg avatar colleenjg commented on August 21, 2024

Yeah, that looks good to me!

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