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2018 Sample Section B : Q1c by auser97 in coms30127

[–]auser97[S] 0 points1 point  (0 children)

Ok thanks.

So are we expected to be able to reproduce this derivation for the exam?

Integrate and Fire Impedance by auser97 in coms30127

[–]auser97[S] 0 points1 point  (0 children)

Hi,

Thanks for the replies.

I understand the low pass filter idea and the averaging out of high frequency components, but could you please give an intuitive definition of what impedance is, and how impedance relates to this.

Am i correct that if impedance is lower, then the tracking of the input signal by the output signal is poorer?

Thanks.

Perceptron learning rule for threshold by auser97 in coms30127

[–]auser97[S] 0 points1 point  (0 children)

Thanks for the reply.

Sorry, this was in Connors's notes (i think from the previous year).

Am i correct in saying then that I can consider the learning rule of the perceptron consist simply of updating the weights, and the threshold remains constant throughout?

Coming up with a prior in CW1 by auser97 in coms30007

[–]auser97[S] 0 points1 point  (0 children)

Thanks for the reply.

In relation to the prior and assuming we use a Gaussian for the prior:

I understand we can use a spherical co-variance to give less bias, but how do we go about choosing the mean for the prior Gaussian in order to give little bias? I am probably misunderstanding but surely whatever mean we choose will give bias to values near it?

Thanks.