I don't see the problem of differentiating inverted and non-inverted architectures, as they are both generative models. The difference lies in what you are generating. In one case, you generate the label, and give as prior information the image, in the other, you generate the image giving the label as prior information.
Both have their advantages and disadvantages, but I don't see why the 'inverted' one is not interesting.
As of the BP = PC literature, I think that showing that by simply introducing a temporal scheduling for the weight updates of PC, we are able to obtain exact BP is interesting. I agree that this variation of PC loses all the advantages that PC has over BP, but it is still important to know that it is possible to derive exact backprop from a variational free energy.
Ambitious_Smile_981 t1_iwdrmam wrote
Reply to comment by maizeq in [Project] Erlang based framework to replace backprop using predictive coding by abhitopia
I don't see the problem of differentiating inverted and non-inverted architectures, as they are both generative models. The difference lies in what you are generating. In one case, you generate the label, and give as prior information the image, in the other, you generate the image giving the label as prior information.
Both have their advantages and disadvantages, but I don't see why the 'inverted' one is not interesting.
As of the BP = PC literature, I think that showing that by simply introducing a temporal scheduling for the weight updates of PC, we are able to obtain exact BP is interesting. I agree that this variation of PC loses all the advantages that PC has over BP, but it is still important to know that it is possible to derive exact backprop from a variational free energy.