How can neuronal networks with local learning rules predict their future inputs? Local learning rules like Contrastive Hebbian Learning, XCAL, etc. are based on the idea of strengthening edges when the neurons they connect fire simultaneously. This causes frequent patterns in the input stream to be encoded in the layers.

I understand how this attracts similar input patterns into learned activation states. However, I fail to see how this memory can be used to predict the future input. How is it possible for neurons to predict future input streams under local learning rules?



From: Introduction Hebbian learning (2005)
see Lisman, J. E. and Otmakhova, N. A. (2001), Storage, recall, and novelty detection of sequences by the hippocampus: Elaborating on the SOCRATIC model to account for normal and aberrant effects of dopamine. Hippocampus, 11: 551–568. doi: 10.1002/hipo.1071: http://wwww.bio.brandeis.edu/lismanlab/pdf/socratic.pdf for one proposed mechanism.

in short, local learning rules can develop connections between items that occur sequentially.  if you have one network that pattern completes (i.e. identifies) the input, you can pass that identification to a second network which learns temporal associations between items.