4 ms·
> By firing earlier it inhibits neighboring cells, creating highly sparse patterns of activity for correctly predicted inputs. This part is so vague. It seems
by mannigfaltig 9y ago
> By firing earlier it inhibits neighboring cells, creating highly sparse patterns of activity for correctly predicted inputs.
This part is so vague. It seems to lack an explanation of how interneurons inhibit other neurons nearby. Also, wouldn‘t sparsity even occur without the early firing enabled by distal pattern matching?
> When relatively few neurons are active relative to the population, then such pattern recognition is robust.
Why?
- scottmp10 9y agoIt's true that the columnar inhibition is not the best supported part of the theory but seems a very strong deduction based on many papers that don't directly support but show the same behavior as this part of the theory. We know that minicolumns exist and cells in minicolumns share receptive fields. But they aren't always active together and HTM theory provides a hypothesis for why. This very recent preprint paper out of Michael Berry's lab at Princeton shows almost identical behavior that HTM sequence memory would predict and I'm not aware of other theories that would have predicted this behavior: https://www.biorxiv.org/content/early/2017/10/03/197608 https://www.biorxiv.org/content/early/2017/10/03/197608 As far as sparsity supporting robust pattern recognition, this paper details the math that shows this: https://arxiv.org/abs/1503.07469 https://arxiv.org/abs/1503.07469
- canjobear 9y ago> When relatively few neurons are active relative to the population, then such pattern recognition is robust. At a high level, it seems that the constraint of having only a few neurons active is equivalent to a simplicity constraint, thus implementing Occam's Razor and yielding generalization.