I was wrong about that. I don't quite understand it well enough to give a proper response so I am going to see if Jeff can write it up.
The explanation I got was that you can train a temporal model by moving the letter around the image. And then when you give it a test image, you expect it to predict the letter moving in different directions. The predicted cells are apparently useful as you move up the hierarchy. Time acts as a sort of supervisor for spatial invariants. But like I said, I am going to try to get someone to do a better explanation. There was quite a lot of vision work done that would be great to capture for you guys. On Wed, Jul 17, 2013 at 8:04 AM, Quinn Liu <[email protected]> wrote: > Hi Michael and Scott, > Thank you very much for your explanations. Michael's explanation > implies that the Temporal Pooler greatly helps in spatial invariance > learning of training data which I can see working. > > But for question 3 Scott has said "No need for TP. It won't help with > spatial representations." I was hoping Scott you could expand on your > answer to what you think about how SP and TP contribute to spatial > invarience recognition. > > Best Regards, > Quinn Liu > > > On Mon, Jul 15, 2013 at 5:07 PM, Michael Ferrier < > [email protected]> wrote: > >> Hi Quinn, >> >> The older version of HTM would group together the spatial patterns that >> would tend to occur in close temporal sequence with one another, and >> produce the same output when it saw any of the spatial patterns within a >> given group. So, if a network were trained on visual input of digits >> zig-zagging through the visual field, then any individual visual feature >> (for example a vertical line) would come to be represented by a temporal >> group that responds when it is presented with a vertical line at any of >> many nearby locations, because in the training data, a vertical line is >> often seen moving from one location to another nearby location. In this way >> it would learn invariance to position. At the lowest level of the hierarchy >> it would learn invariance to position for individual small visual features, >> and at higher levels it would learn invariance for more complex and larger >> arrangements of features and whole visual objects. Invariance to other >> transformations like scale, rotation, etc. could also be learned this way >> given the appropriate training data. >> >> Like Scott said the old version of HTM worked very differently from CLA, >> but they both model the same basic principles (the CLA does so much more >> flexibly). Using a CLA region with one cell per column, a cell should >> become active when given a particular spatial pattern, but should become >> predictive when given any pattern that (during training) often occurs close >> by in temporal sequence to that spatial pattern. So, if a column's proximal >> segment represents the spatial pattern of a vertical line, then that >> column's cell should become predictive whenever a vertical line at any >> nearby position is presented, because during training a given vertical line >> is often followed by another nearby vertical line, since the training set >> is made up of animations of the visual objects smoothly zig-zagging around. >> >> And because a CLA region sends output from both its active and predictive >> cells, from the point of view of the next, higher region in the hierarchy, >> that cell is responding invariantly to any of a set of nearby vertical >> lines. This corresponds to how 'complex cells' respond in visual cortex. >> >> Does that make sense? >> >> -Mike >> >> _____________ >> Michael Ferrier >> Department of Cognitive, Linguistic and Psychological Sciences, Brown >> University >> [email protected] >> >> >> On Mon, Jul 15, 2013 at 4:23 PM, Scott Purdy <[email protected]> wrote: >> >>> Quinn, the older HTM implementations were completely different >>> algorithms and are now obsolete. >>> >>> >>> On Mon, Jul 15, 2013 at 1:09 PM, Quinn Liu <[email protected]> wrote: >>> >>>> Hi Michael, >>>> I had an additional question. In your reply you remarked that "while >>>> digit recognition was successfully modeled with the original version of >>>> HTM, that doesn't seem to be the case with CLA yet". I was wondering if you >>>> or anyone else could expand on this as I am unfamiliar with the original >>>> version of the HTM. Assuming that it is premature version of the current >>>> spatial and temporal learning algorithms how is it different? Thanks! >>>> >>>> Best Regards, >>>> Quinn Liu >>>> >>>> [email protected] >>>> >>>> >>>> On Mon, Jul 15, 2013 at 3:41 PM, Michael Ferrier < >>>> [email protected]> wrote: >>>> >>>>> Hi Fergal, >>>>> >>>>> I completely agree that a visual object recognition system would >>>>> greatly benefit from hierarchy. Causes in the world are hierarchical, and >>>>> the brain uses hierarchy to learn and represent them. The successful >>>>> vision >>>>> models using the original implementation of HTM were also hierarchical. I >>>>> was just saying that, as far as I know, this hasn't been done with CLA yet >>>>> -- according to Jeff, in their vision experiments they were just beginning >>>>> to expand beyond one layer when they stopped working on vision. >>>>> >>>>> I think that both temporal pooling (for invariance) and hierarchy are >>>>> key to using CLA for visual recognition problems, but I don't know of >>>>> anyone who has put all the pieces together yet to do visual recognition >>>>> with CLA. >>>>> >>>>> -Mike >>>>> >>>>> >>>>> >>>>> _____________ >>>>> Michael Ferrier >>>>> Department of Cognitive, Linguistic and Psychological Sciences, Brown >>>>> University >>>>> [email protected] >>>>> >>>>> >>>>> On Mon, Jul 15, 2013 at 11:44 AM, Fergal Byrne < >>>>> [email protected]> wrote: >>>>> >>>>>> >>>>>> Hi Michael, >>>>>> >>>>>> Handwritten characters are undoubtedly multi-component designs, >>>>>> which have evolved to connect with and trigger our ability to learn >>>>>> spatial, temporal and hierarchical patterns. We perceive the same >>>>>> characters even when loads of things change in fonts, and especially when >>>>>> reading different people's handwriting. We can fill in gaps and correct >>>>>> misspellings. So the learning and prediction must be several levels deep >>>>>> in >>>>>> hierarchy. >>>>>> >>>>>> In terms of bottom level mechanics, we use saccades to recognise >>>>>> and "delocalise" components such as characters, facial features, etc, in >>>>>> such a way as to allow this multi-level recognition (including a >>>>>> hierarchy >>>>>> of fixations - for strokes, junctions, topology, characters, letters, >>>>>> words, and even sentences). >>>>>> >>>>>> Speed-readers can saccade to read entire phrases and sentences at a >>>>>> time, allowing reading speeds of thousands of words per minute with >>>>>> better >>>>>> than 70% comprehension scores. With practice, I've been able to get >>>>>> scores >>>>>> in the 1-2000 wpm range. I can also read text in a mirror or upside-down >>>>>> at >>>>>> speeds approaching 50-60% of an average reader. These things could only >>>>>> be >>>>>> done using big, complex region hierarchies with vast volumes of (normal) >>>>>> reading practice. >>>>>> >>>>>> I would have predicted that a single layer CLA would struggle with >>>>>> this kind of data set, because it lacks the multi-level upward and >>>>>> downward >>>>>> structure which I feel this kind of performance requires. >>>>>> >>>>>> Regards, >>>>>> >>>>>> Fergal Byrne >>>>>> >>>>>> _______________________________________________ >>>>>> nupic mailing list >>>>>> [email protected] >>>>>> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >>>>>> >>>>>> >>>>> >>>>> _______________________________________________ >>>>> nupic mailing list >>>>> [email protected] >>>>> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >>>>> >>>>> >>>> >>>> _______________________________________________ >>>> nupic mailing list >>>> [email protected] >>>> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >>>> >>>> >>> >>> _______________________________________________ >>> nupic mailing list >>> [email protected] >>> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >>> >>> >> >> _______________________________________________ >> nupic mailing list >> [email protected] >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> >> > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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