Hi, I was thinking of a reconstruction method because look up tables looked to artificial to me but I do get it that reconstruction from network itself is very important for motor implementation. I dont know if it would be possible to convert outputs from look up tables back as an input to motor system with much efficiency. That was great Fergal !
Probably current method of converting raw input data to a 121 bits input pattern is not the best way we can provide data to CLA. If inputs also can be diffusely represented before putting them into the CLA like sensory inputs from medial lemniscus pathway that would be more awesome i guess. I did not understand much what Fergal said about adjusting those buckets, but I am looking forward to the talk. There is definitely a lot of load being put on cla due to lack of preprocessing. I have seen some projects where raw images were fed to CLA. In Humans we have retina, LGN than the cortex. As CLA is a layer of Cortex, its hard to imagine connecting optic nerve to occipital cortex directly. 4 layers in retina simplify an image to such an extent that it becomes very easy for cortex to extract invariant patterns out of it. Also LGN provides help in arranging the topography. If raw input gets preprocessed (i dont know how) that would be great. If a column is representing something and active cells in that column vary with respect to past events, then PROBABLY every cell in a column will have same connection permanence with input bits. It needs to be verified. That review paper provided by Jeff Hawkins was good. Now I am getting a surface idea of how motor reinforcement will help in better predictions. Going through these discussions, suddenly this brain appears to be so simple and comprehensible . Something great is on its way !! Aseem Hegshetye _______________________________________________ nupic mailing list [email protected] http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
