1.1 --- a/thesis/cortex.org	Sun Apr 27 09:40:19 2014 -0400
     1.2 +++ b/thesis/cortex.org	Sun Apr 27 10:17:45 2014 -0400
     1.3 @@ -2822,29 +2822,29 @@
     1.4     experience vector:
     1.5  
     1.6     An /experience-index/ is an index into the grand experience vector
     1.7 -   that defines the worm's life. It is a timestamp for each set of
     1.8 +   that defines the worm's life. It is a time-stamp for each set of
     1.9     sensations the worm has experienced.
    1.10  
    1.11     First, group the experience-indices into bins according to the
    1.12     similarity of their proprioceptive data. I organize my bins into a
    1.13 -   3 level heirachy. The smallest bins have an approximate size of
    1.14 +   3 level hierarchy. The smallest bins have an approximate size of
    1.15     0.001 radians in all proprioceptive dimensions. Each higher level
    1.16     is 10x bigger than the level below it.
    1.17  
    1.18     The bins serve as a hashing function for proprioceptive data. Given
    1.19     a single piece of proprioceptive experience, the bins allow us to
    1.20 -   rapidly find all other similiar experience-indices of past
    1.21 -   expreience that had a very similiar proprioceptive configuration.
    1.22 +   rapidly find all other similar experience-indices of past
    1.23 +   experience that had a very similar proprioceptive configuration.
    1.24     When looking up a proprioceptive experience, if the smallest bin
    1.25 -   does not match any previous experience, then succesively larger
    1.26 +   does not match any previous experience, then successively larger
    1.27     bins are used until a match is found or we reach the largest bin.
    1.28     
    1.29     Given a sequence of proprioceptive input, I use the bins to
    1.30 -   generate a set of similiar experiencs for each input using the
    1.31 +   generate a set of similar experiences for each input using the
    1.32     tiered proprioceptive bins.
    1.33  
    1.34     Finally, to infer sensory data, I select the longest consecutive
    1.35 -   chain of experiences that threads through the sets of similiar
    1.36 +   chain of experiences that threads through the sets of similar
    1.37     experiences. Consecutive experience means that the experiences
    1.38     appear next to each other in the experience vector.
    1.39