Pg 21 has points on evolution.  Especially interesting is the idea of fighting for territory.  And the idea that, there be limited resources, some win and some lose.  Like notes make a melody, like lights in a grid sign selectively light make writing and designs are our neurons.  Neurons usually have overlap as to what they’ll fire to.  This sloppiness gets cleaned up by inhibition of lateral cells.  This leads to dots in white lines that almost touch.  Neurons in the superficial layers are separated into columns.  This is also where NMDA is concentrated.  Things tend to synchronization, women in dorms for example.  In Israel they’ve seen 12 neurons do this.  This he calls “entraining”.  Entraining cells tend to recruit other entrainers that are equidistant.  * )(  *  )(  *

The two create a “hot spot” between them. This lattice has natural error correction.  Like a crystal, sometimes the hot spot node is needed to get over a , or inhibited by a  high threshold.  The patterns are a method of copying.  Much like

 Jefferson’s copying machine.  Now skills build on old and , therefore, have a head start.  Thermal feathers to flying feathers.  He sees hexagons that are self-replicating taking up park space.  He wonders what will happen when they meet.  Might all firing in synchrony have the advantage?  What would be the critical Number for such to reach the motor cortex?  The cortical fight for territory could be decision.  The edge of a park where a strange space is required could force new choices.  Also different patterns of hexagons could crash and make new things, like Bach hits Beethoven. The real arena for this battle may be a brodman area.  The bigger environment for this battle are our thoughts.  Memories are really partial and inexact.  And we don’t see as continuously as we believe, but fill in the gaps. The seemless present is made partially by comparing it to the past.  Inputs from fading writing on the chalkboard of our mind and biases of attention by the thalamus and amygdala and past autonomic responses pre

sensitize the hexagons.  Chaos looks random, but is partially determined by starting coordinates.  and Therefore chaotic, but not random.  Attractors are what the system goes towards in its own chaotic way, like water to a low point. A waking EEG pattern is another example.  This is like all the experiments at the cal tech with the cubes inverting and spinning.  In sleep our bodies close in on the attractor, but minds leave it.   These are all chaos within probability.  Stimulus places the cortex in one of its basins of attractors to which our chaos must converge to fill in the blanks.  To where the chaos envelopes it and ropes it into coherency.  Indeed, the raw input at sensory material only comes from the center of our visual field, the rest is filled in.  Some patterns may stick longer and harder.  Some may need constant instruction.  Much like dancers who don’t know the next step until instructed, but eventually, they get to know and generate the patterns semi-autonomously, memories would use previously configured attractors.  This could be done with stem hexagons that clone into other sensory territory.  There would be a cascade of firings approximating the attractor that would be close enough.  There could be multiple attractors in one area by taking advantage of dimensions or slant.  PG 75 NMDA explanation.  Automatic gain controls toss in a resistor when sustained high amplitude happens for 2 seconds.  The cortex neurons don’t influence each other much and don’t do CNS spontaneous firing.  The mechanism for their insulation is unknown.   pg 79, good questions non-declarative practiced memories last much longer than episodic saw-it-once-memories.  And we make up rather than think we’ve forgotten.  He likes to think of the hippocampus (actually the entorhinal cortex) playing the root notes that cause the cascade of the memory.  Then after the attractors are somewhat dug, it stores the root notes.  He got this hexagonal idea  from looking at his mother in laws tiled floor.  He applied population biology to neurology.  Populations parcellate environments into demes: isolated subpopulations that don’t often interbreed.  Barriers build between them. These are like gateways.  This whole thing like uerons in their own walled turf.  The different patterns of demes is like different neural hexagon groups  There are inhibitors between the hexagons (barriers) that keep one population from ravaging another.  When an ambiguous stimulus is seen these little hexagons denote possibilities.  Then a cloning war ensues.  The one that wins is our interpretation.  The barrier in the park analogy are inhibition or firing threshold raising in neurons.  EEGs record fights for territory.  The hexagons aren’t uniform and, therefore, can evolve.  Variation is due to blending and “terrain” messing up the copying (error detection).  An example of population spread is the spread sheet finding a niche prepared by the extinction of an old technology (typewriters).  Darwinian processes of complexity and reproduction are a law of the universe on par with chemical bonding.  “Jamais Vu” unfamiliarity with the familiar.

Intermission recaps the theory so far and says what a theory of thought must account for.