Implemented Gradient Diffusion

Charge spreads evenly out to neighbors (and settles to a flat
distribution)
Gaussian width influences the speed of spread
No individual compartment value exceeds the value of the inputs (so our
SAC activity will stay bounded in the range [-1 1])
Compartments are now gridded

Compartments now:
Recursively grid their line segments
Recursively turn their gridded locations into dendrite points
Should have functional synapse in/out weights
Deleted extraneous rescale function
Optimization of diffusiontest.py

The diffusion update rule can be performed in one step (method a) as
opposed to looping through and calculating each compartment separately
(method b).  This allowed for a performance boost.  For a 1000
compartment system with initial starting activities that is allowed to
run for 1000 timesteps, method b takes 40s and method a takes 28s.

This has yet to be implemented in the starburst class.
Implemented optimized diffusion code in starburst
Commented and cleaned up the starburst update activities method
Reorganization
Added white board images
Split Compartment into a Compartment and GrowingCompartment Classes
Bipolars cells have compartments registered with the retina
Bipolar compartments now hold a potential and a neurotransmitter output
Compartments store history
Starburst connects to bipolar layer

The starburst class now holds on to its inputs (not the compartments -
since they can't hold any information specific to any one starburst
cell).
The starburst compartments wire up with the appropriate bipolar layer
(On with On; Off with Off).  Connections have been visually verified.
Implemented Bipolar Class
Verified that bipolar class works using visualization and parameters from the SERS demonstration
Bipolar compartments now connect to starburst compartments
Updating SAC
Rewriting visualization class to handle SAC
Visualization rewrite complete
Added white board imags