Robust-first Computing Wiki

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====== The Infinite Brain ======
In this project, I am undertaking the task of creating an infinitely scaleable brain. The idea is to create a structure that resembles an actual neural network in a brain that has been flattened and unrolled.

===== Biological Structure Overview =====
Neurons are made up of (essentially) four components: the Soma, axons, dendrites, and synapses.

{{:people:taylor_berger:nb_neuron.gif|}} 

  * **Soma**: stores the current charge of the neuron but also leaks charge over time. Once it charges up to a certain point, the charge is released via the axon hillock and sent down the rest of the axon to the synapses
  * **Axon(s)**: the output trajectory (path?) of the neuron. Think of it as an extension cord from the Soma to the synapse
  * **Dendrite(s)**: the input to the Soma. These tree-like structures collect and disperse the charge throughout the rest of the dendrites until it is collected by the Soma or dissipates. 
  * **Synapse(s)**: responsible for transferring the charge from one neuron to the next, usually via synapse-dendrite connection or even synapse-soma

===== Model Proposal =====
I don't need to simulate down to the exact proportions, so I propose building four elements:
  * **Element_Soma**: 
    * Single element, 
    * Very low movement probability or maybe even stationary. 
    * When activated, it collects charge from all dendrites in the event window. 
    * If the charge it collects is above a certain threshold, it tells the adjacent Element_Axon to "fire". 
    * If the number of times this atom "fires" within a certain number of activations, it dies and releases any Element_Axon and Element_Dendtrite attached to it
  * **Element_Axon**: 
    * Chained together element
    * Very low movement probability or if it does move, it only moves "with" an Element_Soma or attached Element_Axon. 
    * Only responsibility is to push a "fire" event to the next Element_Axon or Element_Synapse next to it (possibly integrate myelin sheathes into this element). 
    * If not attached to either an Element_Synapse or Element_Soma, it dies.
  * **Element_Dendtrite**: 
    * Chained together element
    * Very low movement probability or if it does move, it only moves "with" an Element_Soma or attached Element_Dendtrite. 
    * Collects and perpetuates charge from stimulating Element_Synapse by pushing it in some direction along the attached Element_Dendtrites. 
    * If not attached to an Element_Dendrite or Element_Soma, it dies.
  * **Element_Synapse**: 
    * Attached to an Element_Axon
    * Very little movement or if it does move, it only moves with it's attached Element_Axon. 
    * This looks for the closest Element_Dendrite and stimulates it by injecting the current charge it received from an Element_Axon. 
    * If it is not attached to an Element_Axon, it dies. 
    * May spawn new Element_Axon/Element_Synapse pair if it is constantly stimulated to strengthen the "learned" behavior.  

===== Design Challenges =====
Given the previous information and stemming from the ever-present A-Life research cycle, some of my fears include:
  * Creating "attached" or "linked" elements. How to do this? Is it possible to do this? 
  * Growing the network?!?! How can it grow? Each soma has an exponential decay function that determines if it spawns a new soma? Use Dreg/Reg? 
  * Moving the network? Exponential decay function again? So maybe the outside edges "move" more than the internal edges?
  * Sensory neurons? Create separate elements designed to just look for things in their event window and have them stimulate themselves? Have other elements that stimulate them? I.E.: rods, cones and flash of light elements?
  * Output neurons? What can I do to interpret the output? WHAT DOES IT ALL MEAN?!?!?
  * Where can I find a u-shaped curve? Can I borrow yours?

===== Goals & Suggestions =====
  * Developing a network that grows would be rewarding in and of itself
    * Use DRegs and Regs to construct a robust network of semi-ordered structure
    * Soma elements consume regs to create new dendrites/axons
    * dentrites and axon elements consume regs to create branches
  * Trivial computation (nand gate?)
  * Nix Element_synapse