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.

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

• 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

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.

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?

• 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