About resonating neural networks

The concepts behind resonating neural networks can probably best be explained by means of the same mental exercise that I used for working out the core ideas. Basically, we are going to try and follow the journey that a single electrical impulse takes from start to end and translate this into a model.
Now, there are many possible starts: a single hair can move inside your inner ear, a taste cell can be activated, a ray of light can hit a cell on your retina and fire an electrical impulse, and so on. For this exercise, we are going to use the last one: the journey of a single visual impulse.

The process

So, once the cell on the retina did it’s work and the electrical impulse is activated, it travels along a nerve to the brain. From there, that single impulse will most likely activate 1 or more other neurons. These in turn will activate yet another part of the brain and so on and so on. Sometimes the activity can return to the same neurons, causing loop backs. Most likely, this activity somehow finally gets less and less until it dies out (although maybe not completely).

The model

Now, from this simple  process, lets try and create a model that roughly mimics it. So lets pick up some points of interest from the previous description:

• the electrical impulse somehow divides itself over multiple path ways.
• some of these paths die out, others grow.
• there are loop backs
• an electrical signal has a direction: it goes from one point to (an)other(s).
• an electrical signal travels along a path (axons)
• paths connect neural cells

The first thing to notice: something must be happening to the signal cause it is changing. Now, here is where I put down a rule: whatever it is that’s causing these changes, it must either be another neural cell or a pathway (axon), nothing else allowed.

And this is when the idea of resonance was born: what if there are 1 ore more neural cells reacting to the very fact that there is an electrical signal travelling along a pathway, because a cell released some sort of chemicals or some sort of interaction between the 2?  Now, suppose that there are various types of neurons, some of which can change or manipulate other parts of the neural network. These cells could be grouped together into a single cluster and react in sequence to the electrical signal, thus giving the ability to manipulate and change the very signal that caused the activity in the first place.

And thus we get to a programmable network.

A second thing to notice is the ‘split’ behaviour. If there are more neural cells than there are nerves coming into the brain, than the only way that those other neural cells can be activated, is if somehow the electrical impulse divides itself. To mimic this, I introduced the concept of  the ‘split’ instruction. This is a neuron that’s able to change the electrical signal itself by duplicating it and it’s current processing state. Each duplicate however, has small, predetermined variations, causing each signal’s path to grow apart.