THEORY OF OPERATION – HOW IT WORKS
The Intelligence Design Lab-5 has behavior that is guided by a neural navigational network system that maps out an “internal representation” of the external world, our “internal world model”. It's a vital part of our imagination. During human development it is common for this to cause children to stretch out their arms and say “I can fly!” as they run around while using this to visualize themselves navigating the sky.
This virtual critter is born with the common sense to learn how to get out of the way of an approaching shock zone by scooting around to the backside then waiting for the food to be in the clear, without any of that "coded in" the behavior it is the result of the wavelike interactions that depend on what is being mapped into the network. The ability to see itself soon getting a shock comes from periodically addressing network locations one or more time frames ahead of current time. Always thinking ahead is what makes some of the avoid locations blink on and off while the program is running.
Activity patterns in the network recreate important properties of the places mapped into the network. Signal flow propagates outward in all directions from an attractor location, where food or other thing it wants to navigate towards is located, which flows around places to avoid that it can bump into or in some way shocked by along the way. Its confidence in motor actions (forward/reverse and left/right) depend on the magnitude and direction it is actually traveling matching the magnitude and direction of the signal flow at the corresponding place it is currently at. Where there is more than one pathway the shortest path dominates, will be the first to propagate to that point and be favored. Where there are two or more paths of equal distance it may become indecisive but will soon favor one path over the others.
To establish a benchmark that assumes error free signals from parts of the brain that use dead reckoning to convert what is seen through the eyes into spatial coordinates in its external environment the program simply uses the already calculated X,Y positions that are used to place things in the virtual environment. In the real world our brain oppositely converts visual signals to these spatial X,Y locations, which a virtual environment has to instead start with. Where this dead reckoning system were added to this model and working perfectly that's what you would get for coordinates. Using the exact coordinates that the program already has provides ideal numbers to work from, which in turn gives this critter an excellent sense of where visible things are located around itself even though in this Lab its eyes cannot visually see them.
To test its place avoidance behavior a hidden moving shock zone slowly rotates counterclockwise, while the critter chases food in a clockwise direction heading straight towards the hazard. Although the test is demanding the confidence system of this intelligence strives for perfection, as does a human athlete. The relatively high confidence levels shown in the included line chart indicates that the virtual critter is having fun. In the research paper “Dynamic Grouping of Hippocampal Neural Activity During Cognitive Control of Two Spatial Frames” (see notes) that the arena and some of the navigational network is based upon it was found that; some live rats preferred to chase after the treats even though they are not hungry enough to need to eat, while others preferred to remain in the shock free center zone. Even a live animal has to first be willing to accept the challenge. For the virtual critter several If-Then statements that compare actual travel magnitude and direction to that of the internal representation is enough to make it want nothing else but to chase the food around its arena.
Getting out of the way of an approaching shock zone requires a good temporal sense of what is expected to soon happen. This was added by alternating between maps of both current cue card angular time and the next time frame ahead. Either way the time dependent room related memory RoomAvoidBit(X, Y, Time) has to be given a time frame to recall, even where that is present time. Only difference is that more than one moment in time is recalled. It this way ahead of time knows when it's in the way of the shock zone and gets out of there pronto.
After avoiding being surrounded by the approaching zone it has to have the common sense to go around to the safer zone behind and wait for the food to be in the clear, while at the same time knowing where the food is located even when it's surrounded by places to avoid that can (where signal timing is way off) block its signal activity. Where the signals from attract and avoid locations combine: the wanting to go both towards and away from the food results in it becoming nervously anxious, skittish, as are real animals with such a dilemma.
The signal timing that was found to work best closely follows Hebbian Theory. Neighboring cells that fire together, wire together a network with activity patterns that recreate the physical properties of what is in the external environment. It can also be conceptualized as a conservation of energy strategy where at each place in the network an incoming charge is transferred to uncharged neighbors on the opposite side, outgoing direction. The signal energy is moved from place to place, not destroyed then regenerated all over again.
This navigation system demonstrates how simple it is to organize a network that provides navigational intuition like we have. Even where some tweaking is possible it still works surprisingly well. It helps explain why animals (insects are also animals) seem born with a navigational ability that is there from the start. The origin of this behavior in living animals does not have to be a learned instinct that slowly developed over many millions of years of time by blundering animals passing on slightly less blundering behavioral traits to offspring. It's possible for these neural navigational networks to have existed when multicellular animals first appeared, just prior to the Cambrian Explosion. There are then no complex brain centers that had to slowly be “programmed” or hardwired. The origin of these inherent navigational behaviors may best explained by the activity patterns in these relatively simple cellular networks.
The origin of these neural networks may in part be from yet to be explained subcellular networks that work the same way in unicellular protozoans (single celled animals) such as paramecia, which have eye spots, antennae and other features once thought to only exist in multicellular animals. Testing such a hypothesis using this computer model requires additional theory, which may have a controversial title but going further into biology this way meets all of the requirements of the premise for an already proposed theory. In a case like this science requires developing the theory that already exists for this purpose, regardless of it being controversial or not.