A Neuron is a brain cell with dendrites (input fibers) and a single output fiber called axon whose branches end in synapses which are the connection to the dendrites of other neurons. The axon is usually normally quite short, but can reach several meters in length.
Current Interpretation of the Neuron Network
The current understanding of the neuron network is that the state of the dendrites is the carrier of information. The signal coming from the nerves excite the dendrites and the cell uses a priority scheme depending on the strength of the input signals to produce an output signal.
Recognition is achieved by feeding the information into other neuron layers which carry the compare information. These layers only pass the information through when they match the input. It is a sort of image masking process.
Although it is possible that part of the brain might work like this there are some problems with this model.
Brain activity for identical sound clicks should expand from the nerves along a certain path determined by the masking cells meaning that each recognizable image has a location in the brain. This is not the case - all brain activities involve huge areas. With resonator chains this is normal. Millions of resonators will respond to one transmission.
Another problem is that different brain centers communicate over the gap of the two brain halves when several senses are used to recognize an object. Sometimes this communication can lead to an illusion. Seeing a cake on a TV screen can produce the associated taste without actually smelling the cake. This means there is no nerve input for the masking process and therefore it is an enigma how the masking output is produced.
The final problem is the architecture of the neuron network. A neuron network based on recognition layers must use an address scheme to discriminate between images and must bring the full image to all masks for final verification. Neuron masks involved in pre recognition must be also supplied. This would reduce the role of most brain cells to pure wire branching devices to create the necessary address space a thing that could be much better achieved by a trunk nerve.
We could apply the role of resonator chains to groups of cells which represent objects. But now it gets extremely difficult to link the objects. We have to run a wire to each place where the object is needed. With resonators there is no wiring problem.
Interpretation of the Neuron Network in this Model
In the resonator chain model the information is stored in the fibers. The longer a fiber is the more information is stored in it. I think this is in line with the observation that with learning the number of fibers and their length is increased.
The synapses are really not required for in this model if the recognition signal and the object transmission signal is provided by the cell.
To me it looks so that the synapses are the connection points for the event chains, because sequential event processing requires that the result of one resonator chain is fed as a signal into the next input chain. It seems to me that the synapses provide this mechanism.
Recognition of Similarities
In most cases it will be impossible for the brain to find a matching image in memory. In this case the brain has to identify the object with the biggest similarity.
To recognize similarities the brain can use a simple tuning process in which the synapses play an important role.
To make understanding easier I want to demonstrate the tuning process with sound.
When you have two guitars that are equally tuned and strike a string on one guitar the same string on the second guitar will produce a tone because it is in resonance.
If the guitars are out of tune the string on the second guitar will not not respond. To find out how much the receiving guitar is out of tune we turn the string's tuning button which will change the tension of the string so that it responds to a different frequency.
If the tuning is only out a little we have to turn only a little until resonance sets in, if the tuning is out a lot we have to turn a lot to get resonance and if it is too far out we get no resonance at all.
This means we now have a mechanism that can determine how similar two tones are. We can turn this result into a time when we always turn with the same speed. If similarity is high we reach resonance early, if similarity is low we reach it later and when there is no similarity we reach it never. There is a point where we have to stop turning otherwise we rip the string apart.
The same principle can be applied to a resonator chain. Assume a recognition signal gets stuck in an event chain because the next element is not in resonance. We just have to rise the electrical potential at the synapse with a constant speed. This will stretch the oscillators in the resonator chain and make them respond to different frequencies.
If the tuning provides resonance the synapse is discharged into the next resonator chain. If the tuning does not provide resonance it is increased until it reaches a maximum level and then discharged within the synapse to be ready for the next event.
Because recognition is now tied to a time function the most likely object will reach the decision making process first and other processes are automatically suppressed.
Resonator Groups
Might not work without sychronisation - PRELIMINARY
Even with the very small bandwidth of single molecules we might have not enough bandwidth to cover all possible objects created by the brain. We also need different resonators for the different senses so that the senses don't get confused.
There is a simple way to overcome this bandwidth problem by combining identical oscillators into a group. Your telephone tone dialer works this way.
There table below shows how 3 oscillators can be mixed to give 7 different tones. With 8 oscillators you get already 255 tones.
1 2 3 OFF OFF OFF OFF OFF ON TONE-1 OFF ON OFF TONE-2 OFF ON ON TONE-3 ON OFF OFF TONE-4 ON OFF ON TONE-5 ON ON OFF TONE-6 ON ON ON TONE-7
Using resonator groups as shown in the table above nature can prefabricate resonator chains without having to know about their later purpose. Groups of molecules are just copied on top of each other like adding a new standard link to a chain.
Using resonator groups has another big advantage. Communication between the spinal cord, the brain, the motor nerves and the sensory nerves can be coded, leading to a far better reaction time than a wire only selection process using logical gates.
Brain Waves
It is also interesting that the time related ups and downs of the synopses would create beat modulations that could explain the regular patterns of brain waves.
Maybe they could explain the different states of brain waves.
In a calm mental state we don't require much recognition power and we get less problems with recognition which also means less interference.
However, it might also be that we can influence the charging time by our mental state and thereby change the pattern.
Memory
Need to look after my bread and butter as well.
