A virtual machine of Thinking

Unfortunately, I have nothing innovative findings on human brain.
But I have some ideas on architecture of (yet another) Thinking.

Here are some thoughts on how Virtual Machines work.

Thinking maybe implemented countless echos of event-loops and kinetic-I/O for human(or yet another intelligent object) body.

Loops and kinetic-I/O connection are dynamic, stochastic and in some degree repeatable.

In this model, the sense of self consciousness is a series of Loop-rehearsal.

Loop subjective view :

 loop gets access to kinetic-I/O

Kinetic-system subjective view:

 some condition of kinetic-system and loop variance choose some (always roaring) loops.

The group of loops is activated (adopted to kinetic-I/O to control behaviour of body, and other loops to activate).

Loop is a series of operation to change kinetic-system and signal some other synapses and activate/de-activate other loops.

So, I'd like to design loop-VM.

To descrive how 'act', I used the model of interaction between kinetic-system and pool of loops. Which is inspired by human-body and cerebellum-nerve system (older part of human brain to control body dynamics).
But, at some point, loop-loop interaction can be emerged, I guess.

loop-loop interaction and self ignition mechanism is my next target.
But I have no clear idea for now.

resemblance means neighborhood

thinking about the concept of distance...

In a sea of dynamic balance,
resemblance means neighborhood.

Like text search inverted-indices. resemblance is neighborhood.

In neighborhood each (virtual) synapse affects others, vice versa.

Synapses affects neighborhood, in some sense. (But is there any 'environmental factor' other than synapse, while you are thinking about behavior of synapse connection dynamism? )

random, dynamic and plastic network is my tool to seek the number 42

hopefully, I'll post some articles on some networks which are
random,
dynamic,
plasitic,
stochastic.

For randomness of network, I have such an idea like this.

Here are 2 agents 'A' and 'B', and 1 key-value database.

'A' puts some data to the database.
'B' gets the data from the database.

The relationship of 'put'-'get' for 'A' and 'B' can be a 'network'.

When so many agents are out there, 'put'-'get' relation (network linkage) would not be stable. Sometime 'A' puts, and 'C' gets. or 'B' gets again.

If 'A'-'B' linkage were stable, there would be some reason.
1) 'A' and 'B' share almost same timing of 'get', 'put' operation.
2) Only 'A' and 'B' are connected the same database.
Then, I'd like to define 'distance' or 'closeness' on the "network" of 'A' and 'B' nodes.
For 1) case, time dimension may be used to measure the 'distance'.
For 2) case, connection (physical distance) may be used to measure the 'distance'.

So, I modeled some "random" network and distance (inverse of linkage strong).
Then I'd like to expand them to the limit.