Principles of neurocomputing

 

Fig. 1.

 

This scheme is compiled from the most reliably described links between various brain regions. How to understand its operation? The main problem here is multiple feedbacks. The common method in such cases is to break the loop. Then, operation of the open circuit will become obvious. So we need to remove some links from this scheme, but which specifically? The red nucleus is a clearly supplementary structure because the motor cortex already has direct output to motoneurons. So we will throw it out together with the cerebellum. Functions of the latter are well established. It provides muscular coordination and fine-tuning. If it is damaged, patients still cam move, but their movements become more primitive, rough, and non-coordinated. The cerebellum is a typical "improvement".

 

Fig. 2.

 

This picture still may be further simplified.

 

Fig. 3.

 

The upper link on the picture is a well known reflex. The link through the basal ganglia is a bit more complicated. It combines the previous principle of stimulus-reaction with sensori-motor coordination. The last detail comes from the fact that muscular actions change the environment and these changes will be perceived by external sensors.

 

Fig. 4.

 

This scheme is already able to support lengthy computations rather than single reactions. This is a basic neurocomputer. All the rest is various improvements and feedbacks used for stabilization, coordination, and fine-tuning.

It is interesting to compare this to a Turing machine. As you see, it is not a finite-state automaton at all. States are present indeed, but they are states of the environment rather than of the machine itself. Looks like a Turing machine, a finite-state automaton, and other similar constructs have much in common, but each of them is best suited for the particular class of applications.

 

 

Copyright (c) I. Volkov, November 25, 2015