#8. What is Intelligence? Part II. Human Brain as Knowledge Base [neuroscience, engineering]

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Red, theory; black, fact.

7-17-2016

A previous post's discussion of the genetic intelligence will provide the framework for this post, in which functions will be assigned to brain structures by analogy.

The CCE-structural-gene realization of the if-then rule of AI appears to have three realizations in neuroscience. These are as follows: a single neuron (dendrites, sensors; axon, effectors) the basal ganglia (striatum, sensors; globus pallidus, effectors) and the corticothalamic system (postRolandic cortex, sensors; preRolandic cortex, effectors).

Taking the last system first, the specific association of a pattern recognizer to an output to form a rule of conduct would be implemented by white-matter axons running forward in the brain. Remember that the brain's sensory parts lie at the back, and its motor, or output, parts lie at the front.

The association of one rule to the next within a block of instructions would be by the white-matter axons running front-to-back in the brain. Since the brain has no clock, unlike a computer, the instructions must all be of the if-then type even in a highly automatic block, so each rule fires when it sees evidence that the purpose of the previous rule was accomplished. This leads to a pleasing uniformity, where all instructions have the same form. 

This also illustrates how a slow knowledge base can be morphed into something surprisingly close to a fast algorithm, by keeping track of the conditional firing probabilities of the rules, and rearranging the rules in their physical storage medium so that high conditional probabilities correspond to small distances, and vice-versa.

However, in a synaptic intelligence, the "small distances" would be measured on the voltage axis relative to firing threshold, and would indicate a high readiness to fire on the part of some neuron playing the role of decision element for an if-then rule, if the usual previous rule has fired recently. An external priming signal having the form of a steady excitation, disinhibition, or deinactivation could produce this readiness. Inhibitory inputs to thalamus or excitatory inputs to cortical layer one could be such priming inputs.

The low-to-medium conditional firing probabilities would characterize if-then rules that act as jump instructions between blocks, and the basal ganglia appear to have the connections necessary to implement these. 

In Parkinson disease, the basal ganglia are disabled, and the symptoms are as follows: difficulty in getting a voluntary movement started, slowness of movements, undershoot of movements, few movements, and tremor. Except for the tremor, all these symptoms could be due to an inability to jump to the next instruction block. Patients are capable of some routine movements once they get started, and these may represent the output of a single-block program fragment.

Unproven, trial-basis rules of the "jump" type that are found to be useful could be consolidated by a burst of dopamine secretion into the striatum. Unproven, trial-basis rules of the "block" type found useful could be consolidated by dopamine secretion into prefrontal cortex. [The last two sentences express a new idea conceived during the process of keying in this post.] Both of these dopamine inputs are well established by anatomical studies.

(See Deprecated, Part 9)...influence behavior with great indirection, by changing the firing thresholds of other rules, that themselves only operate on thresholds of still other rules, and so on in a chain eventually reaching the rules that actually produce overt behavior. The chain of indirection probably passes medial to lateral over the cortex, beginning with the limbic system. (Each level of indirection may correspond to a level of indentation seen in a modern computer language such as C.) The mid-line areas are known to be part of the default-mode network (DMN), which is active in persons who are awake but lying passively and producing no overt behavior. The DMN is thus thought to be closely associated with consciousness itself, one of the greatest mysteries in neuroscience.

7-19-2016

It appears from this post and a previous post that you can take a professionally written, high-level computer program and parse it into a number of distinctive features, to borrow a term from linguistics. Such features already dealt with are the common use of if-then rules, block structuring, and levels of indentation. Each such distinctive feature will correspond to a structural feature in each of the natural intelligences, the genetic and the synaptic. Extending this concept, we would predict that features of object-oriented programming will be useful in assigning function to form in the two natural intelligences. 

For example, the concept of class may correspond to the Brodmann areas of the human brain, and the grouping of local variables with functions that operate on them may be the explanation of the cerebellum, a brain structure not yet dealt with. In the cerebellum, the local variables would be physically separate from their corresponding functions, but informationaly bound to them by an addressing procedure that uses the massive mossy-fiber/parallel-fiber array.