Friday, 8 July 2011

Brain Storms - 4 - Requirements of a target model

The human brain consists of a very large number of interconnected neurons and is capable of handling a large number of sophisticated concepts. Animals (by which I include the great apes and possibly all vertebrates) have brains made up of similar neurons and can take actions which relate to their memories of the environment in which they live. As the earlier posting, The Black Hole in Brain Research, suggests little is known about the way in which changes at the neuron level use these memories to work their way through to the resulting complex decisions leading to actions.

One way of trying to find out what is happening is to try and build a model of the likely processes and memory structures and find out what the model can do, and how far it fits with observations. This post aims to outline one possible approach to modelling the relevant thought processes and some of the observations which a successful model will need to explain.

We know enough about the brain to know that we need a large number of processors each of which is linked to a memory which can store a pattern, with communication links between processors.. The simplest action would be that a processor receives a signal over a link from another processor, compares the signal with its memory, and depending whether there is a match sends a signal to another processor. This can be considered as a single neuron acting as a logic gate.

The aim is to see how far we can model different aspects of brain activity by expanding this simple model in a way that will support complex decisions and actions. In the subsequent postings I will be looking at how research on an unconventional computer language, called CODIL, carried out some years ago, provides a good starting point for such an approach. However the work done so far falls far short of a complete brain model and it is appropriate to list some of the “goals” which will need to be addresses so that that areas where the model needs to be improved can be identified.

  1. The model must start – like a new-born baby – knowing nothing and be able to boot-strap itself up to a fully working system.
  2. A built in learning mechanism is essential
  3. There must be a mechanism to recognise patterns
  4. The pattern matching process may sometimes be exact, but will sometimes need to be “fuzzy” especially in early learning stages.
  5. There must be some way of organising patterns into categories and events.
  6. Information relating to time and distance need to be handled on a relative way.
  7. At any one time there needs to be a focus of activity (around short term memory?) but some processes will be “subconscious”
  8. Counting (beyond 1, 2, many) and formal mathematics and logic will only be relevant as tasks which the model can learn to do.
  9. There will need to be some housekeeping activities – including forgetting!
  10. The model should be capable of evolving from simple models.

Human language raises some interesting points. The brain's neural net working in parallel while speech is a sequential process. If information is to be transferred from one human to another by speech there has to be conversion processes (parallel > serial > parallel), introducing syntax. An important question is whether there is any need for special facilities in the brain to carry out this translation type task. The model will need to be able to support different cultures and languages and people with very different skills or political/religious views.

There is one factor that needed to allowed for in discussing any model. This is recursion. I have not defined a “pattern” or how it is represented – but in discussing examples it will be useful to be able to say that a particular pattern identifies a concept equivalent to the English word “Elephant”. However, in the same way that the word “Elephant” can be broken down into individual letters the pattern may also be able to be broken down into separate sub-patterns. For this reason I will use the term “Node” to represent a processor/pattern combination without needing to ask whether it is based on a single neuron, or a linked network of neurons, where the pattern is actually defined by links.  

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