My model of human brain operation and human learning is that inside the brain, items of knowledge undergo an evolutionary process of selection, mutation, and elimination. Human inventiveness results from random mutations in existing items of knowledge, and weeding out mistfit mutations.

Moreover, the human brain exists not in isolation, but is in constant interaction with the body and the environment outside of the brain: it receives sensor input and sends out motor control output signals. That is, the brain as a whole functionally plays same role as that of a single McCulloch-and-Pitts like neuron, receiving sensor inputs on its input terminals and sending out a motor signal. It is possible to successfully control a simple autonomous robot with a brain consisting of a very small number (about 2 to 10) of such neuron cells. Brains of more ``intelligent'' entities would be constructed from more complex combinations of neuron cells.

What I aim to do is to extend the above picture of an artificial brain constructed from neuron cells with the following:

• Constructing the brain in such a way that the neuron cells grow automatically in response to the sensor inputs and possible other feedback signals which the brain receives from its environment (sensors in body parts and sensors receiving e.g. visual input from outside the body). New neuron cells are grown in places in the brain where there exists data that has to be (or can be) ``made more sense of''. The rate, and location, of new neuron cell growth, and the (initial) contents/structure of these new neuron cells follows from (data associated with) the parent neuron cells from which the new neuron grows.
• A slight extenstion of the above mechanism allows neuron cells to operate in ways in which they act as executable programs that undergo a process of evolution. Each neuron cell is the embodiment of one single item or aspect of behaviour, somewhat similar to a separate subroutine or line of code in a big computer program.
        Neuron cells that store useful information about aspects of successful behaviour, are ``reinforced'', which means that a ``quality figure'' associated with the neuron cell is incremented. Neuron cells that are not useful to generate successful behaviour are ``degraded'' (meaning decrementing its quality figure) and are eventually eliminated. The feedback input received by the brain after the robot has executed an action generated by the brain, for example a pleasure or pain feedback signal, is used to alter the quality figures of the sub-set of neurons which were used to generate the behaviour that caused the feedback signal.
        Neurons which, when used, consistently result in pleasure feedback are called ``good'' cells, and are high-quality cells. Likewise, neurons that consistently result in pain feedback, are called ''bad'' cells; and these ``bad'' cells are also high-quality cells, because they too store relevant information about successful robot behaviour, namely about specific types of behavior that must be avoided. Both ``good'' and ``bad'' high-quality cells contain learned knowledge that is relevant to successful behaviour of the robots; the ``good'' and ``bad'' cells apply to the same domain of information and/or behaviour (they represent complementary aspects of the same information), and it seems probable that these two kinds of neuron cells must always co-evolve.
        When a neuron cell does not receive fairly consequently the same type of feedback (that is, if it receives alternately pain and pleasure feedback) then this means that it does not store meaningful information; that is, that this particular ``subroutine'' is not a useful part of the programming of the robot. The inconsistent feedback signals cause such neuron cells to degrade their quality figure.
        High-quality cells are more likely to act as parents producing new neuron cells (which may be mutations of the parent cell, mixes of various parent cells, or may be derived in other ways from parent cells).
• Unlike classical types of evolution in which the entities that evolve exist side by side, in the above type of brain contains not only neuron cells that exist in the same way side by side, but there is additionally the interesting, and for an intelligent brain in my opinion very essential, possibility that neurons are connected in layers in a hierarchical fashion, where a layer of neurons grown ``on top of'' a more basic, earlier, layer is concerned with data and patterns on a more ``abstract'' level. Bottom layers would do preprocessing and recognize some crude ``symbols'' among the mass of raw sensory input received by the robot; and higher layers of neurons would then operate in terms of these higher-level symbols instead of the raw signals at the terminals of the brain. This means that lower layers recognize the concepts in which the higher levels ``think''.
        It is here important that the construction of this stacking of layers of neurons is different from a ``classical'' neural network of a few layers of neuron cells connected head-to-tail where the outputs of layer i are fed to the inputs of layers i + 1. Instead, motor and sensor symbols are all considered as inputs, and are presented simultaneously to the neuron cells. The motor symbol used is a random or ``trial'' choice. A neuron cell ``fires'' if the combination of sensor and motor symbols ``fits'' to the cell. If the cell fires, the cell sends an entirely new symbol (the unique name of the cell) on as input to the next, higher, layer of neurons. If the total pattern of sensor and motor symbols presented to the brain gets absorbed by a collection of ``good'' neuron cells, then the action associated with the ``trial'' motor symbol is executed; if the pattern is absorbed by ``bad'' neuron cells, the action is inhibited (not executed, or sent a ``stop'' signal). If the pattern cannot successfully be absorbed by the brain, then this means that the pattern contains new information -- as a result of which, new brain cells are grown at the places where the ``misfits'' between existing neurons and the new data are greatest.
• The above process of generation and change of neuron cells is autonomous : it is not specific to the exact nature and number of the sensor and motor input and output terminals hooked into the brain. The brain adapts dynamically to whatever new sensors and motors are inserted (or removed) from the system. This means that this brain design works for any kind of robot. Installing a blank brain into a totally new kind of robot should result in a robot that autonomously learns to behave successfully in whatever environment it is let loose in. The robot brain is the part of the robot that has this adaption of the possibilities of robot's physique to the opportunities in its environment as its specific purpose : The brain is the part that accomodates and adapts itself to act as an intermediary between both.
• The notion that the measure of optimality for an artificial brain is that the brain should control the robot in such a way that the behaviour of the robot is good for the survival of the robot. The measure or criterium for what is intelligence in the brain is inherently connected to behaviour. Measuring and assessing intelligence is impossible without observing behaviour. If a brain is not connected to sensors and robot arms, or equivalent subsystems, through which the brain interacts with an environment, it's impossible to speak of intelligence. Survival of the intelligent robot also always presupposes an environment in which the robot is placed and with which it interacts : intelligent behaviour means that the robot learns from elements in its environment how to behave in that environment in a way that is optimal for its own survival.
• Parallels can be drawn between colony organisms (such as ant colonies) and the way in which the separate neuron cells in the brain ``cooperate'' to create an apparent coherent and intelligent behaviour of the whole. Individual neuron cells are very simple, and from a combination of interacting simple elements a whole is formed that operates more ``intelligently''. This can explain how the ``intelligence'' in a (human) brain arises (emerges) from simple components which each by themselves are not very ``intelligent''.
• Consciousness, similarly as intelligence, must be measured by observing behaviour. It is not plausible to measure ``consciousness'' directly, nor is it in my opinion plausible to say that an entity that is conscious somehow directly ``feels'' its own state of conscience (like it e.g. feels pain or visually sees an object in front of it). If we observe that an entity behaves in a certain manner that exceeds a certain minimal intelligence, for example if we observe that an entity recognizes its image in a mirror as itself, then we as observers label that entity ``conscious''. This means (in my opinion) that consciousness is simply a more intense form of ``intelligence''. I believe that starting with a single neuron cell and then adding ever more neuron cells, while all the time letting the brain interact (through a suitable robot physique) with a suitably complex and challenging environment will gradually result in first an ``intelligent'' robot, and then (after even more layers of neurons and simultaneous learning are added) a ``conscious'' robot. That is : I see no reason why ``consciousness'' could not simply be a more evolved and more intense form of ``intelligence''; engaging in creation of robot brain designs aimed to create an intelligent robot by necessity would also shed light on the philosophical problems associated with consciousness.
• A longer-term goal for me is to try to connect the above with memetics. Memes, like the neuron cells above, are also entities that undergo evolution -- evolution of memes in a society is another way to describe evolution of culture. I feel that it ought to be possible to ``hook up'' sentences in a (maybe simple and artificial) language to the sybols that are presented to the brain as described above. It might be possible to construct in this way in a relatively simple way a brain that communicates with its environment not through sensor and motor signals, but instead through sentences in a language.

A terse outline of the current state of my work is given in http://www.rubinghscience.org/cv/mydesign.out (a message I posted on the pcp-discuss mailing list of the PCP project (see http://pespmc1.vub.ac.be/NUTSHELL.html)).
Detailed information on a specific part of the actual software that I've created as a part of this work can be found in http://www.rubinghscience.org/aiclub/toc.html, particularly in http://www.rubinghscience.org/aiclub/doc_cellc.txt.