The brain (and, by implication, the mind) have been compared to the latest technological innovation in every generation. The computer metaphor is now in vogue. Computer hardware metaphors were replaced by software metaphors and, lately, by (neuronal) network metaphors.

Metaphors are not confined to the philosophy of neurology. Architects and mathematicians, for instance, have lately come up with the structural concept of "tensegrity" to explain the phenomenon of life. The tendency of humans to see patterns and structures everywhere (even where there are none) is well documented and probably has its survival value.

Another trend is to discount these metaphors as erroneous, irrelevant, deceptive, and misleading. Understanding the mind is a recursive business, rife with self-reference. The entities or processes to which the brain is compared are also "brain-children", the results of "brain-storming", conceived by "minds". What is a computer, a software application, a communications network if not a (material) representation of cerebral events?

A necessary and sufficient connection surely exists between man-made things, tangible and intangible, and human minds. Even a gas pump has a "mind-correlate". It is also conceivable that representations of the "non-human" parts of the Universe exist in our minds, whether a-priori (not deriving from experience) or a-posteriori (dependent upon experience). This "correlation", "emulation", "simulation", "representation" (in short : close connection) between the "excretions", "output", "spin-offs", "products" of the human mind and the human mind itself - is a key to understanding it.

This claim is an instance of a much broader category of claims: that we can learn about the artist by his art, about a creator by his creation, and generally: about the origin by any of the derivatives, inheritors, successors, products and similes thereof.

This general contention is especially strong when the origin and the product share the same nature. If the origin is human (father) and the product is human (child) - there is an enormous amount of data that can be derived from the product and safely applied to the origin. The closer the origin to the product - the more we can learn about the origin from the product.

We have said that knowing the product - we can usually know the origin. The reason is that knowledge about product "collapses" the set of probabilities and increases our knowledge about the origin. Yet, the converse is not always true. The same origin can give rise to many types of entirely unrelated products. There are too many free variables here. The origin exists as a "wave function": a series of potentialities with attached probabilities, the potentials being the logically and physically possible products.

What can we learn about the origin by a crude perusal to the product? Mostly observable structural and functional traits and attributes. We cannot learn a thing about the "true nature" of the origin. We can not know the "true nature" of anything. This is the realm of metaphysics, not of physics.

Take Quantum Mechanics. It provides an astonishingly accurate description of micro-processes and of the Universe without saying much about their "essence". Modern physics strives to provide correct predictions - rather than to expound upon this or that worldview. It describes - it does not explain. Where interpretations are offered (e.g., the Copenhagen interpretation of Quantum Mechanics) they invariably run into philosophical snags. Modern science uses metaphors (e.g., particles and waves). Metaphors have proven to be useful scientific tools in the "thinking scientist's" kit. As these metaphors develop, they trace the developmental phases of the origin.

Consider the software-mind metaphor.

The computer is a "thinking machine" (however limited, simulated, recursive and mechanical). Similarly, the brain is a "thinking machine" (admittedly much more agile, versatile, non-linear, maybe even qualitatively different). Whatever the disparity between the two, they must be related to one another.

This relation is by virtue of two facts: (1) Both the brain and the computer are "thinking machines" and (2) the latter is the product of the former. Thus, the computer metaphor is an unusually tenable and potent one. It is likely to be further enhanced should organic or quantum computers transpire.

At the dawn of computing, software applications were authored serially, in machine language and with strict separation of data (called: "structures") and instruction code (called: "functions" or "procedures"). The machine language reflected the physical wiring of the hardware.

This is akin to the development of the embryonic brain (mind). In the early life of the human embryo, instructions (DNA) are also insulated from data (i.e., from amino acids and other life substances).

In early computing, databases were handled on a "listing" basis ("flat file"), were serial, and had no intrinsic relationship to one another. Early databases constituted a sort of substrate, ready to be acted upon. Only when "intermixed" in the computer (as a software application was run) were functions able to operate on structures.

This phase was followed by the "relational" organization of data (a primitive example of which is the spreadsheet). Data items were related to each other through mathematical formulas. This is the equivalent of the increasing complexity of the wiring of the brain as pregnancy progresses.

The latest evolutionary phase in programming is OOPS (Object Oriented Programming Systems). Objects are modules which encompass both data and instructions in self contained units. The user communicates with the functions performed by these objects - but not with their structure and internal processes.

Programming objects, in other words, are "black boxes" (an engineering term). The programmer is unable to tell how the object does what it does, or how does an external, useful function arise from internal, hidden functions or structures. Objects are epiphenomenal, emergent, phase transient. In short: much closer to reality as described by modern physics.

Though these black boxes communicate - it is not the communication, its speed, or efficacy which determine the overall efficiency of the system. It is the hierarchical and at the same time fuzzy organization of the objects which does the trick. Objects are organized in classes which define their (actualized and potential) properties. The object's behaviour (what it does and what it reacts to) is defined by its membership of a class of objects.

Moreover, objects can be organized in new (sub) classes while inheriting all the definitions and characteristics of the original class in addition to new properties. In a way, these newly emergent classes are the products while the classes they are derived from are the origin. This process so closely resembles natural - and especially biological - phenomena that it lends additional force to the software metaphor.

Thus, classes can be used as building blocks. Their permutations define the set of all soluble problems. It can be proven that Turing Machines are a private instance of a general, much stronger, class theory (a-la Principia Mathematica). The integration of hardware (computer, brain) and software (computer applications, mind) is done through "framework applications" which match the two elements structurally and functionally. The equivalent in the brain is sometimes called by philosophers and psychologists "a-priori categories", or "the collective unconscious".

Computers and their programming evolve. Relational databases cannot be integrated with object oriented ones, for instance. To run Java applets, a "virtual machine" needs to be embedded in the operating system. These phases closely resemble the development of the brain-mind couplet.

When is a metaphor a good metaphor? When it teaches us something new about the origin. It must possess some structural and functional resemblance. But this quantitative and observational facet is not enough. There is also a qualitative one: the metaphor must be instructive, revealing, insightful, aesthetic, and parsimonious - in short, it must constitute a theory and produce falsifiable predictions. A metaphor is also subject to logical and aesthetic rules and to the rigors of the scientific method.

If the software metaphor is correct, the brain must contain the following features:

Parity checks through back propagation of signals. The brain's electrochemical signals must move back (to the origin) and forward, simultaneously, in order to establish a feedback parity loop. The neuron cannot be a binary (two state) machine (a quantum computer is multi-state). It must have many levels of excitation (i.e., many modes of representation of information). The threshold ("all or nothing" firing) hypothesis must be wrong. Redundancy must be built into all the aspects and dimensions of the brain and its activities. Redundant hardware -different centers to perform similar tasks. Redundant communications channels with the same information simultaneously transferred across them. Redundant retrieval of data and redundant usage of obtained data (through working, "upper" memory). The basic concept of the workings of the brain must be the comparison of "representational elements" to "models of the world". Thus, a coherent picture is obtained which yields predictions and allows to manipulate the environment effectively. Many of the functions tackled by the brain must be recursive. We can expect to find that we can reduce all the activities of the brain to computational, mechanically solvable, recursive functions. The brain can be regarded as a Turing Machine and the dreams of Artificial Intelligence are likely come true. The brain must be a learning, self organizing, entity. The brain's very hardware must disassemble, reassemble, reorganize, restructure, reroute, reconnect, disconnect, and, in general, alter itself in response to data. In most man-made machines, the data is external to the processing unit. It enters and exits the machine through designated ports but does not affect the machine's structure or functioning. Not so the brain. It reconfigures itself with every bit of data. One can say that a new brain is created every time a single bit of information is processed.

Only if these six cumulative requirements are met - can we say that the software metaphor is useful.

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