One of the greatest challenges in the field of computer science is to produce computer systems that are ``intelligent'' in some way. This might involve for example, the creation of a system for the guidance of a robot which is capable of moving freely in a complex environment, seeking, recognizing and manipulating a variety of objects. It might involve the creation of a system capable of communicating with humans in natural spoken human language, or of translating between human languages.
It has been observed that natural systems with these capabilities are controlled by nervous systems consisting of large numbers of neurons interconnected by axons and dendrites. Borrowing from nature, a great deal of work has gone into setting up ``neural networks'' in computers [18,33]. In these systems, a collection of simulated ``neurons'' are created, and connected so that they can pass messages. The learning that takes place is accomplished by adjusting the ``weights'' of the connections.
Organic neurons are essentially analog devices, thus when neural networks are implemented on computers, they are digital emulations of analog devices. There is a certain inefficiency involved in emulating an analog device on a digital computer. For this reason, specialized analog hardware has been developed for the more efficient implementation of artificial neural nets [60].
Neural networks, as implemented in computers, either digital or analog, are intentional mimics of organic nervous systems. They are designed to function like natural neural networks in many details. However, natural neural networks represent the solution found by evolution to the problem of creating a control system based on organic chemistry. Evolution works with the physics and chemistry of the medium in which it is embedded.
The solution that evolution found to the problem of communication between organic cells is chemical. Cells communicate by releasing chemicals that bind to and activate receptor molecules on target cells. Working within this medium, evolution created neural nets. Inter-cellular chemical communication in neural nets is ``digital'' in the sense that chemical messages are either present or not present (on or off). In this sense, a single chemical message carries only a single bit of information. More detailed information can be derived from the temporal pattern of the messages, and also the context of the message. The context can include where on the target cell body the message is applied (which influences its ``weight''), and what other messages are arriving at the same time, with which the message in question will be integrated.
It is hoped that evolving multi-cellular digital organisms will become very complex, and will contain some kind of control system that fills the functional role of the nervous system. While it seems likely that the digital nervous system would consist of a network of communicating ``cells'', it seem unlikely that this would bear much resemblance to conventional neural networks.
Compare the mechanism of inter-cellular communication in organic cells (described above), to the mechanisms of inter-process communication in computers. Processes transmit messages in the form of bit patterns, which may be of any length, and so which may contain any amount of information. Information need not be encoded into the temporal pattern of impulse trains. This fundamental difference in communication mechanisms between the digital and the organic mediums must influence the course that evolution will take as it creates information processing systems in the two mediums.
It seems highly unlikely that evolution in the digital context would produce information processing systems that would use the same forms and mechanisms as natural neural nets (e.g., weighted connections, integration of incoming messages, threshold triggered all or nothing output, thousands of connections per unit). The organic medium is a physical/chemical medium, whereas the digital medium is a logical/informational medium. That observation alone would suggest that the digital medium is better suited to the construction of information processing systems.
If this is true, then it may be possible to produce digitally based systems that have functionality equivalent to natural neural networks, but which have a much greater simplicity of structure and process. Given evolution's ability to discover the possibilities inherent in a medium, and it's complete lack of preconceptions, it would be very interesting to observe what kind of information processing systems evolution would construct in the digital medium. If evolution is capable of creating network based information processing systems, it may provide us with a new paradigm for digital ``connectionism'', that would be more natural to the digital medium than simulations of natural neural networks.