12 - A critical view of the modeling of neural networks

Summary

This text started with a description of the organization of the human central nervous system and it ends with a description of the architecture of neurocomputers. An unattentive reader would conclude that the latter is an implementation of the former, which obviously cannot be true. The only claim is that a small but significant step towards the understanding of processes of cognition has been carried out in recent years. The most important issue is probably that recent advances have made more and more conspicuous the fact that real neural networks can be treated as physical systems. Theories can be built and predictions can be compared with experimental observations. This methodology takes the neurosciences at large closer and closer to the classical ‘hard’ sciences such as physics or chemistry. The text strives to explain some of progress in the domain and we have seen how productive the imagination of theoreticians is.

For some biologists, however, the time of theorizing about neural nets has not come yet owing to our current lack of knowledge in the field. The question is: are the models we have introduced in the text really biologically relevant? This is the issue I would like to address in this last chapter. Many considerations are inspired by the remarks which G. Toulouse gathered in the concluding address he gave at the Bat-Sheva seminar held in Jerusalem in May 1988.