In summary, the book lacked the mechanisms necessary to construct structures with a large number of modules, and it did not provide enough examples of practical applications. The internal structure of the module is inspired by the neocortical minicolumn. The author suggests, based on analogy with columns in the cortex, that 30 or fewer nodes per module are desirable. From this the author offers us a basic internal mechanism of the module but nothing, unfortunately, about how to construct a large- scale neural network with such modules. The initial architecture to determine what can and cannot be learned is just given by the human designer, which leads to small scale networks.
Previous studies indicate that our brain consists of a large number of repeating modules.
As the author determined the basic structure of the module by reverse engineering from the brain, I believe the overall modular structure should also be determined by reverse engineering, something like evolution. We know that the process of evolution, in millions of years, gradually resulted in the increased complexity of the brain. The author has tried to select the optimal structure using the genetic algorithm, but there is a need for a more fundamental approach to accommodate the concept of evolution in the design of modular structure. Murre argues throughout the book that modularity is important in overcoming many of the problems and limitations of current neural networks. Having illustrated the basic ideas underlying CALM, the author presents handwritten digit recognition as a practical application.