The goal of Yves Burnod’s An Adaptive Neural Network: The Cerebral Cortex is to create a comprehensive model that describes the workings of the brain and which is consistent with evidence from neurobiology and the social sciences.

Indeed, Yves Burnod’s model successfully describes the workings of the entire cortex in a consistent manner, using only a few key principles; however, in places it is too abstract and general to be applicable or even confirmable by practical experimentation. Though the model makes an attempt at a solid experimental foundation, it often overlooks explaining specific examples in depth in favor of simplicity and computational elegance. This is not surprising due to the sheer amount of often conflicting experimental data drawn from a large number of different fields which use widely divergent research methodology.

The model itself draws most strongly from the fields of Artificial Intelligence, Neurobiology, and Computational Neuroscience, with relatively little theory or evidence from the social sciences. But the fact that Yves Burnod is not a linguist makes it all the more remarkable that he arrives at similar conclusions as neuroscientists like Damasio and structural linguists like Lamb. The lasting contribution of theoreticians like Burnod will be the integration of the more specialized and experimentally detailed theories of the individual fields that make up and relate to the study cognitive science.