Generalizing the Mirror-Neuron-Model for Thinking Processes

摘要

Recently it was shown that a unique permanent network for words exists in the brain. Here words are represented by (mirror-) neurons and interconnected through nerves. However it is an open question how the nervous connections should be chosen so that meaningful sentences arise. Neurons situated far above the word level must be responsible for this purpose, both for generating and for understanding sentences. In order to find the crucial organization of these neurons it is necessary to determine what constitutes the core of information of a sentence at first. According to a definition provided by the philosopher L. Wittgenstein this core is called a "thought". In a pioneering new system consisting of several hierarchically organized networks, each of optimum structure, the concentrated information called "thought" can be obtained by means of very simple abstraction processes and at last it can be represented by a single neuron in its special network surroundings. This can be regarded as a complex or generalized neural mirror process between a sentence and a thought neuron. In addition, in higher levels of a hierarchical system these neurons can be associatively connected one after another only through nerves. In particular by means of that method, a nearly unlimited amount of different natural language texts can be produced. The described mechanism leads to a completely new paradigm about "thinking" that is very different to the ideas in the world of computer programs. The new theory is based upon the assumption that neurons do not store or process coded information but instead they only represent information. After all, these representatives of information - simple neural elements without symbols - can be traced back to original language-elements like words, sentences or even long texts originating at the outside of the brain. Tests with large coherent texts of well known poets and writers showed that the system worked correctly without any explicit grammar rules. It relied entirely on specific network structures.