An attempt to generate internal semantic concepts in a living neuronal network by autonomous segmentation of sequential response

Abstract

In recent years, attempts have been made to elucidate human consciousness, and implementation of consciousness on machines have not yet been realized. In this study, we attempted to develop a system that uses inputs from the outside world as information to generate a primitive consciousness of the living neural network. The in silico system, a preliminary step using a living neuronal network, mimics the process of language acquisition process in infant period. We think that the simple process of segmenting words can be modeled by the mutual-segmentation-hypothesis, the system forms a semantic network by associating the segmented words with the current internal state considering the history of input stimuli, based on the mutual-segmentation-hypothesis. We analyzed the learning results in the case of sentences consisting of the simple words as inputs, and found that some of the words in the semantic network were correctly learned, but some of the words in the output were not natural, because these words were output repeatedly. Furthermore, we analyzed the characteristics of I/O of the living neuronal network, in order to utilize a living neuronal network as the process of changing the internal states by input stimulation.