Neural Maturation Provides the Stability of Representation and the Solution for Understanding Complex Concepts

The mechanisms of the human mind remain a major mystery for science. Progress in these efforts has come from psychology, behavioral science, physiology, and biology. Especially in the past 40 years, advances in molecular biology have expanded significantly. Despite these numerous expansions in understanding the mechanisms of higher brain functions, there are still significant gaps in integrating each research result. In particular, the gaps between gene function and the overall phenotypes related to the ‘mind’ have not been elucidated at all.

Recent technological progress in realizing human-like brain functions provided by artificial intelligence has progressed day by day. However, major parts of the current astonishing progress in AI do not necessarily require an understanding of modern molecular biology.

Here, we examine the significance of neural maturation in neural network-based information processing models. In both supervised and reinforcement learning paradigms, immature transmission could learn properties similar to normal high-fidelity conditions before reaching catastrophe points. However, continuous long-term learning induced a loss of learning results. Then, I looked for genes with physiological significance for neuronal transmission fidelity. I found that the candidate gene KCNH7 is expressed at higher levels during the development of the mouse brain. In the simulated neural model with the KCNH7 channel property, the excitation threshold increased, providing a linear response property. These properties enhanced the stability of representation recall and enabled the understanding of complex concepts in unsupervised learning models. These results demonstrate the significance of neural maturation in achieving higher recognition abilities in adults.