Takeshi Ishida

Takeshi Ishida

A central challenge in the origin of life is understanding how catalytic peptide-like polymers and information-bearing nucleic acid-like polymers emerged as an interde-pendent system. This study constructs a primordial cognitive model incorporating two internal Lotka-Volterra chemical oscillators to investigate, through simulation, whether a catalytic loop, primordial tRNAs, and nucleic acids that record and amplify them, can form through the interaction of polymers represented by binary (0/1) sequences. In this model, a mechanism was introduced where the synthesis of internal oscillations pro-vides a temporal bias for 0/1 selection during polymer elongation, while generated functional sequences are protected, recorded, and re-amplified. Simulation results demonstrated that the proposed cognitive model significantly outperformed a contrast model based on random 0/1 selection in terms of the establishment rate of catalytic loops, the accumulation of functional molecules, polymer elongation, and the reduction of Shannon entropy in sequence distribution. Furthermore, this superiority was generally maintained across sensitivity analyses, including batch calculations with different ran-dom seeds. While this study is a computational model based on abstract binary se-quences and simplified translation/replication rules rather than a direct reconstruction of life's origin, it provides a working hypothesis for the interdependent emergence of catalytic function and information retention by demonstrating that internal oscillations can bias sequence exploration within a framework linking autocatalytic networks, re-cording, and group selection. Future research must verify the generality and empirical validity of this framework by expanding monomer types, evolving into multi-oscillator systems, and establishing correspondences with compartmentalized experimental sys-tems.

Takeshi Ishida

Many cephalopods such as octopus and squid change their skin color purposefully within a very short time. Furthermore, it is widely known that some octopuses have the ability to change the color and unevenness of the skin and to mimic the surroundings in short time. However, much research has not been done on the entire mimicry mechanism in which the octopus recognizes the surrounding landscape and changes the skin pattern. It seems that there is no hypothetical model to explain the whole mimicry mechanism yet. In this study, the mechanism of octopus skin pattern formation was assumed to be based on the Turing model. Here, the pattern formation by the Turing model was realized by the equivalent filter calculation model using the cellular automaton, instead of directly solving the differential equations. It was shown that this model can create various patterns with two feature parameters. Furthermore, for the eyes recognition part where two features are extracted from the Turing pattern image, our study proposed a method that can be calculated back with small amount of calculation using the characteristics of the cellular Turing pattern model. These two calculations can be expressed in the same mathematical frame based on the cellular automaton model using the convolution filter. As a result, it can be created a model which is capable of extracting features from patterns and reconstructing patterns in a short time, the model is considered to be a basic model for considering the mimicry mechanism of octopus. Also, in terms of application to machine learning, it is considered that it shows the possibility of leading to a model with a small amount of learning calculation.