The relationship between reaction-diffusion (RD) systems, characterized by continuous spatiotemporal states, and cellular automata (CA), marked by discrete spatiotemporal states, remains poorly understood. This paper delves into this relationship through an examination of a recently developed CA known as Lenia. We demonstrate that asymptotic Lenia, a variant of Lenia, can be comprehensively described by differential equations, and, unlike the original Lenia, it is independent of time-step ticks. Further, we establish that this formulation is mathematically equivalent to a generalization of the kernel-based Turing model (KT model). Stemming from these insights, we establish that asymptotic Lenia can be replicated by an RD system composed solely of diffusion and spatially local reaction terms, resulting in the simulated asymptotic Lenia based on an RD system, or “RD Lenia”. However, our RD Lenia cannot be construed as a chemical system since the reaction term fails to satisfy mass-action kinetics.

Artificial intelligence (AI) has seen drastic advances given recently powerful models with astonishing individual capacities, while the biological evolutionary strategy focuses more on collective intelligence. We seek to bridge biological collective intelligence with artificial intelligence, by studying collective motion of agents, inspired by the biological ants collectively solving tasks while using chemical pheromone for communication. We train agent in a single setting to acquire chemotaxis and duplicate the trained agents to form a population. We observe several interesting dynamics where collective intelligence is realized in our AI models, and expect to further analyze the impact of communication on collective dynamics.

We experimentally and numerically delve into the life-like behavior of an oil droplet in an aqueous surfactant solution in response to changes in the volume and composition ratio of the droplet. Much research has been dedicated to investigating living and non-living systems independently, albeit the boundary between the two remains unclear. To address this issue, we conducted experimental observations and identified several types of spontaneous motion exhibited by the oil droplet, which varied depending on its parameters. We then quantified the characteristic motion patterns utilizing analysis from multiple aspects and compared the differences between oil droplets - as an example of non-living material -and Tetrahymena thermophila - as a living system. Furthermore, in an attempt to reveal the deterministic or stochastic rule governing each system, a numerical simulation of the Langevin equations was performed.

We present a novel artificial cognitive map system using the generative deep neural networks called Variational Autoencoder / Generative Adversarial Network (VAE/GAN), which encodes input images into the latent space and the structure of the latent space is self-organized through the learning. Our results show that the distance of the predicted image is reflected in the distance of the corresponding latent vector after training, which indicates that the latent space is organized to reflect the proximity structure of the dataset. This system is also able to internally generate temporal sequences analogous to hippocampal replay/pre-play, and we found that these sequences are not just the exact replay of the past experience, and this could be the origin of creating novel sequences from the past experiences. Having this generative nature of cognition is thought as a prerequisite for artificial cognitive systems.

Based on the principle of artificial life, we developed an upper-body android called "Alter." Alter is human-like in appearance, receives sensory information from the outside via an autonomous sensor system located around the android, and moves spontaneously using two autonomous systems of internal dynamics. Its body and arms contain a central pattern generator with seven degrees of freedom and hundreds of plastic artificial neurons. We investigated Alter’s environmental adaptability and the spontaneity of is behavioral patterns. In addition, we discuss the conditions under which a robot can become lifelike.