Hiroki Kojima, Takashi Ikegami

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.

Read the full article at: arxiv.org

Moataz Medhat ElQadi, Adrian G. Dyer, Carolyn Vlasveld, Alan Dorin

Flora

Volume 304, July 2023, 152311

Understanding how changing climatic conditions are impacting flowering plants typically requires intensive effort and expense to sample a local site regularly over long periods of time. The logistics of organising detailed surveys of an extensive area to provide a wider perspective are even more inhibitive. Data on flower bloom patterns across areas stretching hundreds or thousands of kilometres, and with a temporal resolution down to 1 or 2 weeks, is nevertheless very valuable, should it be feasible to collect. To understand the potential for contemporary data to record such flowering patterns, we studied Japan, a country where cherry (Sakura, 桜) flower viewing (Hanami) is a national cultural practice stretching back hundreds of years, and in which contemporary citizens and visitors commonly photograph blossoms to share on social network sites (SNS). We employed the big data this activity creates, within an iEcology framework, by collecting images from the SNS Flickr over the decade 2008–2018. We developed a custom filtering pipeline to validate this extracted data against established databases of historical flowering times. Our results reveal unprecedented detail of the spatiotemporal pattern over which cherry blossoms seasonally sweep from southern to northern Japan during a 12 week period. They also were sufficiently sensitive to reveal a subtle out of peak season bloom. This novel approach and data source therefore provides a simultaneously broad and detailed perspective that communicates the seasonal ecological phenomenon of cherry tree flowering.

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Morteza Nattagh-Najafi, Mohammad Nabil, Rafsun Hossain Mridha and Seyed Amin Nabavizadeh

Entropy 2023, 25(6), 861

Inspired by recent observations on active self-organized critical (SOC) systems, we designed an active pile (or ant pile) model with two ingredients: beyond-threshold toppling and under-threshold active motions. By including the latter component, we were able to replace the typical power-law distribution for geometric observables with a stretched exponential fat-tailed distribution, where the exponent and decay rate are dependent on the activity’s strength (𝜁). This observation helped us to uncover a hidden connection between active SOC systems and 𝛼-stable Levy systems. We demonstrate that one can partially sweep 𝛼-stable Levy distributions by changing 𝜁. The system undergoes a crossover towards Bak–Tang–Weisenfeld (BTW) sandpiles with a power-law behavior (SOC fixed point) below a crossover point 𝜁<𝜁∗≈0.1.

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Lana Sinapayen

This paper reports on patterns exhibiting self-replication with spontaneous, inheritable mutations and exponential genetic drift in Neural Cellular Automata. Despite the models not being explicitly trained for mutation or inheritability, the descendant patterns exponentially drift away from ancestral patterns, even when the automaton is deterministic. While this is far from being the first instance of evolutionary dynamics in a cellular automaton, it is the first to do so by exploiting the power and convenience of Neural Cellular Automata, arguably increasing the space of variations and the opportunity for Open Ended Evolution.

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Richard Watson and Michael Levin

Collective Intelligence

Collective intelligence and individual intelligence are usually considered to be fundamentally different. Individual intelligence is uncontroversial. It occurs in organisms with special neural machinery, evolved by natural selection to enable cognitive and learning functions that serve the fitness benefit of the organism, and then trained through lifetime experience to maximise individual rewards. Whilst the mechanisms of individual intelligence are not fully understood, good models exist for many aspects of individual cognition and learning. Collective intelligence, in contrast, is a much more ambiguous idea. What exactly constitutes collective intelligence is often vague, and the mechanisms that might enable it are frequently domain-specific. These cannot be mechanisms selected specifically for the purpose of collective intelligence because collectives are not (except in special circumstances) evolutionary units, and it is not clear that collectives can learn the way individual intelligences do since they are not a singular locus of rewards and benefits. Here, we use examples from evolution and developmental morphogenesis to argue that these apparent distinctions are not as categorical as they appear. Breaking down such distinctions enables us to borrow from and expand existing models of individual cognition and learning as a framework for collective intelligence, in particular connectionist models familiar in the context of neural networks. We discuss how specific features of these models inform the necessary and sufficient conditions for collective intelligence, and identify current knowledge gaps as opportunities for future research.

Read the full article at: journals.sagepub.com