Our MetaChem framework supports the definition and combination of artificial chemistries. Here we describe an implementation of MetaChem in an object oriented language. We briefly define MetaChem, and provide an example in the form of a toy AChem: StringCatChem. We present the class hierarchy used to define MetaChem such that the implementation can run directly from a graph description of some AChem. This matches the description given by the formal framework definition. We also describe some generic functions of MetaChem that have been implemented and used in StringCatChem. This implementation is available on GitHub.
Life and cognition are inherently circular dynamical processes, and people have difficulty understanding circular causation. We give case studies illustrating some resulting confusions, and propose that the problems may lie in failing to properly distinguish between similar concepts used in different levels of description of a system, typically local and global levels.
Natural chemistry deals with non-deterministic processes, and this is reflected in some artificial chemistries. We can tune these artificial systems by manipulating the functions that define their probabilistic processes. In this work we consider different probabilistic functions for particle linking, applied to our Jordan Algebra Artificial Chemistry. We use five base functions and their variations to investigate the possible behaviours of the system, and try to connect those behaviours to different traits of the functions. We find that, while some correlations can be seen, there are unexpected behaviours that we cannot account for in our current analysis. While we can set and manipulate the probabilities in our system, it is still complex and still displays emergent behaviour that we can not fully control.
A popular hypothesis suggests that the nervous system of different organisms, from neural tissue to whole brains, may operate at or near a critical point. During the last decade, maximum entropy techniques have allowed to go beyond merely finding statistical signatures of criticality, to models directly inferred from data recorded in neural cultures, providing stronger evidence of criticality in neural activity. Nevertheless, these modeling techniques are restricted to neural cultures and have not been extended to neural tissue in living organisms. In this paper, we extend this line of research by analyzing signatures of criticality in a pairwise maximum entropy model inferred from neural recordings of C. elegans during freely-moving locomotion. From the analysis of the inferred models we find some signatures of criticality, as a divergence of the heat capacity of the system. Other indicators, such as Zipf’s distributions, were not found. However, inspecting a similar analysis based in a 2D lattice Ising ...
In this paper I present a general modelling framework for coupled fluid dynamics and chemistry problems, and apply it to the simulation of a series of complex, homeostatic reaction diffusion systems. The model can incorporate any number of chemical species and reactions. Those chemical species diffuse, react and are advected by fluid flows. I illustrate some characteristic results from the modelling of the Gray Scott reaction diffusion system with thermally resolved reactions. Extending my previous work on ecological dynamics of nonliving structures, I demonstrate that thermal homeostasis of reaction diffusion spots can occur in systems without the use of the porous wall boundary condition that has traditionally been used for the Gray Scott system. I present an initial analysis of the parameter space of this system as well as detailing the mechanism behind the thermal homeostasis.
