Modelling Biological Systems with Competitive Coherence

Many living systems, from cells to brains to governments, are controlled by the activity of a small subset of their constituents. It has been argued that coherence is of evolutionary advantage and that this active subset of constituents results from competition between two processes, a Next process that brings about coherence over time, and a Now process that brings about coherence between the interior and the exterior of the system at a particular time. This competition has been termed competitive coherence and has been implemented in a toy-learning program in order to clarify the concept and to generate—and ultimately test—new hypotheses covering subjects as diverse as complexity, emergence, DNA replication, global mutations, dreaming, bioputing (computing using either the parts of biological system or the entire biological system), and equilibrium and nonequilibrium structures. Here, we show that a program using competitive coherence, Coco, can learn to respond to a simple input sequence 1, 2, 3, 2, 3, with responses to inputs that differ according to the position of the input in the sequence and hence require competition between both Next and Now processes