Simplified models for describing multitasking modes in living recognition systems

The purposeof this study is to explore the possibilities and create versions of simplified languages that describe the variety of dynamic modes during the alternating execution of multiple tasks. These languages can be used for an adequate description of experimentally recorded variants of behavior in living systems under such conditions. Methods. The study was conducted using a physical methodology based on qualitative analysis of possible solutions and confirmed by the results of computational experiments. To qualitatively describe the versions of possible mechanisms for switching between dynamic modes of operation of living systems in situations presented by the environment for these systems (expressed through changes in emotional or energy states), the simplest basic model was used. This model includes two balance equations corresponding to either the first or the second solvable problem. For this system, a two-dimensional phase space is constructed, allowing for tracking characteristic changes in the trajectories of the representing points in the ’null-isocline’ system, which depend on the control parameters. Various trajectories of representing points are considered depending on the initial conditions, visually demonstrating the main modes of transitional processes in the developing system. Results and discussion. Classification of dynamic modes in the system has been conducted, depending on control parameters. Such dynamic modes form the basis for simplified language descriptions. The proposed simplified mathematical model allowed for the examination of a wide range of states and various types of its evolutionary changes in full accordance with known examples of behavior modes in living systems.  

Neuron-like modules, balance equations, multitasking, mathematical modeling