Biology subjects

Sinitskiy, A.

Publications and source records attributed to Sinitskiy, A..

3 recordsLinked to original sources

bioRxiv2024

Computational model of primitive nervous system controlling chemotaxis in early multicellular heterotrophs

AO_SCPLOWBSTRACTC_SCPLOWThis paper presents a model to study a hypothetical role of a simple nervous systems in chemotaxis in early multicellular heterotrophs. The model views the organism as a network of motor units connected by flexible fibers and driven by realistic neuron excitation functions. Through numerical simulations, we identified the parameters that maximize the survival time of the modeled organism, focusing on its ability to efficiently locate and consume food. This synchronization enhances the ability of the modeled organism to navigate toward food and avoid harmful conditions. The model is described using basic mechanical principles and highlights the relationship between motor activity and energy balance. Our results suggest that even early prototypes of neural networks might provide significant survival advantages by optimizing movement and energy use. This study offers insights into how the first primitive nervous systems might have functioned. By publishing the code used in the simulations, we hope to contribute to the toolkit of computational methods and models used for exploration of neural origin and evolution.

biophysics↗

bioRxiv2024

Simplest Model of Nervous System. IV. General Solution

In this paper, we extend our previous work on a simplified model of the nervous system by solving the general optimization problem for the evolutionary cost of the nervous system. This optimization takes into account constraints on the scales of membrane potential kinetics and sensory response function to ensure finite, biologically plausible solutions. Our analysis reduces the variational problem to a system of two integro-differential equations, which we solve asymptotically using series expansions. This study confirms the emergence of sharp finite neuronal spikes and robust sensory and motor response functions as evolutionarily optimal solutions. We note that, in principle, alternative evolutionary solutions with different biophysical interpretations might exist for this optimization problem. This work provides a rigorous mathematical framework bridging evolutionary optimization with the fundamental properties of nervous systems.

biophysics↗

bioRxiv2024

Simplest Model of Nervous System. III. Partial Optimization

This paper extends our previous work on the simplest model of a nervous system by providing an asymptotic analysis of the evolutionarily optimal solution in this model. Building on the formalism and principles established earlier, we derive an asymptotic solution to the Fokker-Planck-Kolmogorov equation for a given dynamical equation for the state of the neuron. This solution provides the stationary probability distribution for the position of an organism in its environment and the state of its nervous system. Next, exact and asymptotic solutions for the optimal motor and sensory responses to the approach of a predator are derived. These results align with biological expectations and provide a robust mathematical framework for predicting the behavior of simple nervous systems.

biophysics↗