Biology subjects

Sinitskiy, A. V.

Publications and source records attributed to Sinitskiy, A. V..

4 recordsLinked to original sources

bioRxiv2023

Making Sense of Neural Networks in the Light of Evolutionary Optimization

To what extent can evolution be considered as the sole first principle that explains all properties of nervous systems? This paper proposes an innovative, mathematically rigorous perspective on understanding nervous systems from an evolutionary perspective, leveraging methods of nonequilibrium statistical physics. This approach allows for modeling an exhaustive evolutionary optimization of nervous systems irrespective of any specific molecular and cellular mechanisms, approximate neuronal models or evolutionary history events. This novel method may shed light on key properties of biological neural networks and may also have potential relevance for understanding artificial neural networks.

biophysics↗

bioRxiv2023

Simplest Model of Nervous System. II. Evolutionary Optimization

In this work, we build upon a simple model of a primitive nervous system presented in a prior companion paper. Within this model, we formulate and solve an optimization problem, aiming to mirror the process of evolutionary optimization of the nervous system. The formally derived predictions include the emergence of sharp peaks of neural activity ( spikes), an increasing sensory sensitivity to external signals and a dramatic reduction in the cost of the functioning of the nervous system due to evolutionary optimization. Our work implies that we may be able to make general predictions about the behavior and characteristics of the nervous system irrespective of specific molecular mechanisms or evolutionary trajectories. It also underscores the potential utility of evolutionary optimization as a key principle in mathematical modeling of the nervous system and offers examples of analytical derivations possible in this field. Though grounded in a simple model, our findings offer a novel perspective, merging theoretical frameworks from nonequilibrium statistical physics with evolutionary principles. This perspective may guide more comprehensive inquiries into the intricate nature of neural networks.

biophysics↗

bioRxiv2023

Simplest Model of Nervous System. I. Formalism

This study presents a novel, highly simplified model of the nervous system, inspired by one hypothetical scenario of its origin. The model is designed to accommodate both mathematical derivations and numerical simulations, offering a template for studying generalized principles and dynamics beyond the specifics of the referenced origin scenario. The model offers a holistic perspective by treating the nervous system and the environment (in their simplest forms) as parts of one system and, together with a companion paper, notes the key role of evolutionary factors (in this model, predator evasion) in shaping the properties of the nervous system. To emphasize these fundamental principles, some aspects, such as the highly dimensional nature of the networks or detailed molecular mechanisms of their functioning, are omitted in the current version. Analytically, the model facilitates insights into the stationary distribution as a solution to the Fokker-Planck equation and the corresponding effective potential and rotation (solenoidal) terms. Numerically, it generates biologically plausible (given its high abstraction) solutions and supports comprehensive sampling with limited computational resources. Noteworthy findings from the study include limitations of the commonly used weak noise approximation and the significance of rigorous mathematical analysis over heuristic interpretations of the potential. We hope that this abstract model will serve as a fruitful tool for better understanding a complete set of principles for modeling nervous systems.

biophysics↗

bioRxiv2017

Computer Simulations Predict High Structural Heterogeneity of Functional State of NMDA Receptors

It is unclear how the known atomic structures of neuronal NMDA receptors (NMDARs) relate to the functional states of NMDARs inferred from electrophysiological recordings. We address this problem by all-atom computer simulations, a method successfully applied in the past to much smaller biomolecules. Our simulations predict that four non-active cryoEM structures of NMDARs rapidly interconvert on submicrosecond timescales, and therefore, correspond to the same functional state of the receptor.

neuroscience↗