arXiv subjects

Sergey Saveliev

Publications and source records attributed to Sergey Saveliev.

4 recordsLinked to original sources

arXiv2025

Nonequilibrium statistics of barrier crossings with competing pathways

Many biological, chemical, and physical systems are underpinned by stochastic transitions between equilibrium states in a potential energy. Here, we consider such transitions in a minimal model with two possible competing pathways, both starting from a local potential energy minimum and eventually finding the global minimum. There is competition between the distance to travel in state space and the height of the potential energy barriers to be surmounted, for the transition to occur. One pathway has a higher energy barrier to go over, but requires traversing a shorter distance, whereas the other pathway has a lower potential barrier but it is substantially further away in configuration space. The most likely pathway taken depends on the available time for the transition process; when only a relatively short time is available, the most likely path is the one over the higher barrier. We find that upon varying temperature the overall most likely pathway can switch from one to the other. We calculate the statistics of where the barrier crossing occurs and the distribution of times taken to reach the potential minimum. Interestingly, while the configuration space statistics is complex, the time of arrival statistics is rather simple, having an exponential probability density over most of the time range. Taken together, our results show that empirically observed rates in nonequilibrium systems should not be used to infer barrier heights.

cond-mat.stat-mech↗

arXiv2024

Stress-induced Artificial neuron spiking in Diffusive memristors

Diffusive memristors owing to their ability to produce current spiking when a constant or slowly changing voltage is applied are competitive candidates for the development of artificial electronic neurons. These artificial neurons can be integrated into various prospective autonomous and robotic systems as sensors, e.g. ones implementing object grasping and classification. We report here Ag nanoparticle-based diffusive memristor prepared on a flexible polyethylene terephthalate (PET) substrate in which the electric spiking behaviour was induced by the electric voltage under an additional stimulus of external mechanical impact. By changing the magnitude and frequency of the mechanical impact, we are able to manipulate the spiking response of our artificial neuron. This functionality to control the spiking characterstics paves a pathway for the development of touch-perception sensors that can convert local pressure into electrical spikes for further processing in neural networks. We have proposed a mathematical model which captures the operation principle of the fabricated memristive sensors and qualitatively describes the measured spiking behaviour.

physics.app-ph↗

arXiv2024

Hysteresis and Self-Oscillations in an Artificial Memristive Quantum Neuron

We theoretically study an artificial neuron circuit containing a quantum memristor in the presence of relaxation and dephasing. The charge transport in the quantum element is realized via tunneling of a charge through a quantum particle which shuttles between two terminals -- a functionality reminiscent of classical diffusive memristors. We demonstrate that this physical principle enables hysteretic behavior of the current-voltage characteristics of the quantum device. In addition, being used in artificial neural circuit, the quantum switcher is able to generate self-sustained current oscillations. Our analysis reveals that these self-oscillations are triggered only in quantum regime with a moderate rate of relaxation, and cannot exist either in a purely coherent regime or at a very high decoherence. We investigate the hysteresis and instability leading to the onset of current self-oscillations and analyze their properties depending on the circuit parameters. Our results provide a generic approach to the use of quantum regimes for controlling hysteresis and generating self-oscillations.

quant-ph↗

arXiv2018

Neural wave interference and intrinsic tuning in distributed excitatory-inhibitory networks

We developed a model of cortical computation that implements key features of cortical circuitry and is capable of describing propagation of neural signals between cortical locations in response to spatially distributed stimuli. The model is based on the canonical neural circuit that consists of excitatory and inhibitory cells interacting through reciprocal connections, with recurrent feedback. The canonical circuit is used as a node in a distributed network with nearest neighbor coupling between the nodes. We find that this system is characterized by intrinsic preference for spatial frequency. The value of preferred frequency depends on the relative weights of excitatory and inhibitory connections between cells. This balance of excitation and inhibition changes as stimulus contrast increases, which is why intrinsic spatial frequency is predicted to change with contrast in a manner determined by stimulus temporal frequency. The dynamics of network preference is consistent with properties of the cortical area MT in alert macaque monkeys.

q-bio.NC↗