arXiv subjects

Sergey Savel'ev

Publications and source records attributed to Sergey Savel'ev.

At least 19 recordsLinked to original sources

arXiv2026

Interacting particles crossing barriers: spatial statistics, choice of route taken and role of interactions

We consider the statistics of pairs of particles moving in a potential energy landscape exhibiting a choice of different pathways from a local minimum start point to the global energy minimum destination. We examine the impact of both attractive and repulsive interactions between the particles, treating these via either a soft Gaussian or a Lennard-Jones potential. We investigate the statistics of which particle arrives at the destination first, arrival-time distributions and where in the system the second particle is located when the first arrives. We find that the typical arrival time of the first particle can be significantly different to that of a single particle, sometimes being much longer and in other cases being shorter, depending on the nature of the particle interactions. In the case of soft Gaussian attractive interactions, which effectively bind the pair of particles together to form a single larger particle, the arrival time can be much longer, since bigger particles cross barriers more slowly. In contrast, the arrival time is shorter when there is a repulsive core interaction between the particles, due to excluded-area and particle-ordering effects. We investigate the probability distribution of particles' positions through the Jensen--Shannon divergence which unveils the dependence of the spatial distribution of the particles within the confining potential on the parameters of the particle interaction potentials.

cond-mat.stat-mech↗

arXiv2026

Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals

Physical reservoir computing exploits the nonlinear dynamics of physical systems to process time-dependent data with greater energy efficiency than conventional machine learning approaches. However, physical reservoirs have fixed intrinsic response timescales, whereas real-world signals combine deterministic and stochastic components across multiple timescales. Here we show, using a nanoporous niobium oxide reservoir, synthetic noisy signals and cryptocurrency-price volatility, that the relationship among noise correlation time, reservoir memory and forecast horizon determines whether correlated noise is filtered or predicted. Noise varying faster than the relevant reservoir memory and forecast horizon is averaged by the reservoir, whereas the temporal structure of slower-varying noise is sufficient for algorithmic forecasting. We introduce the reservoir memory horizon and forecasting regime index to distinguish these operating regimes. These contributions demonstrate that timescale matching can guide the encoding of input time series and development of physical reservoir architectures that filter, analyse and predict stochastic signal components across distinct temporal scales.

cs.LG↗

arXiv2026

Scalable platform enabling reservoir computing with nanoporous oxide memristors for image recognition and time series prediction

Typical mammal brains have some form of random connectivity between neurons. Reservoir computing, a neural network approach, uses random weights within its processing layer along with built-in recurrent connections and short-term, fading memory, and is shown to be time and training efficient in processing spatiotemporal signals. Here we prepared a niobium oxide-based thin film memristor device with intrinsic structural in-homogeneity in the form of random nanopores and performed computational tasks of XOR operations, image recognition, and time series prediction and reconstruction. For the latter task we chose a complex three-dimensional chaotic Lorenz-63 time series. By applying three temporal voltage waveforms individually across the device and training the readout layer with electrical current signals from a three-output physical reservoir, we achieved satisfactory prediction and reconstruction accuracy in comparison to the case of no reservoir. This work highlights the potential for scalable, on-chip devices using all-oxide reservoir systems, paving the way for energy-efficient neuromorphic electronics dealing with time signals.

cond-mat.dis-nn↗

arXiv2026

Stochastic Dynamics of Diffusive Memristor Blocks for Neuromorphic Computing

Biological systems use neural circuits to integrate input information and produce outputs. Synaptic convergence, where multiple neurons converge their inputs onto a single downstream neuron, is common in natural neural circuits. However, understanding specific computations performed by such neural blocks and implementating them in hardware requires further research. This work focuses on synaptic convergence in a simplified circuit of three spiking artificial neurons based on diffusive memristors. Numerical modelling and experiments reveal input voltage combinations that enable targeted activation of spiking for specific neuron configurations. We analyse the statistical characteristics of spiking patterns and interpret them from a computational perspective. The numerical simulations match experimental measurements. Our findings contribute to development of universal functional blocks for neuromorphic systems.

cond-mat.mes-hall↗

arXiv2024

Stochastic Instabilities of the Diffusive Memristor

Recently created diffusive memristors have garnered significant research interest owing to their distinctive capability to generate a diverse array of spike dynamics which are similar in nature to those found in biological cells. This gives the memristor an opportunity to be used in a wide range of applications, specifically within neuromorphic systems. The diffusive memristor is known to produce regular, chaotic and stochastic behaviors which leads to interesting phenomena resulting from the interactions between the behavioral properties. The interactions along with the instabilities that lead to the unique spiking phenomena are not fully understood due to the complexities associated with examining the stochastic properties within the diffusive memristor. In this work, we analyze both the classical and the noise induced bifurcations that a set of stochastic differential equations, justified through a Fokker-Planck equation used to model the diffusive memristor, can produce. Finally, we replicate the results of the numerical stochastic threshold phenomena with experimentally measured spiking.

physics.app-ph↗

arXiv2023

Memristive behavior of functionalized graphene quantum dot and polyaniline nanocomposites

Zero-dimensional graphene quantum dots (GQD) dispersed in conducting polymer matrix display a striking range of optical, mechanical, and thermoelectric properties which can be utilized to design next-generation sensors and low-cost thermoelectric. This exotic electrical property in GQDs is achieved by exploiting the concentration of the GQDs and by tailoring the functionalization of the GQDs. However, despite extensive investigation, the nonlinear resistivity behavior leading to memristive like characteristic has not been explored much. Here, we report electrical characterisation of nitrogen functionalized GQD (NGQD) embedded in a polyaniline (PANI) matrix. We observe a strong dependence of the resistance on current and voltage history, the magnitude of which depends on the NGQD concentration and temperature. We explain this memristive property using a phenomenological model of the alignment of PANI rods with a corresponding charge accumulation arising from the NGQD on its surface. The NGQD-PANI system is unique in its ability to matrix offers a unique pathway to design neuromorphic logic and synaptic architectures with crucial advantages over existing systems.

cond-mat.mes-hall↗

arXiv2022

Spatially distributed computation in cortical circuits

The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neuron's response to one dimension strongly depends on other dimensions. Here we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Resulting patterns of interference depend on interaction between stimulus dimensions. Comparison of these modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation.

q-bio.NC↗

arXiv2021

An investigation of higher order moments of empirical financial data and the implications to risk

Here, we analyse the behaviour of the higher order standardised moments of financial time series when we truncate a large data set into smaller and smaller subsets, referred to below as time windows. We look at the effect of the economic environment on the behaviour of higher order moments in these time windows. We observe two different scaling relations of higher order moments when the data sub sets' length decreases; one for longer time windows and another for the shorter time windows. These scaling relations drastically change when the time window encompasses a financial crisis. We also observe a qualitative change of higher order standardised moments compared to the gaussian values in response to a shrinking time window. We extend this analysis to incorporate the effects these scaling relations have upon risk. We decompose the return series within these time windows and carry out a Value-at-Risk calculation. In doing so, we observe the manifestation of the scaling relations through the change in the Value-at-Risk level. Moreover, we model the observed scaling laws by analysing the hierarchy of rare events on higher order moments.

econ.EM↗

arXiv2021

Non-stationary GARCH modelling for fitting higher order moments of financial series within moving time windows

Here, we have analysed a GARCH(1,1) model with the aim to fit higher order moments for different companies' stock prices. When we assume a gaussian conditional distribution, we fail to capture any empirical data when fitting the first three even moments of financial time series. We show instead that a double gaussian conditional probability distribution better captures the higher order moments of the data. To demonstrate this point, we construct regions (phase diagrams), in the fourth and sixth order standardised moment space, where a GARCH(1,1) model can be used to fit these moments and compare them with the corresponding moments from empirical data for different sectors of the economy. We found that the ability of the GARCH model with a double gaussian conditional distribution to fit higher order moments is dictated by the time window our data spans. We can only fit data collected within specific time window lengths and only with certain parameters of the conditional double gaussian distribution. In order to incorporate the non-stationarity of financial series, we assume that the parameters of the GARCH model have time dependence.

econ.EM↗

arXiv2016

Interference of Neural Waves in Distributed Inhibition-stabilized Networks

To gain insight into the neural events responsible for visual perception of static and dynamic optical patterns, we study how neural activation spreads in arrays of inhibition-stabilized neural networks with nearest-neighbor coupling. The activation generated in such networks by local stimuli propagates between locations, forming spatiotemporal waves that affect the dynamics of activation generated by stimuli separated spatially and temporally, and by stimuli with complex spatiotemporal structure. These interactions form characteristic interference patterns that make the network intrinsically selective for certain stimuli, such as modulations of luminance at specific spatial and temporal frequencies and specific velocities of visual motion. Due to the inherent nonlinearity of the network, its intrinsic tuning depends on stimulus intensity and contrast. The interference patterns have multiple features of "lateral" interactions between stimuli, well known in physiological and behavioral studies of visual systems. The diverse phenomena have been previously attributed to distinct neural circuits. Our results demonstrate how the canonical circuit can perform the diverse operations in a manner predicted by neural-wave interference.

q-bio.NC↗

arXiv2016

Chemotaxis of artificial microswimmers in active density waves

Living microorganisms are capable of a tactic response to external stimuli by swimming towards or away from the stimulus source; they do so by adapting their tactic signal transduction pathways to the environment. Their self-motility thus allows them to swim against a traveling tactic wave, whereas a simple fore-rear asymmetry argument would suggest the opposite. Their biomimetic counterpart, the artificial microswimmers, also propel themselves by harvesting kinetic energy from an active medium, but, in contrast, lack the adaptive capacity. Here we investigate the transport of artificial swimmers subject to traveling active waves and show, by means of analytical and numerical methods, that self-propelled particles can actually diffuse in either direction with respect to the wave, depending on its speed and waveform. Moreover, chiral swimmers, which move along spiraling trajectories, may diffuse preferably in a direction perpendicular to the active wave. Such a variety of tactic responses is explained by the modulation of the swimmer's diffusion inside traveling active pulses.

cond-mat.soft↗

arXiv2014

Effects of lasing in a one-dimensional quantum metamaterial

Electromagnetic pulse propagation in a quantum metamaterial - artificial, globally quantum coherent optical medium - is numerically simulated. We show that for the quantum metamaterials based on superconducting quantum bits, initialized in an easily reachable factorized state, lasing in microwave range is triggered, accompanied by the chaotization of qubit states and generation of higher harmonics. These effects may provide a tool for characterization and optimization of quantum metamaterial prototypes.

cond-mat.mes-hall↗

arXiv2012

Coherent emission from disordered arrays of driven Josephson vortices

We propose a mechanism of coherent emission from driven vortices in stacked intrinsic Josephson junctions. In contrast to super-radiance, which occurs only for highly ordered vortex lattices, we predict resonant radiation emission from weakly correlated vortex arrays. Our analytical results for the THz wave intensity, resonance frequencies, and the dependence of THz emission power on dissipation are in good agreement with the ones obtained by recent simulations.

cond-mat.supr-con↗

arXiv2011

Temperature-resonant cyclotron spectra in confined geometries

We consider a two-dimensional gas of colliding charged particles confined to finite size containers of various geometries and subjected to a uniform orthogonal magnetic field. The gas spectral densities are characterized by a broad peak at the cyclotron frequency. Unlike for infinitely extended gases, where the amplitude of the cyclotron peak grows linearly with temperature, here confinement causes such a peak to go through a maximum for an optimal temperature. In view of the fluctuation-dissipation theorem, the reported resonance effect has a direct counterpart in the electric susceptibility of the confined magnetized gas.

nlin.CD↗

arXiv2010

Asymmetry in shape causing absolute negative mobility

We propose a simple classical concept of nanodevices working in an absolute negative mobility (ANM) regime: The minimal spatial asymmetry required for ANM to occur is embedded in the geometry of the transported particle, rather than in the channel design. This allows for a tremendous simplification of device engineering, thus paving the way towards practical implementations of ANM. Operating conditions and performance of our model device are investigated, both numerically and analytically.

cond-mat.stat-mech↗

arXiv2010

Collective shuttling of attracting particles in asymmetric narrow channels

The rectification of a single file of attracting particles subjected to a low frequency ac drive is proposed as a working mechanism for particle shuttling in an asymmetric narrow channel. Increasing the particle attraction results in the file condensing, as signalled by the dramatic enhancement of the net particle current. Magnitude and direction of the current become extremely sensitive to the actual size of the condensate, which can then be made to shuttle between two docking stations, transporting particles in one direction, with an efficiency much larger than conventional diffusive models predict.

cond-mat.stat-mech↗

arXiv2009

Asymmetric long Josephson junction acting as a ratchet for a quantum field

We study the escape rate of flux quanta in a long Josephson junction having an asymmetric spatial inhomogeneous critical current density. We show that such a junction can behave as a quantum ratchet when it is driven by an ac current in the presence of a magnetic field. The rectification gives rise to an onset of the dc voltage across the junction. The usual approach of particle-like tunneling cannot describe this rectification, and a quantum field theory description is required. We also show that under definite conditions the rectification direction and, consequently the dc voltage, can change its sign when varying the temperature near the crossover temperature between the quantum and classical regimes.

cond-mat.other↗

arXiv2009

On the Temperature Dependence of the Casimir Force for Bulk Lossy Media

We discuss the limitations of the applicability of the Lifshitz formula to describe the temperature dependence of the Casimir force between two bulk lossy metals. These limitations follow from the finite sizes of the interacting bodies. Namely, Lifshitz's theory is not applicable when the characteristic wavelengths of the fluctuating fields, responsible for the temperature-dependent terms in the Casimir force, is longer than the sizes of the samples. As a result of this, the widely discussed linearly decreasing temperature dependence of the Casimir force can be observed only for dirty and/or large metal samples at high enough temperatures. This solves the problem of the inconsistency between the Nernst theorem and the "linearly decreasing temperature dependence" of the Casimir free energy, because this linear dependence is not valid when $T \to 0$

cond-mat.other↗