arXiv subjects

Vansh Kharbanda

Publications and source records attributed to Vansh Kharbanda.

3 recordsLinked to original sources

arXiv2026

Energetics of stochastic limit-cycle oscillators: when does coupling reduce dissipation?

Non-linear oscillators serve important functions in many biological systems, including within the inner ear and neuronal networks. The sustainment of oscillations in noisy environments requires continuous energy dissipation, quantified by the steady-state entropy production rate (EPR). We study an idealized, analytically tractable model of a stochastic circular limit cycle and examine how mutual coupling in pairs and populations alters dissipation. For a single oscillator, the EPR depends on three key factors: intrinsic frequency, tangential velocity fluctuations, and mean tangential velocity. The dynamics are characterized by a dimensionless effective temperature given by the ratio of intrinsic relaxation and diffusion timescales. For radial (amplitude), phase (Kuramoto-like), and Cartesian couplings, we derive analytical expressions for the EPR and confirm them numerically. Varying the effective temperature and system size strongly influences how the EPR depends on coupling strength and, in some cases, results in qualitatively distinct behaviors. Moreover, the coupling types affect the tangential velocity distributions differently. Notably, in all cases studied, Cartesian coupling reduces the EPR relative to the uncoupled system, irrespective of effective temperature and system size. The analysis of idealized non-linear oscillators reveals that different classes of coupling interactions and competing timescales present in the oscillators have distinct effects on energy dissipation.

cond-mat.stat-mech↗

arXiv2025

An introduction to large deviations with applications in physics

These notes are based on the lectures that one of us (HT) gave at the Summer School on the "Theory of Large Deviations and Applications", held in July 2024 at Les Houches in France. They present the basic definitions and mathematical results that form the theory of large deviations, as well as many simple motivating examples of applications in statistical physics, which serve as a basis for the many other lectures given at the school that covered more specific applications in biophysics, random matrix theory, nonequilibrium systems, geophysics, and the simulation of rare events, among other topics. These notes extend the lectures, which can be accessed online, by presenting exercises and pointer references for further reading.

cond-mat.stat-mech↗

arXiv2024

Sensory adaptation in a continuum model of bacterial chemotaxis -- working range, cost-accuracy relation, and coupled systems

Sensory adaptation enables organisms to adjust their perception in a changing environment. A paradigm is bacterial chemotaxis, where the output activity of chemoreceptors is adapted to different baseline concentrations via receptor methylation. The range of internal receptor states limits the stimulus magnitude to which these systems can adapt. Here, we employ a highly idealized, Langevin-equation based model to study how the finite range of state variables affects the adaptation accuracy and the energy dissipation in individual and coupled systems. Maintaining an adaptive state requires constant energy dissipation. We show that the steady-state dissipation rate increases approximately linearly with the adaptation accuracy for varying stimulus magnitudes in the so-called perfect adaptation limit. This result complements the well-known logarithmic cost-accuracy relationship for varying chemical driving. Next, we study linearly coupled pairs of sensory units. We find that the interaction reduces the dissipation rate per unit and affects the overall cost-accuracy relationship. A coupling of the slow methylation variables results in a better accuracy than a coupling of activities. Overall, the findings highlight the significance of both the working range and collective operation mode as crucial design factors that impact the accuracy and energy expenditure of molecular adaptation networks.

q-bio.CB↗