arXiv subjects

Wolfgang Losert

Publications and source records attributed to Wolfgang Losert.

At least 19 recordsLinked to original sources

arXiv2026

Shear effects in active models of normal and cancer cells

Mechanical properties of biological tissues, driven by passive and active forces, play a vital role in several processes ranging from development to cancer metastasis. However, the dynamical responses of cells in tissues, subject to mechanical deformations such as shear and the associated rheological properties, are not well characterized. Here, we use three-dimensional agent-based models for normal and cancer tissues to investigate their responses to simple shear as a function of cell stiffness and stochastic active forces. In the normal epithelium, with uniform strength of active force, the yield stress as a function of shear rate follows the Herschel-Bulkley form over a range of cell volume fraction. Strikingly, the shear rate dependence and the elasticity-dependent changes in the yield stress fall on master curves upon suitable scaling. To model cancer-like behavior, a certain fraction ($N_p$) of cells was chosen to have enhanced activity and decreased stiffness. As $N_p$ increases, the extent of collective cell movement decreases, transitioning from affine (collective) to non-affine (individualistic) movement, a finding that is in accord with imaging experiments. Simulations of a model of a stiff solid tumor, with radius $R_s$ embedded in normal tissue, show that as $R_s$ increases, the yield stress increases. Interestingly, the cells migrate collectively as $R_s$ increases. A Gaussian Mixture Model (GMM) and a mean field theory quantitatively account for the simulation as well as experimental results on cancerous, non-cancerous, and a mixture of these two types. The combined theoretical and experimental study establishes that heterogeneity in stiffness and activity determines non-affine movements in normal and cancer tissues.

cond-mat.soft↗

arXiv2026

AI-interpreted Optical Scattering for Robust and Focal Depth-Aware Imaging

Optical scattering has conventionally been regarded as an impediment in imaging research due to the degradation of image quality during reconstruction. Nevertheless, this study explores two cases in which optical scattering may serve a beneficial role in image reconstruction tasks. We compared the No Scattering MNIST dataset with three Scattering MNIST datasets, each generated under distinct scattering conditions. To assess the information content of the resulting speckle patterns, we employed a Variational Autoencoder (VAE) approach which achieves accuracy comparable to state-of-the-art deep learning approaches, but has an interpretable latent space. We find that scattering can enhance data robustness against spatial pixel loss by effectively distributing information. We also demonstrate that scattering can enable distinctions of focal depth information. We anticipate that these findings will contribute to more efficient imaging techniques, particularly in the presence of obstacles and three-dimensional signals.

physics.optics↗

arXiv2026

Quantum error correction and biological error correction: A structural analogy between qubits and neurons

We draw a structural analogy between quantum error correction (QEC) and error handling in neural circuits with respect to their redundant encodings and constraint-based inferences. In QEC, logical information is embedded in a protected codespace within a larger Hilbert space. A set of commuting checks (e.g. stabilizer constraints) is repeatedly evaluated to produce an error syndrome that identifies which constraints were violated without directly revealing the logical state. A decoder then maps the syndrome to a recovery operation that returns the system to the codespace and suppresses logical failure below a threshold. Neural circuits exhibit error-control strategies that can be viewed through a related biological error correction (BEC) pattern: information is distributed across multiple neurons (redundant encoding), yielding reliable collective activity from error-prone unit operations of individual neurons. The structural analogy with QEC raises the question whether collective activity may be constrained on lower-dimensional manifolds (a biological codespace), allowing recurrent circuit dynamics and mismatch signals to function as syndrome-like indicators of constraint violations, driving fast corrective dynamics and slower adaptive updates. Our structural analogy also suggests that new insights into brain-inspired algorithms for collective information processing may inform novel QEC approaches. We perform numerical experiments using simplified models of qubit and neuron dynamics to illustrate the analogy.

physics.bio-ph↗

arXiv2025

Rhythmic sharing: A bio-inspired paradigm for zero-shot adaptive learning in neural networks

The brain rapidly adapts to new contexts and learns from limited data, a coveted characteristic that artificial intelligence (AI) algorithms struggle to mimic. Inspired by the mechanical oscillatory rhythms of neural cells, we developed a learning paradigm utilizing link strength oscillations, where learning is associated with the coordination of these oscillations. Link oscillations can rapidly change coordination, allowing the network to sense and adapt to subtle contextual changes without supervision. The network becomes a generalist AI architecture, capable of predicting dynamics of multiple contexts including unseen ones. These results make our paradigm a powerful starting point for novel models of cognition. Because our paradigm is agnostic to specifics of the neural network, our study opens doors for introducing rapid adaptive learning into leading AI models.

cs.LG↗

arXiv2025

Impacts and Ejecta in Natural Granular Material

With laboratory experiments we investigate the ejecta of low-velocity (~m/s) impacts into multi-scale granular media and compare them against ejecta from impacts into mono-scale media. Impacts are into a 50 cm diameter galvanized washtub filled with fine sand that has larger diameter gravel buried below the surface is filmed with two high-speed cameras. The resulting ejecta curtain consists mainly of fine sand, and has a complex asymmetric structure that depends on the location and interaction of the ejecta with the larger gravel grains mixed into the sand. To characterize the highly heterogeneous ejecta curtain we combine three analysis techniques: Particle tracking measures the ejecta velocities and ejecta angles best in low density regions, while particle image velocimetry (PIV) elucidates average motion in dense regions, and histogram of oriented gradients (HOG) which captures directions of motion against a patterned background. We find significant asymmetries in the multi-scale ejecta's velocity distributions and ejection angles compared to the symmetry seen in the ejecta from impacts into mono-scale media. Our experiments show that larger grains under the surface impede and direct ejecta along preferential paths during the impact process.

cond-mat.soft↗

arXiv2025

Characterizing Learning in Spiking Neural Networks with Astrocyte-Like Units

Traditional artificial neural networks take inspiration from biological networks, using layers of neuron-like nodes to pass information for processing. More realistic models include spiking in the neural network, capturing the electrical characteristics more closely. However, a large proportion of brain cells are of the glial cell type, in particular astrocytes which have been suggested to play a role in performing computations. Here, we introduce a modified spiking neural network model with added astrocyte-like units in a neural network and asses their impact on learning. We implement the network as a liquid state machine and task the network with performing a chaotic time-series prediction task. We varied the number and ratio of neuron-like and astrocyte-like units in the network to examine the latter units effect on learning. We show that the combination of neurons and astrocytes together, as opposed to neural- and astrocyte-only networks, are critical for driving learning. Interestingly, we found that the highest learning rate was achieved when the ratio between astrocyte-like and neuron-like units was roughly 2 to 1, mirroring some estimates of the ratio of biological astrocytes to neurons. Our results demonstrate that incorporating astrocyte-like units which represent information across longer timescales can alter the learning rates of neural networks, and the proportion of astrocytes to neurons should be tuned appropriately to a given task.

cs.LG↗

arXiv2024

Machine learning from limited data: Predicting biological dynamics under a time-varying external input

Reservoir computing (RC) is known as a powerful machine learning approach for learning complex dynamics from limited data. Here, we use RC to predict highly stochastic dynamics of cell shapes. We find that RC is able to predict the steady state climate from very limited data. Furthermore, the RC learns the timescale of transients from only four observations. We find that these capabilities of the RC to act as a dynamic twin allows us to also infer important statistics of cell shape dynamics of unobserved conditions.

physics.bio-ph↗

arXiv2023

Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network

In this article, a correlation metric $κ_C$ is proposed for the inference of the dynamical state of neuronal networks. $κ_C$ is computed from the scaling of the correlation length with the size of the observation region, which shows qualitatively different behavior near and away from the critical point of a continuous phase transition. The implementation is first studied on a neuronal network model, where the results of this new metric coincide with those obtained from neuronal avalanche analysis, thus well characterizing the critical state of the network. The approach is further tested with brain optogenetic recordings in behaving mice from a publicly available database. Potential applications and limitations for its use with currently available optical imaging techniques are discussed.

cond-mat.dis-nn↗

arXiv2022

Experimentally measuring rolling and sliding in three-dimensional dense granular packings

We experimentally measure a three-dimensional (3D) granular system's reversibility under cyclic compression. We image the grains using a refractive-index-matched fluid, then analyze the images using the artificial intelligence of variational autoencoders. These techniques allow us to track all the grains' translations and 3D rotations with accuracy sufficient to infer sliding and rolling displacements. Our observations reveal unique roles played by 3D rotational motions in granular flows. We find that rotations and contact-point motion dominate the dynamics in the bulk, far from the perturbation's source. Furthermore, we determine that 3D rotations are irreversible under cyclic compression. Consequently, contact-point sliding, which is dissipative, accumulates throughout the cycle. Using numerical simulations whose accuracy our experiment supports, we discover that much of the dissipation occurs in the bulk, where grains rotate more than they translate. Our observations suggest that the analysis of 3D rotations is needed for understanding granular materials' unique and powerful ability to absorb and dissipate energy.

cond-mat.soft↗

arXiv2021

Memory in 3D cyclically driven granular material

We perform experimental and numerical studies of a granular system under cyclic-compression to investigate reversibility and memory effects. We focus on the quasi-static forcing of dense systems, which is most relevant to a wide range of geophysical, industrial, and astrophysical problems. We find that soft-sphere simulations with proper stiffness and friction quantitatively reproduce both the translational and rotational displacements of the grains. We then utilize these simulations to demonstrate that such systems are capable of storing the history of previous compressions. While both mean translational and rotational displacements encode such memory, the response is fundamentally different for translations compared to rotations. For translational displacements, this memory of prior forcing depends on the coefficient of static inter-particle friction, but rotational memory is not altered by the level of friction.

cond-mat.soft↗

arXiv2021

Gravity Governs Shear Localization in Confined Dense Granular Flows

Prediction of flow profiles of slowly sheared granular materials is a major geophysical and industrial challenge. Understanding the role of gravity is particularly important for future planetary exploration in varying gravitational environments. Using the principle of minimization of energy dissipation, and combining experiments and variational analysis, we disentangle the contributions of the gravitational acceleration and confining pressure on shear strain localization induced by moving fault boundaries at the bottom of a granular layer. The flow profile is independent of the gravity for geometries with a free top surface. However, under a confining pressure or if the sheared layer withstands the weight of the upper layers, increasing gravity promotes the transition from closed shear zones buried in the bulk to open ones that intersect the top surface. We show that the center position and width of the shear zone and the axial angular velocity at the top surface follow universal scaling laws when properly scaled by the gravity, applied pressure, and layer thickness. Our finding that the flow profiles lie on a universal master curve opens the possibility to predict the quasistatic shear flow of granular materials in extraterrestrial environments.

cond-mat.soft↗

arXiv2021

Effects of interparticle friction on the response of 3D cyclically compressed granular material

We numerically study the effect of inter-particle friction coefficient on the response to cyclical pure shear of spherical particles in three dimensions. We focus on the rotations and translations of grains and look at the spatial distribution of these displacements as well as their probability distribution functions. We find that with increasing friction, the shear band becomes thinner and more pronounced. At low friction, the amplitude of particle rotations is homogeneously distributed in the system and is therefore mostly independent from both the affine and non-affine particle translations. In contrast, at high friction, the rotations are strongly localized in the shear zone. This work shows the importance of studying the effects of inter-particle friction on the response of granular materials to cyclic forcing, both for a better understanding of how rotations correlate to translations in sheared granular systems, and due to the relevance of cyclic forcing for most real-world applications in planetary science and industry.

cond-mat.soft↗

arXiv2019

Reversibility of granular rotations and translations

We analyze reversibility of both displacements and rotations of spherical grains in three-dimensional compression experiments. Using transparent acrylic beads with cylindrical holes and index matching techniques, we are not only capable of tracking displacements but also, for the first time, analyze reversibility of rotations. We observe that for moderate compression amplitudes, up to one bead diameter, the translational displacements of the beads after each cycle become mostly reversible after an initial transient. By contrast, granular rotations are largely irreversible. We find a weak correlation between translational and rotational displacements, indicating that rotational reversibility depends on more subtle changes in contact distributions and contact forces between grains compared with displacement reversibility.

cond-mat.soft↗

arXiv2015

Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach

Complex phenotypic differences among different acute leukemias cannot be fully captured by analyzing the expression levels of one single molecule, such as a miR, at a time, but requires systematic analysis of large sets of miRs. While a popular approach for analysis of such datasets is principal component analysis (PCA), this method is not designed to optimally discriminate different phenotypes. Moreover, PCA and other low-dimensional representation methods yield linear or non-linear combinations of all measured miRs. Global human miR expression was measured in AML, B-ALL, and T-ALL cell lines and patient RNA samples. By systematically applying support vector machines to all measured miRs taken in dyad and triad groups, we built miR networks using cell line data and validated our findings with primary patient samples. All the coordinately transcribed members of the miR-23a cluster (which includes also miR-24 and miR-27a), known to function as tumor suppressors of acute leukemias, appeared in the AML, B-ALL and T-ALL centric networks. Subsequent qRT-PCR analysis showed that the most connected miR in the B-ALL-centric network, miR-708, is highly and specifically expressed in B-ALLs, suggesting that miR-708 might serve as a biomarker for B-ALL. This approach is systematic, quantitative, scalable, and unbiased. Rather than a single signature, our approach yields a network of signatures reflecting the redundant nature of biological signaling pathways. The network representation allows for visual analysis of all signatures by an expert and for future integration of additional information. Furthermore, each signature involves only small sets of miRs, such as dyads and triads, which are well suited for in depth validation through laboratory experiments such as loss- and gain-of-function assays designed to drive changes in leukemia cell survival, proliferation and differentiation.

q-bio.QM↗

arXiv2015

A Microstructural View of Burrowing with RoboClam

RoboClam is a burrowing technology inspired by Ensis directus, the Atlantic razor clam. Atlantic razor clams should only be strong enough to dig a few centimeters into the soil, yet they burrow to over 70 cm. The animal uses a clever trick to achieve this: by contracting its body, it agitates and locally fluidizes the soil, reducing the drag and energetic cost of burrowing. RoboClam technology, which is based on the digging mechanics of razor clams, may be valuable for subsea applications that could benefit from efficient burrowing, such as anchoring, mine detonation, and cable laying. We directly visualize the movement of soil grains during the contraction of RoboClam, using a novel index-matching technique along with particle tracking. We show that the size of the failure zone around contracting RoboClam, can be theoretically predicted from the substrate and pore fluid properties, provided that the timescale of contraction is sufficiently large. We also show that the nonaffine motions of the grains are a small fraction of the motion within the fluidized zone, affirming the relevance of a continuum model for this system, even though the grain size is comparable to the size of RoboClam.

cond-mat.soft↗

arXiv2014

Granular Dynamics during Impact

We study the impact of a projectile onto a bed of 3 mm grains immersed in an index-matched fluid. Specifically, we vary the amount of prestrain on the sample, strengthening the force chains within the system. We find this affects only the prefactor of linear depth-dependent term in the stopping force. We therefore attribute this term to pressure within the material, and not the grain-intruder friction as is sometimes suggested. Using a laser sheet scanning technique to visualize internal grain motion, a high-speed camera, and particle tracking, we can measure the trajectory of each grain throughout an impact event. Microscopically, our results indicate that weaker initial force chains result in more irreversible, plastic rearrangements during impact, suggesting static friction between grains does play a substantial role in the energy dissipation within the granular material.

cond-mat.soft↗

arXiv2013

Understanding Health and Disease with Multidimensional Single-Cell Methods

Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.

q-bio.QM↗

arXiv2013

Coexistence and Transition between Shear Zones in Slow Granular Flows

We report experiments on slow granular flows in a split-bottom Couette cell that show novel strain localization features. Nontrivial flow profiles have been observed which are shown to be the consequence of simultaneous formation of shear zones in the bulk and at the boundaries. The fluctuating band model based on a minimization principle can be fitted to the experiments over a large variation of morphology and filling height with one single fit parameter, the relative friction coefficient between wall and bulk. The possibility of multiple shear zone formation is controlled by the relative friction. Moreover, we observe that the symmetry of an initial state, with coexisting shear zones at both side walls, breaks spontaneously below a threshold value of the shear velocity. A dynamical transition between two asymmetric flow states happens over a characteristic time scale which depends on the shear strength.

cond-mat.soft↗