arXiv subjects

David Cai

Publications and source records attributed to David Cai.

18 recordsLinked to original sources

arXiv2026

Prospects for Direct Electron Detectors in Ultrafast Electron Diffraction and Scattering Experiments

Ultrafast electron diffraction and phonon-diffuse scattering [UED(S)] experiments make use of photo-induced changes to electron scattering intensity across 2D detectors to report on a very wide range of dynamic structural phenomena in molecules and materials. Hybrid pixel counting detectors (HPCDs) are a promising technology for improved sensitivity and signal-to-noise in UED(S) experiments, as they offer near-zero readout noise and dark counts with the possibility of new acquisition modalities (e.g., shot-to-shot normalization) due to their high frame rates. However, it is well known that HPCDs suffer from count losses at high electron fluxes even in CW beam applications. How this translates to ultrashort electron pulse exposures has yet to be determined and is critical to understanding the application of this technology to ultrafast electron scattering experiments. Here we show that count losses are significantly exacerbated in ultrafast (pulsed) experiments and that HPCDs require unconventional data handling and saturate above $\approx\!2$ electrons per pixel per pulse. This count-rate limitation presents a severe constraint on electron bunch charge when interrogating single crystal samples. Normalization strategies to optimize signal-to-noise in UED(S) and a complete model for measurement uncertainties using HPCDs are developed and tested using a large dataset. Finally, we suggest ways HPCDs could be better adapted to ultrashort pulsed beam experiments.

physics.ins-det↗

arXiv2024

Excimer Formation in Zinc-phthalocyanine Revealed using Ultrafast Electron Diffraction

The formation of excited dimer states, so called excimers, is an important phenomenon in many organic molecular semiconductors. In contrast to Frenkel exciton-polaron excited states, an excimer is long-lived and energetically low-lying due to stabilization resulting from a substantial reorganization of the inter-molecular geometry. In this letter, we show that ultrafast electron diffraction can follow the dynamics of solid-state excimer formation in polycrystalline thin films of a molecular semiconductor, revealing both the key reaction modes and the eventual structure of the emitting state. We study the prototypical organic semiconductor zinc-phthalocyanine (ZnPc) in its crystallographic $\alpha$-phase as a model excimeric system. We show, that the excimer forms in a two-step process starting with a fast dimerization ($\lesssim 0.4\, \text{ps}$) followed by a subsequent slow shear-twist motion ($14\, \text{ps}$) leading to an alignment of the $\pi$-systems of the involved monomers. Furthermore, we show that while the same excimer geomtry is present in partially fluorinated derivatives of ZnPc, the formation kinematics slow down with increasing level of fluorination.

physics.chem-ph↗

arXiv2019

Improved effective linearization of nonlinear Schr\"odinger waves by increasing nonlinearity

From among the waves whose dynamics are governed by the nonlinear Schr\"odinger (NLS) equation, we find a robust, spatiotemporally disordered family, in which waves initialized with increasing amplitudes, on average, over long time scales, effectively evolve as ever more weakly coupled collections of plane waves. In particular, the relative amount of energy contained in their coupling decays to zero with increasing wave amplitude.

nlin.PS↗

arXiv2019

Network Mechanism for Insect Olfaction

Early olfactory pathway responses to the presentation of an odor exhibit remarkably similar dynamical behavior across phyla from insects to mammals, and frequently involve transitions among quiescence, collective network oscillations, and asynchronous firing. We hypothesize that the time scales of fast excitation and fast and slow inhibition present in these networks may be the essential element underlying this similar behavior, and design an idealized, conductance-based integrate-and-fire (I&F) model to verify this hypothesis via numerical simulations. To better understand the mathematical structure underlying the common dynamical behavior across species, we derive a firing-rate (FR) model and use it to extract a slow passage through a saddle-node-on-an-invariant-circle (SNIC) bifurcation structure. We expect this bifurcation structure to provide new insights into the understanding of the dynamical behavior of neuronal assemblies and that a similar structure can be found in other sensory systems.

q-bio.NC↗

arXiv2019

Modulation-resonance mechanism for surface waves in a two-layer fluid system

We propose a Boussinesq-type model to study the surface/interfacial wave manifestation of an underlying, slowly-varying, long-wavelength, baroclinic flow in a two-layer, density-stratified system. The results of our model show numerically that, under strong nonlinearity, surface waves, with their typical wavenumber being the resonant $k_{\mathrm{res}}$, can be generated locally at the leading edge of the underlying slowly-varying, long-wavelength baroclinic flow. Here, the resonant $k_{\mathrm{res}}$ satisfies the class 3 triad resonance condition among two short-mode waves and one long-mode wave in which all waves propagate in the same direction. Moreover, when the slope of the baroclinic flow is sufficiently small, only one spatially-localized large-amplitude surface wave packet can be generated at the leading edge. This localized surface wave packet becomes high in amplitude and large in group velocity after the interaction with its surrounding waves. These results are qualitatively consistent with various experimental observations including resonant surface waves at the leading edge of an internal wave. Subsequently, we propose a mechanism, referred to as the modulation-resonance mechanism, underlying these surface phenomena, based on our numerical simulations. The proposed modulation-resonance mechanism combines the linear modulation (ray-based) theory for the spatiotemporal asymmetric behavior of surface waves and the nonlinear class 3 triad resonance theory for the energy focusing of surface waves around the resonant wavenumber $k_{\mathrm{res}}$ in Fourier space.

physics.flu-dyn↗

arXiv2019

Digital System Reconstruction by Pairwise Transfer Entropy

Transfer entropy (TE) is an attractive model-free method to detect causality and infer structural connectivity of general digital systems. However it relies on high dimensions used in its definition to clearly remove the memory effect and distinguish the direct causality from the indirect ones which makes it almost inoperable in practice. In this work, we try to use a low order and pairwise TE framework with binary data suitably filtered from the recorded signals to avoid the high dimensional problem. Under this setting, we find and explain that the TE values from the connected and unconnected pairs have a significant difference of magnitude, which can be easily classified by cluster methods. This phenomenon widely and robustly holds over a wide range of systems and dynamical regimes. In addition, we find the TE value is quadratically related to the coupling strength and thus we can establish a quantitative mapping between the causal and structural connectivity.

q-bio.QM↗

arXiv2018

Swift Two-sample Test on High-dimensional Neural Spiking Data

To understand how neural networks process information, it is important to investigate how neural network dynamics varies with respect to different stimuli. One challenging task is to design efficient statistical approaches to analyze multiple spike train data obtained from a short recording time. Based on the development of high-dimensional statistical methods, it is able to deal with data whose dimension is much larger than the sample size. However, these methods often require statistically independent samples to start with, while neural data are correlated over consecutive sampling time bins. We develop an approach to pretreat neural data to become independent samples over time by transferring the correlation of dynamics for each neuron in different sampling time bins into the correlation of dynamics among different dimensions within each sampling time bin. We verify the method using simulation data generated from Integrate-and-fire neuron network models and a large-scale network model of primary visual cortex within a short time, e.g., a few seconds. Our method may offer experimenters to use the advantage of the development of statistical methods to analyze high-dimensional neural data.

q-bio.NC↗

arXiv2018

Maximum Entropy Principle Analysis in Network Systems with Short-time Recordings

In many realistic systems, maximum entropy principle (MEP) analysis provides an effective characterization of the probability distribution of network states. However, to implement the MEP analysis, a sufficiently long-time data recording in general is often required, e.g., hours of spiking recordings of neurons in neuronal networks. The issue of whether the MEP analysis can be successfully applied to network systems with data from short recordings has yet to be fully addressed. In this work, we investigate relationships underlying the probability distributions, moments, and effective interactions in the MEP analysis and then show that, with short recordings of network dynamics, the MEP analysis can be applied to reconstructing probability distributions of network states under the condition of asynchronous activity of nodes in the network. Using spike trains obtained from both Hodgkin-Huxley neuronal networks and electrophysiological experiments, we verify our results and demonstrate that MEP analysis provides a tool to investigate the neuronal population coding properties, even for short recordings.

physics.bio-ph↗

arXiv2018

Dynamical and Coupling Structure of Pulse-Coupled Networks in Maximum Entropy Analysis

Maximum entropy principle (MEP) analysis with few non-zero effective interactions successfully characterizes the distribution of dynamical states of pulse-coupled networks in many experiments, e.g., in neuroscience. To better understand the underlying mechanism, we found a relation between the dynamical structure, i.e., effective interactions in MEP analysis, and the coupling structure of pulse-coupled network to understand how a sparse coupling structure could lead to a sparse coding by effective interactions. This relation quantitatively displays how the dynamical structure is closely related to the coupling structure.

physics.bio-ph↗

arXiv2017

A New Framework for Determination of Excitatory and Inhibitory Conductances Using Somatic Clamp

The interaction between excitation and inhibition is crucial for brain computation. To understand synaptic mechanisms underlying brain function, it is important to separate excitatory and inhibitory inputs to a target neuron. In the traditional method, after applying somatic current or voltage clamp, the excitatory and inhibitory conductances are determined from the synaptic current-voltage (I-V) relation --- the slope corresponds to the total conductance and the intercept corresponds to the reversal current. Because of the space clamp effect, the measured conductance in general deviates substantially from the local conductance on the dendrite. Therefore, the interpretation of the conductance measured by the traditional method remains to be clarified. In this work, based on the investigation of an idealized ball-and-stick neuron model and a biologically realistic pyramidal neuron model, we first demonstrate both analytically and numerically that the conductance determined by the traditional method has no clear biological interpretation due to the neglect of a nonlinear interaction between the clamp current and the synaptic current across the spatial dendrites. As a consequence, the traditional method can induce an arbitrarily large error of conductance measurement, sometimes even leads to unphysically negative conductance. To circumvent the difficulty of elucidating synaptic impact on neuronal computation using the traditional method, we then propose a framework to determine the effective conductance that reflects directly the functional impact of synaptic inputs on action potential initiation and thereby neuronal information processing. Our framework has been further verified in realistic neuron simulations, thus greatly improves upon the traditional approach by providing a reliable and accurate assessment of the role of synaptic activity in neuronal computation.

q-bio.NC↗

arXiv2017

Emergence of a Balanced Core through Dynamical Competition in Heterogeneous Neuronal Networks

The balance between excitation and inhibition is crucial for neuronal computation. It is observed that the balanced state of neuronal networks exists in many experiments, yet its underlying mechanism remains to be fully clarified. Theoretical studies of the balanced state mainly focus on the analysis of the homogeneous Erd$\ddot{\text{o}}$s-R\'enyi network. However, neuronal networks have been found to be inhomogeneous in many cortical areas. In particular, the connectivity of neuronal networks can be of the type of scale-free, small-world, or even with specific motifs. In this work, we examine the questions of whether the balanced state is universal with respect to network topology and what characteristics the balanced state possesses in inhomogeneous networks such as scale-free and small-world networks. We discover that, for a sparsely but strongly connected inhomogeneous network, despite that the whole network receives external inputs, there is a small active subnetwork (active core) inherently embedded within it. The neurons in this active core have relatively high firing rates while the neurons in the rest of the network are quiescent. Surprisingly, the active core possesses a balanced state and this state is independent of the model of single-neuron dynamics. The dynamics of the active core can be well predicted using the Fokker-Planck equation with the mean-field assumption. Our results suggest that, in the presence of inhomogeneous network connectivity, the balanced state may be ubiquitous in the brain, and the network connectivity in the active core is essentially close to the Erd$\ddot{\text{o}}$s-R\'enyi structure. The existence of the small active core embedded in a large network may provide a potential dynamical scenario underlying sparse coding in neuronal networks.

q-bio.NC↗

arXiv2017

Determination of Effective Synaptic Conductances Using Somatic Voltage Clamp

The interplay between excitatory and inhibitory neurons imparts rich functions of the brain. To understand the underlying synaptic mechanisms, a fundamental approach is to study the dynamics of excitatory and inhibitory conductances of each neuron. The traditional method of determining conductance employs the synaptic current-voltage (I-V) relation obtained via voltage clamp. Using theoretical analysis, electrophysiological experiments, and realistic simulations, here we demonstrate that the traditional method conceptually fails to measure the conductance due to the neglect of a nonlinear interaction between the clamp current and the synaptic current. Consequently, it incurs substantial measurement error, even giving rise to unphysically negative conductance as observed in experiments. To elucidate synaptic impact on neuronal information processing, we introduce the concept of effective conductance and propose a framework to determine it accurately. Our work suggests re-examination of previous studies involving conductance measurement and provides a reliable approach to assess synaptic influence on neuronal computation.

q-bio.NC↗

arXiv2007

Dynamics of bacterial flow: Emergence of spatiotemporal coherent structures

We propose a simple model of self-propelled particles to show that coherent structures, such as jets and swirls, can arise from a plausible microscopic mechanisms: (i) the elongated shape of the self-propelled particles with (ii) the hardcore interactions among them. We demonstrate via computer simulation that these coherent structures, which emerge at sufficiently high densities of particles, have characteristics that are similar to those observed in recent experiments in bacteria baths.

cond-mat.soft↗

arXiv2007

Interactions of renormalized waves in thermalized Fermi-Pasta-Ulam chains

The dispersive interacting waves in Fermi-Pasta-Ulam (FPU) chains of particles in \textit{thermal equilibrium} are studied from both statistical and wave resonance perspectives. It is shown that, even in a strongly nonlinear regime, the chain in thermal equilibrium can be effectively described by a system of weakly interacting \textit{renormalized} nonlinear waves that possess (i) the Rayleigh-Jeans distribution and (ii) zero correlations between waves, just as noninteracting free waves would. This renormalization is achieved through a set of canonical transformations. The renormalized linear dispersion of these renormalized waves is obtained and shown to be in excellent agreement with numerical experiments. Moreover, a dynamical interpretation of the renormalization of the dispersion relation is provided via a self-consistency, mean-field argument. It turns out that this renormalization arises mainly from the trivial resonant wave interactions, i.e., interactions with no momentum exchange. Furthermore, using a multiple time-scale, statistical averaging method, we show that the interactions of near-resonant waves give rise to the broadening of the resonance peaks in the frequency spectrum of renormalized modes. The theoretical prediction for the resonance width for the thermalized $β$-FPU chain is found to be in very good agreement with its numerically measured value.

nlin.CD↗

arXiv2005

Renormalized waves and discrete breathers in $β$-FPU chains

We demonstrate via numerical simulation that in the \textit{strongly} nonlinear limit, the $β$-FPU system in thermal equilibrium behaves surprisingly like weakly nonlinear waves in properly renormalized normal variables. This arises because the collective effect of strongly nonlinear interactions effectively renormalizes linear dispersion frequency and leads to effectively weak interaction among these renormalized waves. Furthermore, we show that the dynamical scenario for thermalized $β$-FPU chains is spatially highly localized discrete breathers riding chaotically on spatially extended, renormalized waves.

math-ph↗

arXiv1999

A phenomenological electronic stopping power model for molecular dynamics and Monte Carlo simulation of ion implantation into silicon

It is crucial to have a good phenomenological model of electronic stopping power for modeling the physics of ion implantation into crystalline silicon. In the spirit of the Brandt-Kitagawa effective charge theory, we develop a model for electronic stopping power for an ion, which can be factorized into (i) a globally averaged effective charge taking into account effects of close and distant collisions by target electrons with the ion, and (ii) a local charge density dependent electronic stopping power for a proton. This phenomenological model is implemented into both molecular dynamics and Monte Carlo simulations. There is only one free parameter in the model, namely, the one electron radius rs0 for unbound electrons. By fine tuning this parameter, it is shown that the model can work successfully for both boron and arsenic implants. We report that the results of the dopant profile simulation for both species are in excellent agreement with the experimental profiles measured by secondary-ion mass spectrometry(SIMS) over a wide range of energies and with different incident directions. We point out that the model has wide applicability, for it captures the correct physics of electronic stopping in ion implantation. This model also provides a good physically-based damping mechanism for molecular dynamics simulations in the electronic stopping power regime, as evidenced by the striking agreement of dopant profiles calculated in our molecular dynamics simulations with the SIMS data.

physics.comp-ph↗

arXiv1993

Localized States in Discrete Nonlinear Schrödinger Equations

A new 1-D discrete nonlinear Schrödinger (NLS) Hamiltonian is introduced which includes the integrable Ablowitz-Ladik system as a limit. The symmetry properties of the system are studied. The relationship between intrinsic localized states and the soliton of the Ablowitz-Ladik NLS is discussed. It is pointed out that a staggered localized state can be viewed as a particle of a {\em negative} effective mass. It is shown that staggered localized states can exist in the discrete dark NLS. The motion of localized states and Peierls-Nabarro pinning are studied.

patt-sol↗

arXiv1992

Length-scale competition in the damped sine-Gordon chain

It is shown that there are two different regimes for the damped sine-Gordon chain driven by the spatio-temporal periodic force $Γsin(ωt - k_{n} x)$ with a flat initial condition. For $Γ_{c}(n)$ to a translating {\em 2-breather} excitation from a state locked to the driver. For $ω< k_{n}$, the excitations of the system are the locked states with the phase velocity $ω/k_{n}$ in all the region of $Γ$ studied. In the first regime, the frequency of the breathers is controlled by $ω$, and the velocity of the breathers, controlled by $k_{n}$, is shown to be the group velocity determined from the linear dispersion relation for the sine-Gordon equation. A linear stability analysis reveals that, in addition to two competing length-scales, namely, the width of the breathers and the spatial period of the driving, there is one more length-scale which plays an important role in controlling the dynamics of the system at small driving. In the second regime the length-scale $k_{n}$ controls the excitation. The above picture is further corroborated by numerical nonlinear spectral analysis. An energy balance estimate is also presented and shown to predict the critical value of $Γ$ in good agreement with the numerics.

cond-mat↗