Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input.

ABSTRACT: Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get better at distinguishing English and [l], as in "rock" vs. "lock," relative to infants learning Japanese. Influential accounts of this early phonetic learning phenomenon initially proposed that infants group sounds into native vowel- and consonant-like phonetic categories-like and [l] in English-through a statistical clustering mechanism dubbed "distributional learning." The feasibility of this mechanism for learning phonetic categories has been challenged, however. Here, we demonstrate that a distributional learning algorithm operating on naturalistic speech can predict early phonetic learning, as observed in Japanese and American English infants, suggesting that infants might learn through distributional learning after all. We further show, however, that, contrary to the original distributional learning proposal, our model learns units too brief and too fine-grained acoustically to correspond to phonetic categories. This challenges the influential idea that what infants learn are phonetic categories. More broadly, our work introduces a mechanism-driven approach to the study of early phonetic learning, together with a quantitative modeling framework that can handle realistic input. This allows accounts of early phonetic learning to be linked to concrete, systematic predictions regarding infants' attunement.

SUBMITTER: Schatz T

PROVIDER: S-EPMC7924220 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Optimization of Nanoparticle-Based SERS Substrates through Large-Scale Realistic Simulations.

Project description:Surface-enhanced Raman scattering (SERS) has become a widely used spectroscopic technique for chemical identification, providing unbeaten sensitivity down to the single-molecule level. The amplification of the optical near field produced by collective electron excitations -plasmons- in nanostructured metal surfaces gives rise to a dramatic increase by many orders of magnitude in the Raman scattering intensities from neighboring molecules. This effect strongly depends on the detailed geometry and composition of the plasmon-supporting metallic structures. However, the search for optimized SERS substrates has largely relied on empirical data, due in part to the complexity of the structures, whose simulation becomes prohibitively demanding. In this work, we use state-of-the-art electromagnetic computation techniques to produce predictive simulations for a wide range of nanoparticle-based SERS substrates, including realistic configurations consisting of random arrangements of hundreds of nanoparticles with various morphologies. This allows us to derive rules of thumb for the influence of particle anisotropy and substrate coverage on the obtained SERS enhancement and optimum spectral ranges of operation. Our results provide a solid background to understand and design optimized SERS substrates.

| S-EPMC5319398 | biostudies-literature

Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.

Project description:Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.

| S-EPMC9640191 | biostudies-literature

Chromatic Perceptual Learning but No Category Effects without Linguistic Input.

Project description:Perceptual learning involves an improvement in perceptual judgment with practice, which is often specific to stimulus or task factors. Perceptual learning has been shown on a range of visual tasks but very little research has explored chromatic perceptual learning. Here, we use two low level perceptual threshold tasks and a supra-threshold target detection task to assess chromatic perceptual learning and category effects. Experiment 1 investigates whether chromatic thresholds reduce as a result of training and at what level of analysis learning effects occur. Experiment 2 explores the effect of category training on chromatic thresholds, whether training of this nature is category specific and whether it can induce categorical responding. Experiment 3 investigates the effect of category training on a higher level, lateralized target detection task, previously found to be sensitive to category effects. The findings indicate that performance on a perceptual threshold task improves following training but improvements do not transfer across retinal location or hue. Therefore, chromatic perceptual learning is category specific and can occur at relatively early stages of visual analysis. Additionally, category training does not induce category effects on a low level perceptual threshold task, as indicated by comparable discrimination thresholds at the newly learned hue boundary and adjacent test points. However, category training does induce emerging category effects on a supra-threshold target detection task. Whilst chromatic perceptual learning is possible, learnt category effects appear to be a product of left hemisphere processing, and may require the input of higher level linguistic coding processes in order to manifest.

| S-EPMC4879779 | biostudies-literature

Large-scale simulation of biomembranes incorporating realistic kinetics into coarse-grained models.

Project description:Biomembranes are two-dimensional assemblies of phospholipids that are only a few nanometres thick, but form micrometre-sized structures vital to cellular function. Explicit molecular modelling of biologically relevant membrane systems is computationally expensive due to the large number of solvent particles and slow membrane kinetics. Coarse-grained solvent-free membrane models offer efficient sampling but sacrifice realistic kinetics, thereby limiting the ability to predict pathways and mechanisms of membrane processes. Here, we present a framework for integrating coarse-grained membrane models with continuum-based hydrodynamics. This framework facilitates efficient simulation of large biomembrane systems with large timesteps, while achieving realistic equilibrium and non-equilibrium kinetics. It helps to bridge between the nanometer/nanosecond spatiotemporal resolutions of coarse-grained models and biologically relevant time- and lengthscales. As a demonstration, we investigate fluctuations of red blood cells, with varying cytoplasmic viscosities, in 150-milliseconds-long trajectories, and compare kinetic properties against single-cell experimental observations.

| S-EPMC7289815 | biostudies-literature

Large-Scale Molecular Dynamics Simulations Reveal New Insights Into the Phase Transition Mechanisms in MIL-53(Al).

Project description:Soft porous crystals have the ability to undergo large structural transformations upon exposure to external stimuli while maintaining their long-range structural order, and the size of the crystal plays an important role in this flexible behavior. Computational modeling has the potential to unravel mechanistic details of these phase transitions, provided that the models are representative for experimental crystal sizes and allow for spatially disordered phenomena to occur. Here, we take a major step forward and enable simulations of metal-organic frameworks containing more than a million atoms. This is achieved by exploiting the massive parallelism of state-of-the-art GPUs using the OpenMM software package, for which we developed a new pressure control algorithm that allows for fully anisotropic unit cell fluctuations. As a proof of concept, we study the transition mechanism in MIL-53(Al) under various external pressures. In the lower pressure regime, a layer-by-layer mechanism is observed, while at higher pressures, the transition is initiated at discrete nucleation points and temporarily induces various domains in both the open and closed pore phases. The presented workflow opens the possibility to deduce transition mechanism diagrams for soft porous crystals in terms of the crystal size and the strength of the external stimulus.

| S-EPMC8429608 | biostudies-literature

Anomalous phase separation in a correlated electron system: Machine-learning-enabled large-scale kinetic Monte Carlo simulations.

Project description:SignificancePhase separation is crucial to the functionalities of many correlated electron materials with notable examples including colossal magnetoresistance in manganites and high-Tc superconductivity in cuprates. However, the nonequilibrium phase-separation dynamics in such systems are poorly understood theoretically, partly because the required multiscale modeling is computationally very demanding. With the aid of machine-learning methods, we have achieved large-scale dynamical simulations in a representative correlated electron system. We observe an unusual relaxation process that is beyond the framework of classical phase-ordering theories. We also uncover a correlation-induced freezing behavior, which could be a generic feature of phase separation in correlated electron systems.

| S-EPMC9170136 | biostudies-literature

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy.

Project description:Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the ab initio framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with ab initio accuracy to address this issue. By training on structural data and corresponding ab initio eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with 106 atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations.

| S-EPMC11310461 | biostudies-literature

Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model.

Project description:Neurogenesis persists throughout life in the dentate gyrus region of the mammalian hippocampus. Computational models have established that the addition of neurons degrades existing memories (i.e., produces forgetting). These predictions are supported by empirical observations in rodents, where post-training increases in neurogenesis also promote forgetting of hippocampus-dependent memories. However, in these computational models which use 10-1,000 neurons to represent the dentate gyrus, forgetting is only observed at rates of new neuron addition that greatly exceed adult neurogenesis rates observed in vivo. In order to address this, here we generated an artificial neural network which incorporated more realistic features of the hippocampus - including increased network size (with up to 20,000 dentate gyrus neurons), sparse activity, and sparse connectivity - features that were not present in earlier models. In addition, we explored how properties of new neurons - their connectivity, excitability, and plasticity - impact forgetting using a pattern categorization task. Our results revealed that neurogenic networks forget previously learned input-output pattern associations. This forgetting predicted a performance enhancement in subsequent conflictual learning, compared to static networks (with no added neurons). These effects were especially sensitive to changes in increased output connectivity and excitability of new neurons. Crucially, forgetting was observed at much lower rates of neurogenesis in larger networks, with the addition of as little as 0.2% of the total DG population sufficient to induce forgetting.

| S-EPMC8719326 | biostudies-literature

Large-scale NMR simulations in liquid state: A tutorial.

Project description:Liquid state nuclear magnetic resonance is the only class of magnetic resonance experiments for which the simulation problem is solved comprehensively for spin systems of any size. This paper contains a practical walkthrough for one of the many available simulation packages - Spinach. Its unique feature is polynomial complexity scaling: the ability to simulate large spin systems quantum mechanically and with accurate account of relaxation, diffusion, chemical processes, and hydrodynamics. This paper is a gentle introduction written with a PhD student in mind.

| S-EPMC6001792 | biostudies-literature

Biomolecular Simulations under Realistic Macroscopic Salt Conditions.

Project description:Biomolecular simulations are typically performed in an aqueous environment where the number of ions remains fixed for the duration of the simulation, generally with either a minimally neutralizing ion environment or a number of salt pairs intended to match the macroscopic salt concentration. In contrast, real biomolecules experience local ion environments where the salt concentration is dynamic and may differ from bulk. The degree of salt concentration variability and average deviation from the macroscopic concentration remains, as yet, unknown. Here, we describe the theory and implementation of a Monte Carlo osmostat that can be added to explicit solvent molecular dynamics or Monte Carlo simulations to sample from a semigrand canonical ensemble in which the number of salt pairs fluctuates dynamically during the simulation. The osmostat reproduces the correct equilibrium statistics for a simulation volume that can exchange ions with a large reservoir at a defined macroscopic salt concentration. To achieve useful Monte Carlo acceptance rates, the method makes use of nonequilibrium candidate Monte Carlo (NCMC) moves in which monovalent ions and water molecules are alchemically transmuted using short nonequilibrium trajectories, with a modified Metropolis-Hastings criterion ensuring correct equilibrium statistics for an ( ??, N, p, T) ensemble to achieve a ?1046× boost in acceptance rates. We demonstrate how typical protein (DHFR and the tyrosine kinase Src) and nucleic acid (Drew-Dickerson B-DNA dodecamer) systems exhibit salt concentration distributions that significantly differ from fixed-salt bulk simulations and display fluctuations that are on the same order of magnitude as the average.

| S-EPMC6078207 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data