Processes and measurements: a framework for understanding neural oscillations in field potentials

Trends in Cognitive Sciences, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Evaluating Feature Importance in the Context of Simulation-Based Inference for Cortical Circuit Parameter Estimation

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 452 - 463

Опубликована: Янв. 1, 2025

Язык: Английский

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A Python toolbox for neural circuit parameter inference

npj Systems Biology and Applications, Год журнала: 2025, Номер 11(1)

Опубликована: Май 9, 2025

Язык: Английский

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Automated inference of disease mechanisms in patient-hiPSC-derived neuronal networks

Communications Biology, Год журнала: 2025, Номер 8(1)

Опубликована: Май 20, 2025

Язык: Английский

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Dynamic causal modelling in probabilistic programming languages

Journal of The Royal Society Interface, Год журнала: 2025, Номер 22(227)

Опубликована: Июнь 1, 2025

Understanding the intricate dynamics of brain activities necessitates models that incorporate causality and nonlinearity. Dynamic causal modelling (DCM) presents a statistical framework embraces relationships among regions their responses to experimental manipulations, such as stimulation. In this study, we perform Bayesian inference on neurobiologically plausible generative model simulates event-related potentials observed in magneto/encephalography data. This translates into probabilistic latent states system driven by input stimuli, described set nonlinear ordinary differential equations (ODEs) potentially correlated parameters. We provide guideline for reliable presence multimodality, which arises from parameter degeneracy, ultimately enhancing predictive accuracy neural dynamics. Solutions include optimizing hyperparameters, leveraging initialization with prior information employing weighted stacking based accuracy. Moreover, implement conduct comprehensive comparison several programming languages streamline process benchmark efficiency. Our investigation shows inversion DCM extends beyond variational approximation frameworks, demonstrating effectiveness gradient-based Markov chain Monte Carlo methods. illustrate efficiency posterior estimation using self-tuning variant Hamiltonian automatic Laplace approximation, effectively addressing degeneracy challenges. technical endeavour holds potential advance state-space ODE models, contribute neuroscience research applications neuroimaging through DCM.

Язык: Английский

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Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

iScience, Год журнала: 2023, Номер 26(11), С. 108222 - 108222

Опубликована: Окт. 17, 2023

Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables robust encoding of high volume information in circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability were done with single-compartment neuron models, we instead focus the detailed biophysical models multi-compartmental morphologies. We leverage Markov chain Monte Carlo method generate populations electrical reproducing experimental recordings while being compatible set morphologies faithfully represent specifi morpho-electrical type. demonstrate our approach layer 5 pyramidal cells study particular, find that morphological alone insufficient reproduce variability. Overall, this provides strong statistical basis create neurons controlled

Язык: Английский

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HNN-core: A Python software for cellular and circuit-level interpretation of human MEG/EEG

The Journal of Open Source Software, Год журнала: 2023, Номер 8(92), С. 5848 - 5848

Опубликована: Дек. 15, 2023

HNN-core is a library for circuit and cellular level interpretation of non-invasive human magneto-/electro-encephalography (MEG/EEG) data. It based on the Human Neocortical Neurosolver (HNN) software (Neymotin et al., 2020), modeling tool designed to simulate multiscale neural mechanisms generating current dipoles in localized patch neocortex. HNN's foundation biophysically detailed network representing canonical neocortical column containing populations pyramidal inhibitory neurons together with layer-specific exogenous synaptic drive (Figure 1 left). In addition simulating network-level interactions, HNN produces intracellular currents long apical dendrites cells across cortical layers known be responsible macroscopic dipole generation.

Язык: Английский

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AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis

PLoS Computational Biology, Год журнала: 2024, Номер 20(11), С. e1012473 - e1012473

Опубликована: Ноя. 14, 2024

Understanding how multicellular organisms reliably orchestrate cell-fate decisions is a central challenge in developmental biology, particularly early mammalian development, where tissue-level differentiation arises from seemingly cell-autonomous mechanisms. In this study, we present multi-scale, spatial-stochastic simulation framework for mouse embryogenesis, focusing on inner cell mass (ICM) into epiblast (EPI) and primitive endoderm (PRE) at the blastocyst stage. Our models key regulatory tissue-scale interactions biophysically realistic fashion, capturing inherent stochasticity of intracellular gene expression intercellular signaling, while efficiently simulating these processes by advancing event-driven techniques. Leveraging power Simulation-Based Inference (SBI) through AI-driven Sequential Neural Posterior Estimation (SNPE) algorithm, conduct large-scale Bayesian inferential analysis to identify parameter sets that faithfully reproduce experimentally observed features ICM specification. results reveal mechanistic insights combined action autocrine paracrine FGF4 signaling coordinates stochastic cellular scale achieve robust reproducible patterning tissue scale. We further demonstrate exhibits specific time window sensitivity exogenous FGF4, enabling lineage proportions be adjusted based timing dosage, thereby extending current experimental findings providing quantitative predictions both mutant wild-type systems. Notably, not only ensures correct EPI-PRE but also enhances resilience perturbations, reducing fate-proportioning errors 10-20% compared purely system. Additionally, uncover surprising role variability initial conditions, showing high gene-expression heterogeneity can improve accuracy precision proportioning, which remains when fewer than 25% population experiences perturbed conditions. work offers comprehensive, description biochemical driving identifies necessary conditions its unfolding. It provides future exploration similar systems biology.

Язык: Английский

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Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Июнь 17, 2023

Abstract Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) suggested a bottom-up feedforward (FF) type laminar profile for evoked but top-down feedback (FB) cross-sensory the cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) simple or stimuli. In estimated MEG source waveforms region of interest, showed peaks at 37 and 90 ms 125 ms. The inputs were then modeled through FF FB connections targeting different cortical layers using Human Neocortical Neurosolver (HNN), which consists neocortical circuit model linking cellular– circuit-level mechanisms MEG. HNN models measured response could explained an input followed input, input. Thus, combined results support hypothesis is type. illustrate how dynamic patterns MEG/EEG provide information about characteristics into area terms hierarchical organization among areas. Significance statement Laminar intracortical profiles characterize feedforward– feedback-type influences area. By combining biophysical computational neural modeling, obtained evidence being finding consistent with previous primates. interpreted context

Язык: Английский

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Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Сен. 8, 2024

Abstract Trajectories of motile cells represent a rich source data that provide insights into the mechanisms cell migration via mathematical modeling and statistical analysis. However, mechanistic models require type dependent parameter estimation, which in case computational simulation is technically challenging due to nonlinear inherently stochastic nature models. Here, we employ simulation-based inference (SBI) estimate specific model parameters from trajectories based on Bayesian inference. Using automated time-lapse image acquisition recognition large sets 1D single are recorded migrating microfabricated lanes. A deep neural density estimator trained simulated generated previously published mechanical migration. The network turn used infer probability distribution limited number correspond experimental trajectories. Our results demonstrate efficacy SBI discerning properties non-cancerous breast epithelial line MCF-10A cancerous MDA-MB-231. Moreover, capable unveiling impact inhibitors Latrunculin Y-27632 relevant elements without prior knowledge effect inhibitors. proposed approach analysis combined with standardized platform opens new avenues for installation motility libraries, including cytoskeleton drug efficacies,and may play role evaluation refined Subject Areas Biological Physics / Interdisciplinary

Язык: Английский

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