- Dynamic Causal Models for Human Electrophysiology🔍
- Generic dynamic causal modelling🔍
- Dynamic causal modelling revisited🔍
- Dynamic causal modelling of evoked potentials🔍
- Dynamic causal modeling🔍
- Dynamic Causal Modeling🔍
- Dynamic Causal Modeling for fMRI With Wilson|Cowan|Based ...🔍
- Neural masses and fields in dynamic causal modeling🔍
Dynamic Causal Models for Human Electrophysiology
Dynamic causal models (DCMs) for EEG are a suite of neuroimaging analysis tools designed to provide estimates of the neurobiological mechanisms that generate ...
Dynamic Causal Models for Human Electrophysiology: EEG, MEG ...
Dynamic Causal Models for Human Electrophysiology: EEG, MEG, and Local Field Potentials · R. Moran · Published 2013 · Medicine.
Generic dynamic causal modelling: An illustrative application ... - NCBI
For electrophysiological time series in particular, one could (in principle) use a wide range of neural mass (or field) models that vary in ...
Dynamic causal modelling revisited - ScienceDirect.com
This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological ...
Dynamic causal modelling of evoked potentials: A reproducibility study
Dynamic causal modelling (DCM) has been applied recently to event-related responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a ...
Dynamic causal modeling - Wikipedia
Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison.
Dynamic Causal Modeling - an overview | ScienceDirect Topics
Dynamic Causal Modeling is a method used in neuroscience to understand the mechanisms of behavioral and cognitive dysfunction, investigate synaptic ...
Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based ...
Dynamic causal modeling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain ...
Dynamic causal modeling - Scholarpedia
It is a Bayesian model comparison procedure that rests on comparing models of how time series data were generated. Dynamic causal models are ...
Neural masses and fields in dynamic causal modeling - Frontiers
Dynamic causal modeling (DCM) provides a framework for the analysis of effective connectivity among neuronal subpopulations that subtend ...
Dynamic Causal Modelling of Active Vision - Journal of Neuroscience
Using dynamic causal modeling for magnetoencephalography with (male and female) human participants, we assess the evidence for changes in ...
Dynamic Causal Modeling with Neural Population Models
Dynamic causal models were first invented in 2003 for the purpose of estimating human brain connectivity and task-dependent functional integration using ...
Dynamic causal modeling of layered magnetoencephalographic ...
The model captures the average dynamics of a detailed two layered circuit. It combines a temporal model of neural dynamics with a spatial model ...
Effective connectivity during animacy perception – dynamic causal ...
Biological agents are the most complex systems humans have to model and predict. In predictive coding, high-level cortical areas inform ...
Dynamic causal modelling for EEG and MEG
This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of ...
Dynamic Causal Modeling (DCM) for EEG Approach to ...
Request PDF | Dynamic Causal Modeling (DCM) for EEG Approach to Neuroergonomics | To study the underlying neural mechanisms of human cognitive and physical ...
Dynamic Causal Modeling on the Identification of Interacting ...
Abstract: Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal ...
Conductance-based dynamic causal modeling
... models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease. However, due to ...
Dynamic causal models for EEG | Request PDF - ResearchGate
In this chapter, we describe the dynamic causal modelling (DCM) of event-related responses measured with electroencephalography (EEG) or magnetoencephalography ...
A practical introduction into Dynamic Causal Modeling
Dynamic Causal Modelling (DCM) for fMRI has three key strengths. First, DCM properly distinguishes between neural and vascular contributions to ...