HGF Toolbox v4.10

Version 4.10 of the HGF Toolbox has been released.

The HGF Toolbox implements many variants of the hierarchical Gaussian filter (HGF) and many other models used in time-series modeling, such as hidden Markov models, hierarchical hidden Markov Models, Rescorla-Wagner, etc.

The main highlights of this release are

– addition of the hgf_categorical model for categorical outcomes,

– addition of the hgf_binary_pu model for binary outcomes with perceptual uncertainty (pu), and

– addition of the Pearce-Hall reinforcement learning model (ph_binary)

Many additional improvements have been made, and the full release notes are below.

The HGF is a generic Bayesian hierarchical . . . → Read More: HGF Toolbox v4.10

HGF Toolbox v4.0

Version 4.0 of the HGF Toolbox has been released.

The HGF Toolbox implements many variants of the hierarchical Gaussian filter (HGF) and many other models used in time-series modeling, such as hidden Markov models, hierarchical hidden Markov Models, Rescorla-Wagner, etc.

The main highlights of this release are

– the new PDF documentation,

– the new interactive demo, and

– the greater ease of configuration.

Configuration is now easier because of the replacement of the parameter theta in the HGF models by its log-transformed equivalent, omega_n, where n is the number of levels in the model. This parameter is estimated . . . → Read More: HGF Toolbox v4.0

PhysIO Toolbox

The PhysIO Toolbox can be downloaded as part of the TAPAS software collection of the TNU.

Example_FContrast_RETROICOR_SingleSubj The general purpose of this toolbox is the model-based physiological noise correction of fMRI data using peripheral measures of respiration and cardiac pulsation. A comprehensive description of the toolbox can be found in the remainder of this article and in the accompanying quick start manual and handbook.

Current Version: r671 (February 2nd, 2015)
(Code | Examples | GettingStarted | Handbook) New features include:

  • Compatibility tested for SPM12, small bugfixes Batch Dependencies
  • Cleaner Batch Interface with grouped sub-menus (cfg_choice)
  • New model: ‘none’ to just read out physiological raw data and preprocess, without noise modelling
  • Philips: Scan-timing via gradient log now automatized (gradient_log_auto)
  • Siemens: Tics-Logfile read-in (proprietary, needs Siemens-agreement)
  • All peak detections (cardiac/respiratory) now via auto_matched algorithm
  • Adapt plots/saving for Matlab R2014b

. . . → Read More: PhysIO Toolbox

HGF Toolbox v3.0

Version 3.0 of the HGF Toolbox has been released.

The HGF Toolbox implements many variants of the hierarchical Gaussian filter (HGF). The HGF is a generic Bayesian hierarchical model for inference on a changing environment based on sequential input. This makes it a general model of learning in discrete time. The HGF was introduced in

Mathys C, Daunizeau J, Friston KJ, Stephan KE (2011). A Bayesian foundation for individual learning under uncertainty. Front. Hum. Neurosci. 5:39. doi:10.3389/fnhum.2011.00039

In addition to learning models based on the HGF, this toolbox contains implementations of many other learning and response models, such . . . → Read More: HGF Toolbox v3.0

HGF Toolbox v2.1

Version 2.1 of the HGF Toolbox has been released.

The HGF Toolbox implements the hierarchical Gaussian filter (HGF) introduced in

Mathys C, Daunizeau J, Friston KJ, Stephan KE (2011). A Bayesian foundation for individual learning under uncertainty. Front. Hum. Neurosci. 5:39. doi: 10.3389/fnhum.2011.00039

After downloading, unzip and read the introduction in the README file.

This release introduces Bayesian parameter averaging and contains bugfixes.

The most important bug concerns the calculation of the negative free energy F, which dropped constant terms in the log-joint for estimation purposes without adding them back in. This does not affect parameter and . . . → Read More: HGF Toolbox v2.1

Variational Bayesian linear regression

The conceptual and practical limitations of classical multiple linear regression models can be resolved naturally in a Bayesian framework. Unless based on an overly simplistic parameterization, however, exact inference in Bayesian regression models is analytically intractable. This problem can be overcome using methods for approximate inference.

This MATLAB toolbox implements variational inference for a fully Bayesian multiple linear regression model, including Bayesian model selection and prediction of unseen data points on the basis of the posterior predictive density.

. . . → Read More: Variational Bayesian linear regression

Mixed-effects inference for classification group studies

Multivariate classification algorithms are powerful tools for predicting cognitive or pathophysiological states from neuroimaging data. However, meaningful inference requires models that explicitly account for fixed-effects (within-subjects) and random-effects (across-subjects) variance components. While models of this sort are standard practice in mass-univariate analyses of fMRI data, they have not yet received much attention in the decoding literature.

This software closes this gap by providing full Bayesian mixed-effects inference for multivariate classification studies.

. . . → Read More: Mixed-effects inference for classification group studies