Code

MCMC for Tuning Curve Analysis

This code package performs fully Bayesian estimation of parametric tuning curves with various noise models and contains examples for model comparison and hypothesis testing. The actual sampling is done using the Blaise toolkit for probabilistic inference.

Code: bayesphys_v1.zip

Reference: PDF Hierarchical Bayesian modeling and Markov chain Monte Carlo sampling for tuning curve analysis (2010)

Cronin B*, Stevenson IH*, Sur M, and Körding KP.
Journal of Neurophysiology 103: 591-602. (Abstract)

Functional Connectivity (Hierarchical GLM with sparseness and smoothness priors)

A toolbox for fitting the functional connectivity model described in Stevenson et al. (2008). It uses coordinate ascent and the RPROP (resilient back-propagation) algorithm to fit functional connections and connection weight parameters from a set of spike trains. This toolbox also contains wrappers for clustering the connectivity matrix. Code and references for the IRM (Infinite Relational Model) algorithm can be found at Charles Kemp's website.

Code: coming soon

Reference: PDF Bayesian inference of functional connectivity and network structure from spikes (2008)

Stevenson IH, Rebesco JM, Hatsopoulos NG, Haga Z, Miller LE, Körding KP.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Special Issue on Brain Connectivity.

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