Variational Bayesian inference for linear and logistic regression
MATLAB/Octave code to perform linear and logistic regression, with shrinkage priors. Inference of parameters and hyper-parameters is performed by Variational Bayes. Scripts with and without Automated Relevance Determination are provided.
Code: Variational Bayesian linear and logistic regression.
Documentation: arXiv paper and JOSS paper.
Diffusion model first-passage time distributions and sampling
Code to compute the first passage time distributions for diffusion models for drifts and bounds that can evolve arbitrarily over time, and for drawing first-passage time and bound samples from such models. Optimized methods are provided for special cases of constant drift/bounds. The code is available either as C++ library with MATLAB (MEX) and Python interface, or as Julia module.
Code: C++/MATLAB/Python code for diffusion models and Julia DiffModels.jl module for diffusion models.
Code for CoSMo 2017
MATLAB code for tutorials and for generating most of the figures in the slides of Jan Drugowitsch's session at the 2017 Summer School in Computational Sensory-Motor Neuroscience (CoSMo 2017).
Code: MATLAB code for CoSMo 2017
Code for the FENS Winter School 2015
MATLAB code to generate all the figures of my tutorial for normative solutions to the speed/accuracy trade-off in perceptual decision-making. This tutorial was held at the FENS-Hertie Winter School 2015 on the neuroscience of decision-making.
Code: MATLAB scripts to generate the tutorial figures.
Documentation: tutorial notes, containing all derivations/figures, and tutorial slides.
Code & data accompanying papers
Kutschireiter, Rast & Drugowitsch (2022). Angular path integration by projection filtering with increment observations. [Code]
Jang, Sharma & Drugowitsch (2021). Optimal policy for attention-modulated decisions explains human fixation behavior. [Code & Data]
Kafashan et al. (2021). Scaling of sensory information in large neural populations shows signatures of information-limiting correlations. [Code] [Data]
Mendonca et al. (2020). The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs. [Code]
Drugowitsch et al. (2019). Learing optimal decisions with confidence. [Code]
Tajima, Drugowitsch, Patel & Pouget (2019). Optimal policy for multi-alternative decisions. [Code]
Tajima, Drugowitsch & Pouget (2016). Optimal policy for value-based decision-making. [Code]
Drugowitsch (2016). Fast and accurate Monte Carlo sampling of first-passage times from Wiener diffusion models. [Code]
Drugowitsch, Moreno-Bote & Pouget (2014). Optimal decision-making with time-varying evidence reliability. [Code]
Drugowitsch, DeAngelis, Klier, Angelaki & Pouget (2014). Optimal multisensory decision-making in a ration-time task / Drugowitsch, DeAngelis, Angelaki & Pouget (2015). Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making [Data]
Computational Neuroscience Journal Club
Please consult the Computational Neuroscience Journal Club webpage for a list of future meetings.