## Software

#### 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

Documentation: slides

#### 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.