# resources

## 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, Basnak, Wilson & Drugowitsch (2023). Bayesian inference in ring attractor networks. Code

Bill, Gershman & Drugowitsch (2022). Visual motion perception as online hierarchical inference. Code

Drevet, Drugowitsch & Wyart (2022). Efficient stabilization of imprecise statistical inference through conditional belief updating. Code Data

Krause & Drugowitsch (2022). A large majority of awake hippocampal sharp-wave ripples feature spatial trajectories with momentum. Code

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

Yang, Bill, Drugowitsch & Gershman (2021). Human visual motion perception shows hallmarks of Bayesian structural inference. Code & Data

Kafashan et al. (2021). Scaling of sensory information in large neural populations shows signatures of information-limiting correlations. Code Data

Bill, Pailian, Drugowitsch & Gershman (2020). Hierarchical structure is employed by humans during visual motion perception. Code MOT Data Prediction 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 Code

Drugowitsch (2008). Design and analysis of learning classifier systems: a probabilistic approach. Code

## Recorded talks/seminars

Sep 2022: The secret Bayesian life of ring attractor networks. Seminar by Anna Kutschireiter at World Wide Neuroscience WWNeuRise.

Mar 2022: The secret Bayesian lives of ring attractor networks. Contributed talk by Anna Kutschireiter at Cosyne 2022 conference.

Sep 2021: A Bayesian perspective on the fruit fly’s internal compass. Contributed talk by Anna Kutschireiter at Bernstein Conference 2021.

Nov 2021: Structure in motion: visual motion perception as online hierarchical inference. Flashlight talk by Johannes Bill at Neuromatch 4.0 conference.

Dec 2020: Adaptation properties allow identification of optimized neural codes. Poster presentation by Luke Rast at NeurIPS 2020.

Mar 2020: The limits of diffusion models for decision-making. Talk by Jan Drugowitsch at Cosyne 2020 workshop on What’s a behavior: Systems neuroscience meets behavioral ecology.

Dec 2014: Optimal decision-making with time-varying evidence reliability. Spotlight talk by Jan Drugowitsch at NeurIPS 2014.

## Computational neuroscience journal club

Please consult the Computational Neuroscience Journal Club webpage for a list of future meetings.

## Lab interal

Lab members should have access to the shared Google Drive folder and a shared Dropbox folder. If you don’t, please get in touch with Jan to get access.