mackelab Goto Github PK

.github

Repo for mackelab github landing page, maintained by @michaeldeistler

acm2017_abc

Approximate Bayesian Computation (Applied Cognitive Modeling course 2017)

amortized-decision-making

Repository for the paper "Amortized Bayesian Decision Making for simulation-based models" - Mila @milagorecki and Michael @michaeldeistler

apt_icml

Anonymized code for ICML 2019 submission

autohmm

Hidden Markov Models (HMMs) with tied states and autoregressive observations

benchmarking_sbi_runs

Raw results for Benchmarking SBI manuscript hosted on LFS

brian2

Brian is a free, open source simulator for spiking neural networks.

corbinian

CorBinian: A toolbox for modelling and simulating high-dimensional binary and count-data with correlations

critical_retina

Code and data for 'Signatures of criticality arise in simple neural populations models with correlations'

ddm_stride

Pipeline for applying simulation-based inference to Drift-Diffusion Models

decode

by @aspeiser

deepspike

delfi

Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead

dg_python

Dichotomized Gaussian model implemented in python

engine

A Generic Framework for Likelihood-Free Inference

epiphyte

Python toolkit for working with high-dimensional neural data recorded during naturalistic, continuous stimuli @a-darcher @rachrapp

epsilonfree-fork

Fork of gpapamak/epsilon_free_inference

gatsbi

GATSBI: Generative Adversarial Training for Simulation-Based Inference

gp-maps-python

Gaussian process method for inferring cortical maps (Python version), code by Dominik Straub @dominikstrb

grassmann_binary_distribution

Implementation of binary distribution in the Grassmann formalism, including conditional version. Main contributor: Cornelius Schröder (@coschroeder)

identifymechanisticmodels_2020

Code for "Training deep neural density estimators to identify mechanistic models of neural dynamics"

labproject

Labproject about comparing distributions metrics by @mackelab

le_stitch

lfi-experiments

Applications of likelihoodfree to different problems

lsmlsda2020

Repo for students of the class 'Large Scale Modelling and Large Scale Data Analysis'

machine-learning-i

Slides and lecture notes for the course 'machine learning I' taught at the Graduate School Neural Information Processing in Tuebingen.

mackelab-toolbox

This repository contains Python code shared across the lab. Original maintainer: @alcrene

mesogif

This repository archives the code used to obtain the results in René et al., 2020. Maintainer: @alcrene

mnle-for-ddms

Research code for Mixed Neural Likelihood Estimation (MNLE, Boelts et al. 2022)

msne-datascience-2018

Course material for the Fundamentals of Computer Science for Computational Neuroengineering course at TUM