Hugo Fluhr (hugofluhr)

hugofluhr

Geek Repo

Company:UZH

Location:Zürich, Switzerland

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Hugo Fluhr's repositories

color_palette

Code to generate color palettes from images

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connectome_spectral_analysis

Repository for paper https://authors.elsevier.com/sd/article/S1053-8119(21)00884-3

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course-content

NMA Computational Neuroscience course

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DeepLabCut

Markerless pose estimation of user-defined features with deep learning for all animals

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dotfiles

My global configuration files

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first-pull-request

git exercise for nipraxis course

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MBeautifier

MBeautifier is a MATLAB source code formatter, beautifier. It can be used directly in the MATLAB Editor and it is configurable.

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mikulan_et_al_2020

Simultaneous human intracerebral stimulation and HD-EEG: ground-truth for source localization methods - Scripts and usage demonstration

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piriform-cortex-diversity

Diversity of piriform cortex neuron

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ML_course

EPFL Machine Learning Course, Fall 2019

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neuronaldynamics-exercises

Python exercises accompanying the book "Neuronal Dynamics"

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nigsp

NiGSP: a module for graph signal processing on neuroimaging data

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NML2023

my code for the Network Machine Learning courses

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ntds_2018

Material for the EPFL master course "A Network Tour of Data Science", edition 2018.

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precourse

A repo for the pre-course work at home exercises

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pycsou

Pycsou is a Python 3 package for solving linear inverse problems with state-of-the-art proximal algorithms. The software implements in a highly modular way the main building blocks -cost functionals, penalty terms and linear operators- of generic penalised convex optimisation problems.

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pygsp

Graph Signal Processing in Python

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PySGWT

Spectral Graph Wavelet Transform

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reversal_learning_task

Repo for the Scientific Programming course task

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SocialNetworkAnalysis

Repo for code related to UMich's Applied Social Network Analysis with Python

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Source_Reconstruction_DS001

python scripts to perform EEG source reconstruction of VEP in face perception task (DS001)

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SPC_analysis

FLIM data analysis in MATLAB, written for the SPC suite

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strategiclearning_and_lddm

Code for Masís et al, 2023

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