A Python-based Hyperparameter Optimization Toolbox for Neural Networks designed to accelerate and simplify the construction, training, and evaluation of machine learning models.

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PHOTON Modules

PHOTON Modules are add-ons to the PHOTON package which allow users to include modality-specific operations directly into their machine learning pipeline. Thus, users can harness PHOTON's full power while directly working with their favorite modality-specific software or algorithms. In this way, all preprocessing steps can also be used with hyperparameter search.


The PHOTON Wizard enables you to design complex machine learning pipelines in PHOTON without the need to code.

Ramona Leenings, Nils Winter

AI and Machine Learning in Psychiatry Group

University of Muenster

PHOTON Genetics

PHOTON-Genetics enables loading and preprocessing of genetic data based on the PLINK package.

Prof. Dr. B. Müller-Myhsok

Statistical Genetics

Max Planck Institute of Psychiatry Munich


PHOTON-Neuro enables loading and proprecessing neuroimaging data such as structural and functional Magnetic Resonance Imaging (MRI) data. In addition, it supports a range of advanced feature extraction and feature engineering as well as atlas-based analyses.

Prof. Dr. Dr. U. Dannlowski

Translational Psychiatry Group

University of Muenster


PHOTON-Now enables loading and proprecessing temporal (i.e. time-series) data with a focus on neuroimaging modalities (such as EEG, MEG and resting-state functional MRI) and smartphone sensor data. In addition, it supports a range of advanced time-series analysis tools based on Deep Learning (e.g. LSTMs) and Complex Systems Analysis (e.g. stochastic differential equation modeling).

PD Dr. T. Hahn & Dr. O. Kamps

AI and Machine Learning in Psychiatry Group, Center for Nonlinear Science

University of Muenster

PHOTON Repository

PHOTON-Repository is a platform enabling direct sharing of pre-trained machine learning models - PHOTON-based or otherwise - with the community. It thus allows researchers to disseminate their accomplishments while simultaneously providing a hub for all reseachers seeking state-of-the-art models. Thereby, PHOTON-Repository fosters and supports current efforts towards replicability and real-life validation.


AI and Machine Learning in Psychiatry Group

University of Muenster


PHOTON-eDoc is a growing set of tools and algorithms enabling the analysis of real-life clinical data including for example psychometric data and text from eletronic patient records. It is designed specifically for unstructured data which would otherwise be difficult to handle in machine learning analyses.

Dr. med. Nils Opel, PD Dr. T. Hahn

Translational Psychiatry Group & AI and Machine Learning in Psychiatry Group

University of Muenster

Sounds good?!

Dive deeper, explore some examples and read the documentation.

Model Repository

Science needs exchange and cooperation.

We re-invented the wheel so many times in our work, that we feel the need to create a place where we can all share our best ideas - not as a paper, but in a form usable right away for everyone. Let others benefit from your work - benefit from others' work.

Explore the model repository


Tim Hahn

Tim Hahn

PD Dr. rer. nat.

Daniel Emden

Daniel Emden

Dipl. Inf.

Dominik Grotegerd

Dominik Grotegerd

Dr. rer. nat. Dipl. Inf.

Claas Kähler

Claas Kaehler

Dipl. Inf.

Ramona Leenings

Ramona Leenings

B. Sc. Wirtschaftsinformatik,
M. Sc. Cognitive Science

Nils Winter

Nils Winter

M. Sc. Psychology

Special Thanks goes to the Department of Computer Science, University of Muenster

Xiaoyi Jiang

Prof. Dr. Xiaoyi Jiang

Head of Pattern Recognition and Image Analysis Department
PHOTON Advisory Board

Xiaoyi Jiang Students

PHOTON Developers

Computer Science Students

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