Patricia Rubisch

PostDoctoral Researcher

Medical School Berlin

Biography

I am a PostDoctoral researcher in the group of Melanie Stefan at Medical School Berlin. My research is focused on models of synaptic plasticity in multi-scale models to investigate the functional role of synaptic heterogeneity. Previously I have worked on voltage-dependent plasticity with Matthias Hennig at the Univeristy of Edinburgh which sparked my interest for the role of inhibitory regulation of long term plasticity.

Interests

Synaptic Plasticity
Models of Learning in the healthy and diseased brain
Synaptopathologies
Multi-scale modelling
Spiking Neural Networks
Biological credit assingment

Education

PhD in Computational Neuroscience, 2024
University of Edinburgh
MScR in Computational Neuroscience, 2019
Univeristy of Edinburgh
BSc in Cognitive Science, 2018
Eberhard Karls Universität Tübingen

Experience

PostDoctoral Researcher

Medical School Berlin

December 2023 – Present Berlin, Germany

Investigating the influence of synaptic heterogeneity on plasticity induction at the Stefan Lab.

Teaching Assistant

University of Edinburgh

September 2019 – October 2023 Edinburgh, UK

Providing teaching support for various courses. Computational Cognitive Neuroscience (Teaching Assistant), Neural Computation (Marker), Introduction to Cognitive Science (Tutor, Demonstrator, Marker)

Student Research Assistant

AG Neural Information Processing, Eberhard Karls Universität Tübingen

December 2015 – September 2018 Tübingen, Germany

Implementation and execution of psychophysics experiments.

Publications

S. Haghiri, Patricia Rubisch, R. Geirhos, F. Wichmann, U. von Luxburg

January, 0001 arXiv

Comparison-based frameword for psychophysics: Lab versus crowdsourcing

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S. Otte, Patricia Rubisch, M. V. Butz

January, 0001 In ICANN

Gradient-based learning of compositional dynamics with modular RNNs

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R. Geirhos, Patricia Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, W. Brendel

January, 0001 In ICLR

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

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R. Geirhos, Patricia Rubisch, J. Rauber, C. R. M. Temme, C. Michaelis, W. Brendel, F. A. Wichmann

January, 0001 Journal of Vision (19)

Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs

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Contact

patricia.rubisch@medicalschool-berlin.de
Leipziger Platz 10, Office 1.11, Berlin, 10117
DM Me