I'm a postdoc at the Technical University of Munich (Technische Universität München) with Julijana Gjorgjieva. Before that, I did my graduate work with Brent Doiron at the University of Pittsburgh and University of Chicago.

matt [dot] getz [at] tum [dot] de

My current work is focused on understanding how organisms learn, by linking neural network activity and plasticity to behavior, with the goal of better understanding how changes in neural network structure and dynamics affect an organism's actions.

Previously, I studied the relationship between cortical circuit dynamics and modulatory signals. One line explored how modulation affects information flow through neural circuits; another looked at how correlated heterogeneities in response dynamics of neurons can uncover constraints on circuit mechanisms of attentional processing. 

Broadly I'm interested in how the structure and dynamics of neural circuits relate and give rise to cognitive representations. As a theoretical + computational neuroscientist, I use techniques and approaches from applied math, physics, and, more recently, machine learning to build computational and mathematical models in service of these questions, in close collaboration with experimental scientists.

github scholar

Matthew P. Getz, Chengcheng Huang†, Brent Doiron†. Subpopulation Codes Permit Information Modulation Across Cortical States. bioRxiv, 2022 (link)

A circuit-based theory of the impact of cortical state on information flow. (October 2020) neuromatch 3.0 (online)

MP. Getz, Julijana Gjorgjieva. (2024) Dynamical representations between biologically plausible and implausible task-trained neural networks. Bernstein Conference, Frankfurt, Germany.

Pablo Crespo, Dimitra Maoutsa, MP. Getz, Julijana Gjorgjieva. (2024) Shaping Low-Rank Recurrent Neural Networks with Biological Learning Rules. Bernstein Conference, Frankfurt, Germany.

MP. Getz*, Gregory Handy*, Alex Negrón*, Brent Doiron. (2023) Tuned inhibition explains strong correlations across segregated excitatory subnetworks. Cosyne Abstracts 2023, Montreal, Canada.

MP. Getz, Brent Doiron. (2022) Using correlated heterogeneities to constrain mechanistic models of attention. Bernstein Conference, Berlin, Germany.

Alex Negrón, MP. Getz†, Gregory Handy†, Brent Doiron†. (2022) The Nature of Correlated Variability in Segregated Cortical Excitatory Subnetworks. SIAM Life Sciences, Pittsburgh, PA.

MP. Getz, Chengcheng Huang, Brent Doiron. (2020) Subpopulation coding reveals a mechanism for improved information flow through cortical circuits. Cosyne Abstracts 2020, Denver, CO, USA.

MP. Getz, Chengcheng Huang, Brent Doiron. (2019) Understanding Modulatory Effects on Cortical Circuits through Subpopulation Coding. 28th Annual Computational Neuroscience Meeting: CNS*2019. BMC Neurosci 20, 56 (2019). (link)

MP. Getz, Chengcheng Huang, Jeffrey Dunworth, Marlene R. Cohen, Brent Doiron. (2018) Attentional modulation of neural covariability in a distributed circuit-based population model. Cosyne Abstracts 2018, Denver, CO, USA.

Terina N. Martinez, Michael Sasner, Mark T. Herberth, Robert C. Switzer III, S.O. Ahmad, Kelvin C. Luk, Sylvie Ramboz, Andrea E. Kudwa, Deniz Kirik, Joe Flores, Ronald J. Mandel, MP. Getz, Ryan Brown, Joshua C. Grieger, R. Jude Samulski, David Dismuke, Sonal S. Das, Mark A. Frasier, and Kuldip D. Dave. Characterization, comparison, and cross-validation of in vivo alpha-synuclein models of parkinsonism. (2014) Society for Neuroscience, Washington, DC.