Marcus A. Triplett

I'm a postdoctoral research scientist at Columbia University working with Liam Paninski on computational methods for (1) control and mapping of neural circuits using multiphoton holographic optogenetics, and (2) analysis and optical imaging of neural population activity. Previously, I completed my PhD in Computational Neuroscience with Geoff Goodhill at the University of Queensland, Australia. Before that, I obtained a BSc in Computer Science and Mathematics at the University of Auckland, New Zealand, where my primary research interest was in logic.

Columbia University Affiliations:
Grossman Center for the Statistics of Mind
Center for Theoretical Neuroscience
Zuckerman Mind Brain Behavior Institute
Department of Statistics


Google Scholar

Computational Neuroscience

M. A. Triplett, M. Gajowa, H. Adesnik & L. Paninski. (2023). Bayesian target optimisation for high-precision holographic optogenetics. Advances in Neural Information Processing Systems (accepted, spotlight award). bioRxiv 2023.05.25.542307. [Link]

B. Antin*, M. Sadahiro*, M. Gajowa, M. A. Triplett, H. Adesnik & L. Paninski. (2023). Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization. bioRxiv 2023.07.13.548849. [Link]

M. A. Triplett*, M. Gajowa*, B. Antin, M. Sadahiro, H. Adesnik & L. Paninski. (2022). Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing. bioRxiv 2022.09.14.507926. [Link]

M. A. Triplett & G. J. Goodhill (2022). Inference of multiplicative factors underlying neural variability in calcium imaging data. Neural Computation 34(5), 1-27. [Link]

M. A. Triplett, Z. Pujic, B. Sun, L. Avitan & G. J. Goodhill. (2020). Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data. PLoS Computational Biology 16(11): e1008330. [Link]

M. A. Triplett & G. J. Goodhill. (2019). Probabilistic encoding models for multivariate neural data. Frontiers in Neural Circuits 13:1. [Link]

M. A. Triplett, L. Avitan & G. J. Goodhill. (2018). Emergence of spontaneous assembly activity in developing neural networks without afferent input. PLoS Computational Biology  14(9): e1006421. [Link]

F. Abbas, M. A. Triplett, G. J. Goodhill & M. P. Meyer. (2017). A three-layer network model of direction selective circuits in the optic tectum. Frontiers in Neural Circuits 11:88. [Link]

Statistical Genetics

M. R. Robinson, G. English, G. Moser, L. R. Lloyd-Jones, M. A. Triplett, Z. Zhu et al. (2017). Genotype-covariate interaction effects and the heritability of adult body mass index. Nature Genetics 49, 1174-1181 . [Link]

Mathematical and Philosophical Logic

A. Nies, M. A. Triplett & K. Yokoyama. (2021). The reverse mathematics of theorems of Jordan and Lebesgue. Journal of Symbolic Logic 86(4), 1657-1675. [Link]

P. Girard & M. A. Triplett. (2017). Prioritised ceteris paribus logic for counterfactual reasoning. Synthese 195:1681. [Link]

P. Girard & M. A. Triplett. (2015). Ceteris paribus logic in counterfactual reasoning. Proceedings Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge. Electronic Proceedings in Theoretical Computer Science 215. [Link]


Neural encoding models for multivariate optical imaging data. (2020). PhD thesis. [Link]

Computable functions of bounded variation and the complexity of Jordan decomposition. (2015). Honours dissertation. [Link]

Mortimer B. Zuckerman Mind Brain Behavior Institute
Columbia University
3227 Broadway, New York, NY 10027
United States of America