Towards an Ultrasound Probe with Vision: Structured Light to Determine Surface Orientation. - Samantha Horvath; John M. Galeotti; Bo Wang; Matt Perich; Jihang Wang; Mel W. Siegel; Patrick Vescovi; George D. Stetten (AE-CAI, 2011)

Machine learning for neural decoding. - Joshua I. Glaser; Raeed H. Chowdhury; Matthew G. Perich; Lee E. Miller; Konrad P. Körding (CoRR, 2017)

Linear-nonlinear-time-warp-poisson models of neural activity. - Patrick N. Lawlor; Matthew G. Perich; Lee E. Miller; Konrad P. Körding (J. Comput. Neurosci., 2018)

Targeted Neural Dynamical Modeling. - Cole L. Hurwitz; Akash Srivastava; Kai Xu 016; Justin Jude; Matthew G. Perich; Lee E. Miller; Matthias H. Hennig (NeurIPS, 2021)

Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation. - Justin Jude; Matthew G. Perich; Lee E. Miller; Matthias H. Hennig (ICML, 2022)

Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles. - Justin Jude; Matthew G. Perich; Lee E. Miller; Matthias H. Hennig (NeurReps, 2022)

A Unified, Scalable Framework for Neural Population Decoding. - Mehdi Azabou; Vinam Arora; Venkataramana Ganesh; Ximeng Mao; Santosh Nachimuthu; Michael Mendelson; Blake A. Richards; Matthew G. Perich; Guillaume Lajoie; Eva L. Dyer (NeurIPS, 2023)

Expressivity of Neural Networks with Random Weights and Learned Biases. - Ezekiel Williams; Alexandre Payeur; Avery Hee-Woon Ryoo; Thomas Jiralerspong; Matthew G. Perich; Luca Mazzucato; Guillaume Lajoie (ICLR, 2025)

Generalizable, real-time neural decoding with hybrid state-space models. - Avery Hee-Woon Ryoo; Nanda H. Krishna; Ximeng Mao; Mehdi Azabou; Eva L. Dyer; Matthew G. Perich; Guillaume Lajoie (CoRR, 2025)

POCO: Scalable Neural Forecasting through Population Conditioning. - Yu Duan; Hamza Tahir Chaudhry; Misha B. Ahrens; Christopher D. Harvey; Matthew G. Perich; Karl Deisseroth; Kanaka Rajan (CoRR, 2025)