Matthew G. Perich

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All published works

Action	Title	Year	Authors
+ PDF Chat	Expressivity of Neural Networks with Random Weights and Learned Biases.	2024	Ezekiel Williams Avery Hee-Woon Ryoo Thomas Jiralerspong Alexandre Payeur Matthew G. Perich Luca Mazzucato Guillaume Lajoie
+	A Unified, Scalable Framework for Neural Population Decoding.	2023	Mehdi Azabou Vinam Arora Venkataramana Ganesh Ximeng Mao Santosh Nachimuthu Michael J Mendelson Blake A. Richards Matthew G. Perich Guillaume Lajoie Eva L. Dyer
+	A Unified, Scalable Framework for Neural Population Decoding	2023	Mehdi Azabou Vinam Arora Venkataramana Ganesh Ximeng Mao Santosh Nachimuthu Michael Mendelson Blake A. Richards Matthew G. Perich Guillaume Lajoie Eva L. Dyer
+	Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation	2022	Justin Jude Matthew G. Perich Lee E. Miller Matthias H. Hennig
+	Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles	2022	Justin Jude Matthew G. Perich Lee E. Miller Matthias H. Hennig
+	Targeted Neural Dynamical Modeling	2021	Cole Hurwitz Akash Srivastava Kai Xu Justin Jude Matthew G. Perich Lee E. Miller Matthias H. Hennig
+	Targeted Neural Dynamical Modeling	2021	Cole Hurwitz Akash Srivastava Kai Xu Justin Jude Matthew G. Perich Lee E. Miller Matthias H. Hennig
+	Targeted Neural Dynamical Modeling	2021	Cole Hurwitz Akash Srivastava Kai Xu Justin Jude Matthew G. Perich Lee E. Miller Matthias H. Hennig
+ PDF Chat	Machine Learning for Neural Decoding	2020	Joshua I. Glaser Ari S. Benjamin Raeed H. Chowdhury Matthew G. Perich Lee E. Miller Konrad P. Körding
+	Machine learning for neural decoding	2017	Joshua I. Glaser Ari S. Benjamin Raeed H. Chowdhury Matthew G. Perich Lee E. Miller Konrad P. Körding

Common Coauthors

Coauthor	Papers Together
Lee E. Miller	7
Matthias H. Hennig	5
Justin Jude	5
Guillaume Lajoie	3
Cole Hurwitz	3
Akash Srivastava	3
Blake A. Richards	2
Joshua I. Glaser	2
Ximeng Mao	2
Eva L. Dyer	2
Konrad P. Körding	2
Raeed H. Chowdhury	2
Ari S. Benjamin	2
Santosh Nachimuthu	2
Vinam Arora	2
Kai Xu	2
Venkataramana Ganesh	2
Mehdi Azabou	2
Michael Mendelson	1
Luca Mazzucato	1
Alexandre Payeur	1
Ezekiel Williams	1
Thomas Jiralerspong	1
Michael J Mendelson	1
Avery Hee-Woon Ryoo	1
Kai Xu	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	The Elements of Statistical Learning	2001	Trevor Hastie J. Friedman Robert Tibshirani	1
+ PDF Chat	Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification	2015	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	1
+ PDF Chat	Causal interpretation rules for encoding and decoding models in neuroimaging	2015	Sebastian Weichwald Timm Meyer Ozan Özdenizci Bernhard Schölkopf Tonio Ball Moritz Grosse‐Wentrup	1
+ PDF Chat	No free lunch theorems for optimization	1997	David H. Wolpert William G. Macready	1
+	Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation	2014	Kyunghyun Cho Bart van Merriënboer Çaǧlar Gülçehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk Yoshua Bengio	1
+	Demixed principal component analysis of neural population data	2016	Dmitry Kobak Wieland Brendel Christos Constantinidis Claudia E. Feierstein Ádám Kepecs Zachary F. Mainen Xue-Lian Qi Ranulfo Romo Naoshige Uchida Christian K. Machens	1
+	LFADS - Latent Factor Analysis via Dynamical Systems	2016	David Sussillo Rafał Józefowicz L. F. Abbott Chethan Pandarinath	1
+	Making brain–machine interfaces robust to future neural variability	2016	David Sussillo Sergey D. Stavisky Jonathan C. Kao Stephen I. Ryu Krishna V. Shenoy	1
+	Isolating Sources of Disentanglement in Variational Autoencoders	2018	Ricky T. Q. Chen Xuechen Li Roger Grosse David Duvenaud	1
+ PDF Chat	The roles of supervised machine learning in systems neuroscience	2019	Joshua I. Glaser Ari S. Benjamin Roozbeh Farhoodi Konrad P. Körding	1
+ PDF Chat	Interpreting encoding and decoding models	2019	Nikolaus Kriegeskorte Pamela K. Douglas	1
+	The Roles of Supervised Machine Learning in Systems Neuroscience	2018	Joshua I. Glaser Ari S. Benjamin Roozbeh Farhoodi Konrad P. Körding	1
+	Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks	2019	Qi She Anqi Wu	1
+	Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling	2020	Akash Srivastava Yamini Bansal Yukun Ding Cole Hurwitz Kai Xu Bernhard Egger Prasanna Sattigeri Josh Tenenbaum David Cox Dan Gutfreund	1
+	Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE	2020	Ding Zhou Xue-Xin Wei	1
+ PDF Chat	Building population models for large-scale neural recordings: Opportunities and pitfalls	2021	Cole Hurwitz Nina Kudryashova Arno Onken Matthias H. Hennig	1
+	Ridge Regression: Biased Estimation for Nonorthogonal Problems	1970	Arthur E. Hoerl Robert W. Kennard	1