Author

Hugo L. Fernandes

Northwestern University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Assay:electronic health records· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Organism:non-human primate· 1 Organism:rat· 1

Frequent Co-Authors

Konrad Paul KordingiD· 3 shared Lee E. MilleriD· 2 shared Ari S. BenjaminiD· 1 shared Christopher VersteegiD· 1 shared Claire Chambers· 1 shared Eva L. DyeriD· 1 shared Matthew G. PerichiD· 1 shared Mohammad Gheshlaghi Azar· 1 shared

Activity by Year

2017-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersNeuroscience

Konrad Paul KordingORCID

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3 papers

Recent·Most cited

Policies or Knowledge: Priors differ between perceptual and sensorimotor tasks

Claire Chambers, Hugo L. Fernandes, Konrad Paul Kording

The paper investigates whether learned spatial priors generalize from a sensorimotor task to a perceptual task, finding that priors differ significantly between task modalities.

Journal of Neurophysiology · Apr 24, 2019Peer-reviewedNeuroscience

Modern machine learning outperforms GLMs at predicting spikes

Ari S. Benjamin, Hugo L. Fernandes, Tucker Tomlinson, Pavan Ramkumar, Christopher Versteeg, Raeed H. Chowdhury, Lee E. Miller, Konrad Paul Kording

This study compares GLMs to machine learning methods for predicting neural spikes and finds that modern methods like XGBoost and ensembles are more accurate and robust.

Frontiers in Computational Neuroscience · Jul 19, 2018Peer-reviewedNeuroscience

A cryptography-based approach for movement decoding

Eva L. Dyer, Mohammad Gheshlaghi Azar, Matthew G. Perich, Hugo L. Fernandes, Stephanie N. Naufel, Lee E. Miller, Konrad Paul Kording

This paper introduces a new decoding method called distribution alignment decoding (DAD) that uses movement statistics to map neural activity to motor variables, achieving comparable or better performance than traditional supervised decoders in some cases.

Nature Biomedical Engineering · Dec 6, 2017Peer-reviewedNeuroscience