Author

Laura Ahumada

University of FloridaORCID 0000-0002-9353-2457Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:electronic health records· 3 Organism:human· 3 Approach:statistical model· 3 Research Focus:differential expression· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1

Frequent Co-Authors

Andreas KeiliD· 3 shared Faith E. GilbertiD· 3 shared Caitlin M. Traiser· 2 shared Christian PanitziD· 2 shared Mingzhou DingiD· 2 shared Alejandro Santos‐MayoiD· 1 shared Hannah EngleiD· 1 shared Jourdan J. PouliotiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersNeuroscience

Andreas KeilORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Resolving Competition in Auditory Cortex: Effects of Emotional Content and Misophonia Sensitivity

Laura Ahumada, Faith E. Gilbert, Richard T. Ward, Jourdan J. Pouliot, Ryan P. Mears, Andreas Keil

The paper investigates how emotional content and misophonia sensitivity affect auditory cortex responses to concurrent sounds using ASSR and other measures.

Cerebral Cortex · Feb 1, 2026Peer-reviewedNeuroscience

Decoding in the fourth dimension: Classification of temporal patterns and their generalization across locations

Alejandro Santos‐Mayo, Faith E. Gilbert, Laura Ahumada, Caitlin M. Traiser, Hannah Engle, Christian Panitz, Mingzhou Ding, Andreas Keil

The paper introduces a decoding method using temporal features from neural time series to classify experimental conditions and assess generalization across sensor locations.

Human Brain Mapping · Jan 30, 2025Peer-reviewedNeuroscience

Quantifying Population-level Neural Tuning Functions Using Ricker Wavelets and the Bayesian Bootstrap

Laura Ahumada, Christian Panitz, Caitlin M. Traiser, Faith E. Gilbert, Mingzhou Ding, Andreas Keil

This paper introduces a method using Ricker wavelets and Bayesian bootstrap to quantify neural tuning functions in human EEG data during aversive learning, showing improved fit and interpretability compared to traditional models.

bioRxiv · May 22, 2024Neuroscience