Danika Lea Paulo

A population code for semantics in human hippocampus

The paper investigates how semantic information is encoded in human hippocampal activity during speech processing, finding that distributed neural patterns capture semantic relationships similar to language models.

Electrophysiological features of signals recorded from white matter

The paper analyzes electrophysiological signals from white matter and finds they have distinct spectral features and higher complexity compared to gray matter.

Neural signatures of impaired semantic contextualization in Autism Spectrum Disorder

This study uses large language models to analyze neural responses in autistic individuals, finding reduced contextualization signatures compared to controls.

Distinct tasks engage a shared neural subspace in human hippocampus and anterior cingulate cortex

The paper investigates how neural circuits in the human hippocampus and anterior cingulate cortex support multiple tasks, finding shared neural subspaces across distinct behaviors.

A geometric foundation for word meaning in the brain

The paper shows that semantic features in word meaning are encoded in the brain through geometric structures similar to those in language models.

A semantotopic map in human hippocampus

The paper maps semantic organization in the human hippocampus, finding semantotopic patterns and multiscale semantic similarity along the mediolateral axis.

Hippocampus serves as a repository for spoken and heard word meanings during conversations

The paper shows hippocampal neurons encode meanings of spoken and heard words using shared geometric embeddings, enabling abstract cross-personal representations while preserving speaker identity binding.

Human neuronal firing is modulated by the frequency of local field potential oscillations

The paper investigates frequency tuning in human neurons using intracranial recordings and finds 27% of neurons show firing preferences within specific oscillatory frequencies, particularly low-theta.

Generalized brain-state modeling with KenazLBM

KenazLBM is a model that characterizes and predicts brain states from raw iEEG data, generalizing across subjects with heterogeneous recording channels.

Linguistic contextualization in the human hippocampus

The paper shows that hippocampal neurons encode word position and context in a way similar to self-attention in large language models.

Complementary roles for hippocampus, anterior cingulate cortex, and orbitofrontal cortex in composing continuous choice

The paper examines brain activity during a continuous prey-pursuit task, identifying distinct roles for hippocampus, ACC, and OFC in decision-making processes.

Brain-state modeling using electroencephalography: Application to adaptive closed-loop neuromodulation for epilepsy

This paper introduces BrainState, a self-supervised method to model complex brain states from EEG data, applied to epilepsy patients to capture pre-seizure states and neuromodulation effects.

Depression Attenuates Caudate and Dorsolateral Prefrontal Cortex Alpha and Beta Power Response to Reward

This study investigates how depression affects neural oscillations in the caudate and DLPFC during reward feedback, finding reduced alpha and beta power responses in depressed individuals.

Deep learning segmentation of the nucleus basalis of Meynert on 3T MRI

This paper develops a deep learning model to accurately segment the nucleus basalis of Meynert on 3T MRI, showing improved performance over standard atlases in both healthy subjects and TLE patients.

The Interictal Suppression Hypothesis in Focal Epilepsy: Electrographic and Structural Evaluation

The paper tests the interictal suppression hypothesis in focal epilepsy using electrographic and structural data to identify seizure onset zones and develop a classification model with high accuracy.

Multi-channel deep learning with intracranial neurostimulation can localize seizure onset zones in humans

This paper develops a deep learning model to classify seizure onset zones using cortico-cortical evoked potentials from intracranial neurostimulation recordings.