Daniel J. Friedman

The paper uses electrocorticography to study neural coding of linguistic information during sentence production, finding non-overlapping networks and magnitude-independent encoding of higher-order linguistic information.

The paper uses graph-theoretical connectivity from ECoG to predict eloquent cortex during presurgical mapping, finding varying accuracy across functional domains.

This study investigates the neural dynamics of automatic speech production using ECoG recordings, revealing a distributed cortical network with complex feedforward and feedback signal representation.

This study identifies high-level visual cortex as the neural substrate for one-shot perceptual learning and develops a deep neural network model that replicates human behavior and neural coding.

This paper shows that speaker identity is encoded in spatial patterns of intracranial EEG during attention, enabling a new method for auditory attention decoding.

The paper investigates neural dynamics of lexical alternatives during speech production using electrocorticography and language models, finding three principles of how these alternatives are represented in the brain.

The paper introduces a dataset of ECoG recordings from nine participants listening to a podcast, along with linguistic features and tutorials for analysis.

The paper measures alpha oscillations in human visual cortex using intracranial electrodes and finds they are spatially specific, with receptive field models showing larger spatial tuning compared to broadband power.

The paper uses ECoG recordings to decode word processing during sentence production, showing syntactic structure influences word order encoding in frontal cortex.

The paper identifies a left-lateralized dorsolateral prefrontal network involved in semantic processing during word retrieval using ECoG data from patients.

The paper investigates neural state patterns during memory search, showing hippocampal-neocortical communication facilitates retrieval of event-related information.

This study shows that speakers' brains generate information-rich words through extra neural computation, evidenced by stronger neural activity before articulation, and that LLMs similarly require deeper computation for improbable words.

The paper shows that linguistic embedding spaces can capture neural alignment during conversations, revealing shared meaning between speakers and listeners.

The paper shows that larger language models better capture linguistic structure and predict neural activity, with performance peaking in earlier layers as model size increases.

The paper shows that aligning brains into a shared space improves LLM-based encoding model performance and denoising of individual brain responses.

The paper examines how neural adaptation in human visual cortex varies across the visual hierarchy using iEEG data and a computational model of normalization dynamics.

This paper introduces a deep learning framework for decoding speech from neural signals, achieving high correlation with original speech spectrograms and providing open-source code.

This paper introduces a transformer-based model for speech decoding from neural signals using surface and depth electrodes, achieving high performance across multiple participants and electrode types.

This study investigates auditory suppression during speech production in humans and finds a correlation between suppression and sensitivity to auditory feedback, suggesting suppression aids speech monitoring.

This study maps speech error timing and location during cortical stimulation, finding distinct latency patterns that suggest functional roles in speech planning and motor execution.

The paper uses a deep speech-to-text model to predict neural responses during spontaneous conversations, revealing a distributed cortical hierarchy for speech and language processing.

The paper models temporal dynamics of neural responses in human visual cortex using a computational model that captures contrast and adaptation effects across different visual areas.

This study finds a correspondence between the layered structure of deep language models and the temporal structure of neural activity during language processing in the human brain.

This study shows that HPA axis dysfunction in a mouse model increases SUDEP risk and worsens epilepsy outcomes, with potential implications for psychiatric comorbidities.

The paper shows that brain embeddings from the IFG share geometric structure with DLM contextual embeddings, enabling zero-shot encoding and decoding of unseen words.

This study uses a deep learning model to decode speech from neural signals, revealing that frontal and temporal cortex process both feedforward and feedback information during speech production.

The paper tracks memory formation and retrieval during story listening, showing neural correlates of predictive recall and hippocampo-cortical interactions.

The paper identifies proteomic differences in epilepsy brain tissue, highlighting altered protein networks in hippocampus, cortex, and dentate gyrus, with GNB1 as a key decreased protein.

This study investigates sequence encoding in human cortex and hippocampus using intracranial recordings, finding hierarchical processing of temporal regularities from lower-level features to higher-level units.

This study examines how interictal epileptiform discharges (IEDs) affect memory during encoding and retrieval, finding they reduce memory success and disrupt hippocampal ripples.