Sasha Devore

The paper shows that divisive normalization models better capture temporal dynamics in human somatosensory cortex compared to linear models, mirroring visual cortex findings.

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

This study evaluates eOPN3, a red-shifted inhibitory opsin, in a pig model of Temporal Lobe Epilepsy to modulate seizure activity using optogenetics.

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

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.

A preregistered multimodal neuroimaging study across 256 human participants directly tests Integrated Information Theory against Global Neuronal Workspace Theory, confirming some predictions while revealing major empirical challenges for both.

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.

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.

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

In nine epilepsy patients, cortical activity carries next-word information up to 800 ms before onset, tracks GPT-2 surprise after onset, and fits GPT-2 contextual embeddings better than static ones.

The paper compares proteomic and transcriptomic profiles of hippocampus and cortex in SUDEP and high-risk SUDEP cases, finding no proteomic differences but 55 differentially expressed genes in hippocampus.