Bijan Pesaran

The paper introduces MIND and UnitAssay for scalable, high-density, multi-regional electrophysiology in non-human primates, enabling stable recordings and reliable single-unit identification.

This paper presents a functional atlas of the human central nervous system using high-resolution surface electrophysiology from 70 patients, revealing conserved electrophysiological patterns and enabling neuroprosthetic applications.

The paper uses electrophysiological data from the nucleus accumbens to guide deep brain stimulation for opioid use disorder, showing suppression of drug-related cravings with targeted stimulation.

This paper presents a high-bandwidth, wireless brain-computer interface device capable of chronic recordings in animals, demonstrating reliable performance over weeks.

The paper shows that prefrontal cortex neurons during working memory tasks form structured subpopulations rather than random mixed selectivity, with distinct activity profiles for visual stimulation and memory maintenance.

This study identifies beta oscillations and traveling waves as biomarkers of recovery from traumatic brain injury using intracranial electrode data.

This paper develops a method to detect and classify events in multimodal time series data, demonstrating its effectiveness on neural spike-LFP data for eye movement detection and classification.

This paper introduces a multiscale subspace identification algorithm for modeling multimodal spiking and field potential data, showing improved efficiency and accuracy in capturing low-dimensional dynamics and predicting behavior.

The paper investigates how theta-frequency rhythms modulate multiregional brain communication and finds that theta-coherent dynamics are involved in dynamic communication across sender-receiver-modulator sites.

The paper shows that coherent-beta activity in the prefrontal cortex modulates exogenous attentional selection during conflict, supporting top-down control mechanisms.

DFINE infers nonlinear latent structures in neural population activity, achieving flexible inference and better prediction of behavior and neural activity.

This paper introduces a subdural CMOS optical probe for bidirectional neural interfacing, designed for implantation in primate brains and potential human application.

This paper develops a model and inference methods for regime-dependent multiscale neural dynamics, showing improved state estimation compared to single-scale or stationary methods.

This paper introduces RAE and MRAE models for broadband ECoG signal reconstruction, showing MRAE improves accuracy over RAE by overcoming time-bandwidth limitations.

The paper investigates inhibitory communication between brain regions during coordinated looking and reaching, finding that beta-frequency neural coherence transiently suppresses neuronal firing in one region to improve reach accuracy.

This paper introduces RNN PSID, a nonlinear dynamic modeling method for analyzing neural dynamics, and shows it outperforms linear and non-preferential methods in behavior decoding.

This paper introduces PSID, a method to dissociate behaviorally relevant neural dynamics from irrelevant ones, revealing lower-dimensional and more predictive dynamics in monkey motor tasks.

This paper introduces an algorithm to optimize electrode selection in neural recordings, improving neuron isolation quality and yield compared to existing methods.

This study decodes and modulates multiregional communication in the primate mood processing network using neuromodulation and causal network analysis.

This study investigates how optogenetic stimulation drives coherent neuronal dynamics in non-human primates by analyzing excitatory-inhibitory temporal windows.

The paper investigates how frontal action circuits adapt economic choices to context by tracking evolving preferences and integrating them with action signals.