Paul H. E. Tiesinga

This study identifies EEG biomarkers of motor recovery after stroke, finding that changes in relative alpha power and network dynamics explain most of the variance in recovery.

This study shows that planning in non-human primates is a distinct cognitive ability separate from working memory, attention, and learning.

The paper introduces MissION, a protein language model-based classifier for predicting functional effects of ion channel missense variants, achieving higher accuracy than existing models.

This study investigates how different tACS waveform shapes affect brain network oscillations, finding that sinusoidal and pulsed waveforms enhance global synchrony and reduce metastability, while others have weaker effects.

This study uses dynamical models to show that aging brains show shifts in parameters indicating a transition to a supercritical regime, suggesting altered brain dynamics.

The paper shows that gaze is guided by two attentional priorities: exploratory attention for uncertainty and exploitative attention for reward history, using nonhuman primates in a learning task.

The paper models how attentional priorities are updated in multidimensional environments using parallel belief states to explain learning and decision-making.

This study shows that stimulating 2A adrenoceptors with Guanfacine improves error-based learning and prediction error signaling in the anterior cingulate cortex and striatum of nonhuman primates.

The paper introduces a cost-effective, modular implant system for chronic electrophysiology in mice, improving ease of use, recovery, and experimental versatility.

This study shows that ACC and striatum causally adjust exploration-exploitation balance during reinforcement learning, with opposing effects on learning from reward outcomes.

The paper introduces a test to assess the relevance of trial-averaged neuronal metrics to neuronal computation, finding they are less relevant in less controlled settings.

The paper introduces a geodesic-based distance method to better capture non-linear topological features in neural activity data from mouse visual cortex.

The paper identifies an interneuron subclass in primate prefrontal and anterior cingulate cortex that shows gamma synchronization linked to uncertainty and prediction errors during learning.

The paper uses a spiking neuron model to show that balanced inhibitory interneuron strengths in adult mouse V1 are necessary for flexible frequency switching between gamma and beta oscillations.

The paper investigates midfrontal theta activity using MEG and EEG data, finding that it is multidimensional rather than unitary, with multiple uncorrelated sources contributing to the signal.

The paper predicts a cell-type specific mouse mesoconnectome using gene expression data and shows that ridge regression and spatial gene modules achieve moderate accuracy.

This paper introduces a high-density CMOS scanning probe for neural recording, demonstrating its ability to capture neural activity across multiple brain regions with improved resolution compared to lower density devices.

This paper introduces an adaptive spike-artifact removal method for LFP data, revealing beta and gamma band neuronal synchronization previously obscured by spike bleed-through artifacts.

The paper shows neurons in fronto-striatal circuits encode feature-specific prediction errors during attention and learning, contributing to feature-based attention updates.

This study identifies molecular correlates of experience-dependent map plasticity in the barrel cortex by analyzing transcriptional and translational changes during altered whisker use.