Erfan Nozari

Neural dynamics become significantly faster and more nonlinear at the peak of information processing

The paper shows that neural populations adjust their optimal temporal resolution during stimulus processing, becoming faster and more nonlinear when decoding information is most accurate.

Lateral Propagation from Sensory Cortex Provides Differential Gain for Sensory Selection

The paper investigates lateral propagation from sensory cortex as a mechanism for differential gain in sensory selection during a whisker-based task in mice.

On the Optimal Temporal Resolution for Information Representation in Neural Activity: A Theoretical Analysis

The paper develops a theoretical framework to determine optimal temporal scales for neural information representation based on signal and noise autocorrelation dynamics.

Bracket Coding: An Emergent Balance Between TemporalIntegration and Segregation in Early Visual Population Activity

The paper identifies a novel coding scheme called bracket coding in mouse visual cortex, showing its presence across brain regions and tasks, and proposes a computational model for its generation.

Multiscale predictive modeling robustly improves the accuracy of pseudo-prospective seizure forecasting in drug-resistant epilepsy

This paper improves seizure forecasting in drug-resistant epilepsy by using multiscale predictive modeling of iEEG features and seizure risk, achieving a 51% increase in pseudo-prospective accuracy.

Passive neuromodulation: an energy-driven mechanism for closed-loop suppression of epileptic seizure

This paper introduces passive neuromodulation as a method to suppress epileptic seizures by draining energy from neural circuits, showing its efficacy in computational models.

Whole-Brain Causal Discovery Using fMRI

This paper introduces CaLLTiF, a causal discovery method for fMRI that improves accuracy and scalability, showing consistent causal patterns in human resting-state data.

Whole-brain causal connectivity during decoded neurofeedback: a meta study

This study investigates whole-brain causal connectivity during decoded neurofeedback and identifies network dynamics associated with performance differences.

Predictive Modeling of Evoked Intracranial EEG Response to Medial Temporal Lobe Stimulation in Patients with Epilepsy

The paper develops predictive models of intracranial EEG responses to medial temporal lobe stimulation in epilepsy patients, finding distance-dependent effects and generalizable models with causal historical dependence.

Optimal Decoding of Neural Dynamics Occurs at Mesoscale Spatial and Temporal Resolutions

The paper develops a model to decode neural activity and finds optimal spatial and temporal resolutions for decoding image information from MUA data.

Is the brain macroscopically linear? A system identification of resting state dynamics

The paper challenges the assumption that nonlinear models are necessary for whole-brain dynamics by showing linear models achieve better fit, simplicity, and interpretability in resting state fMRI and iEEG data.

Interrelated effects of age and parenthood on whole-brain controllability: protective effects of parenthood in mothers

This study examines how age and parenthood affect whole-brain controllability, finding that parenthood preserves controllability in mothers but not in men.

Network controllability measures of subnetworks: implications for neurosciences

The paper investigates how controllability metrics in subnetworks deviate from full networks and provides theoretical insights to address this bias.