Ali Ghazizadeh

The paper identifies a two-rate system in vlPFC neurons that temporally multiplex slow and fast dynamics during value learning and memory, leading to spontaneous recovery of old values.

This paper investigates the mechanisms of weight alignment in BP-TAW, showing it is governed by statistical properties of neural signals and can be improved with weight normalization.

The paper investigates how moral value judgments of faces are encoded in the brain using EEG-fMRI, finding that the left caudate tail encodes these memories without involving the fusiform face area.

The paper shows that during efficient visual search for high-value objects, vlPFC neurons exhibit enhanced processing of peripheral targets, correlating with improved detection.

This study investigates the neural substrates of object value memory in humans using fMRI, showing distinct cortical and subcortical activation patterns over time.

The paper shows that efficient value-based visual search is associated with specific firing rate, spiking irregularity, and LFP patterns in SNr neurons during target detection.

The paper investigates how prefrontal cortex and basal ganglia represent value learning in macaques, finding that PFC signals value categories while SNr reflects value variability.

The paper shows that the ventrolateral prefrontal cortex (vlPFC) encodes value pop-out during visual search, with neural activity predicting search efficiency.

The paper introduces a method to estimate neural variability components and finds that spiking irregularity can increase after stimulus presentation, reducing the quenching of the fano-factor.

The paper identifies the role of gustatory cortex in evidence accumulation during food choice using EEG-fMRI data and finds EEG energy as a key BOLD regressor.

The paper argues that significant MVPA results in fMRI do not necessarily indicate coordinated multivoxel coding, as higher-order statistics between voxels are unlikely to be observed due to hemodynamic and resolution limitations.

This paper introduces an optimal binsize selection method for peri-event time histograms using the Akaike Information Criterion, showing improved detection of neuronal responses and dynamics.

The paper uses fMRI in macaques to show that novelty and value coding segregate across functionally connected brain networks, with distinct patterns in ventral temporal, parietal, and TP networks.

This study uses a drift-diffusion model to explain response variability and capacity limitations in tracking one or two objects.

This paper introduces a fully automated spike sorting algorithm combining t-SNE and DBSCAN, optimized with GA, which outperforms existing methods and human experts in accuracy and efficiency for large-scale neural recordings.

The paper demonstrates that repeated reward experience enables macaques to efficiently search for high-value objects, with search times decreasing and accuracy increasing with longer training.