Mahdiyar Shahbazi

A Context-Free Model of Savings in Motor Learning

The paper shows that recurrent neural networks (RNNs) exhibit motor learning savings without explicit contextual cues, suggesting a neural basis for memory retention through high-dimensional activity shifts.

Testing hypotheses about correlations between brain activation patterns

This paper develops a method to test hypotheses about the correlation between brain activation patterns in fMRI studies, showing that a subject-wise bootstrap on the maximum-likelihood group estimate performs best.

Repetition effects reveal the sub-sequence representation of actions

The paper investigates how repetition of movement sequences affects performance, finding that memory traces are activated when the last 2-3 movements match the current execution.

A Python Toolbox for Representational Similarity Analysis

The paper introduces an open-source Python toolbox for representational similarity analysis, providing tools to evaluate neural representations and models.

Cortical areas for planning sequences before and during movement

The paper investigates how cortical areas contribute to pre-planning and online planning of movement sequences using fMRI in humans.

Future movement plans interact in sequential arm movements

The paper investigates how the brain plans multiple future arm movements during sequential actions, finding that planning processes interact and influence each other.

Neural correlates of online action preparation

The paper investigates neural mechanisms of online action preparation during sequential movements, finding increased activation in parietal and visual areas for overlapping actions, while premotor cortex remains unaffected.

Using distance on the Riemannian manifold to compare representations in brain and models

This paper introduces a Riemannian distance metric for comparing representational similarity matrices in brain and model data, showing improved reliability and sensitivity.