Shahab Bakhtiari

The paper investigates how artificial neural networks trained on motion-related tasks develop representations that align with brain activity and support object categorization using motion cues.

The paper uses task-optimized neural networks to compare how face and object recognition models align with neural activity in visual cortex during face familiarity processing.

The paper investigates how training curriculum affects generalization in visual learning using human experiments and neural networks, finding that easy-to-hard sequences improve performance through low-dimensional neural representations.

This study investigates how semantic context and sensory reliability influence multisensory integration in decision-making, revealing two distinct pathways for combining sensory information.

This study investigates how training data properties affect the alignment of deep neural networks with mouse visual cortex activity, finding that naturalistic environments significantly improve similarity.

This study shows that perceptual learning in humans is biased by cortical asymmetries, with suboptimal learning strategies observed when training on motion stimuli with asymmetric representations.

This study uses MEG to track perisaccadic visual signals, revealing beta-band activity propagating from parietal to visual cortex, indicating remapping of visual signals during eye movements.

A 3D ResNet trained to estimate an agent's own motion from simulated visual input predicts selectivity of primate dorsal-stream neurons, whereas action-recognition networks on naturalistic video do not.

Training a two-pathway deep neural network with a single self-supervised predictive loss function models both dorsal and ventral pathways of mouse visual cortex and outperforms competing models.

This study shows that visual perceptual learning transfers fully between manual and oculomotor responses, suggesting it involves a generalizable decision rule.

The paper introduces VaLPACa, a variational ladder autoencoder that disentangles hierarchical dynamics in calcium imaging data by separating calcium flux from neural computation.

This study investigates the role of subthalamic nucleus (STN) deep brain stimulation (DBS) in anti-saccade behavior in Parkinson’s disease patients, finding STN DBS increases error rates and interacts with dopamine therapy.

This study investigates how stimulus complexity affects the specificity of visual perceptual learning and finds that more complex stimuli reduce specificity by increasing spatial integration.