Pouya Bashivan

This paper shows that local lateral connectivity in deep neural networks can replicate cortex-like topographical organization without explicit learning rules.

This study shows that hippocampal neurons use both linear and nonlinear computations to encode perceptual and memory information, challenging the traditional spatial vs. non-spatial dichotomy.

This paper introduces a neural network model of visual search that generates human-like scanpaths and replicates neural signatures of the fronto-parietal network during goal-directed visual exploration.

The paper shows burst firing in neural populations creates invariant representations of natural communication signals, demonstrated through recordings and computational modeling.

This study evaluates how well deep neural networks generalize to out-of-distribution stimuli in predicting primate visual cortex activity, finding that common benchmarks do not predict neural predictivity performance.

The paper identifies visual feature preferences of neurons in mouse visual cortical areas using generative AI and closed-loop recordings, revealing how these preferences are organized to encode own-stimuli more effectively.

The paper investigates the role of auditory cortex in auditory working memory, finding that sustained neural activity in auditory cortex correlates with memory performance and predicts individual differences in capacity.

Among dozens of ImageNet-trained networks scored against macaque V4/IT recordings and human behavior, DenseNet-169, CORnet-S and ResNet-101 rank highest, and brain similarity stops tracking accuracy above 70% top-1.

The paper evaluates hyperparameter configurations for predicting human brain responses using convolutional neural networks and identifies optimal settings for fMRI and MEG data.

The paper uses deep image synthesis to control primate V4 neural activity, achieving population-level control beyond natural levels.

This paper compares human, monkey, and deep neural network performance in object recognition at the image level, finding that current models fail to predict primate behavior.