Aran Nayebi

This study uses intrinsic motivation algorithms to create agents that mimic Drosophila exploration behavior and neural representations of future actions.

This study examines how data and model size affect the accuracy of ANN models predicting neural activity in C. elegans, finding log-linear data scaling and nonlinear model scaling.

The paper develops a three-layer network model to predict retinal responses to natural scenes, revealing fast dynamics in ganglion cell signaling and the role of interneurons in generating the retinal code.

The paper reassesses claims about grid cell emergence in path-integrator circuits, finding consistency with prior theory.

This study develops ConvRNN models to simulate primate ventral visual stream processing, finding that recurrent networks achieve better task performance and efficiency than deeper feedforward networks.

The paper uses in vivo imaging to compare synaptic dynamics between excitatory synapses on pyramidal neurons and inhibitory interneurons, finding that PV+ synapses are more stable and less heterogeneous.

This study uses task-driven neural networks to show that heterogeneous MEC cells are functionally significant and that reward-modulated models better capture MEC response profiles than traditional models.

This paper develops a computational model of mouse visual cortex, finding that self-supervised contrastive learning produces better matches to mouse cortex than supervised methods, suggesting a resource-efficient, general-purpose visual system.

This paper shows that unsupervised neural networks can model primate ventral visual stream responses as well as supervised methods, with brain-like representations from limited data.

CORnet-S, a four-area recurrent network with areas pre-mapped to V1, V2, V4 and inferior temporal cortex, ranks among top Brain-Score models while having a shorter longest convolutional path than competitors.