Selmaan N. Chettih

This paper introduces a framework for multi-view animal pose estimation that improves accuracy and uncertainty estimation with limited labeled data, validated across multiple species.

This paper introduces a recurrent network model of the hippocampus that generates barcodes to bind memory elements, reducing interference and enabling flexible retrieval.

The paper identifies unique neural activity patterns in chickadees during caching events, suggesting they represent episodic memories.

The paper shows that mice with different cognitive experiences use distinct cortical areas and neural activity patterns for navigation decisions, highlighting past learning as a key factor in determining cortical necessity for decision-making.

The paper investigates how information about visual stimuli is distributed across large neuronal populations in mouse V1, finding sublinear scaling due to correlated noise.

The paper uses a mouse V1 model to study how perturbations affect coding efficiency and robustness, showing shifts in inhibition and excitation based on stimulus contrast.

The paper shows that visual cortex activity influences perception, but this effect depends on task difficulty and network connectivity patterns.

The paper develops all-optical electrophysiology tools to record subthreshold dynamics and excitability in mouse hippocampus during behavior, revealing brain-state dependent changes in neuronal activity.