Santiago Jaramillo

This paper introduces PhysMAP, a framework that uses multimodal electrophysiological data to identify and study cell types in vivo, showing improved alignment with transcriptomic cell types and circuit dynamics.

This study shows that DOI, a serotonergic psychedelic, increases neural response variability and impairs deviance detection in the auditory cortex of mice by reducing sensitivity to unexpected sounds.

PhysMAP identifies neuronal cell types from electrophysiological data by integrating multiple modalities, outperforming single-modal approaches in mouse brain recordings.

The paper shows that mice's decision-making states during tasks can be predicted by arousal and uninstructed movements.

The paper shows that mice can learn to categorize speech sounds based on formant transitions and voice onset time, with selectivity for these features in specific auditory cortical regions.

The paper shows that passive exposure to task-relevant stimuli improves learning speed in mice and is effectively modeled by neural networks using unsupervised learning during exposure.

The paper compares sound-evoked responses of D1-expressing direct-pathway neurons and their neighbors in the posterior striatum of naive mice, finding similar frequency tuning and amplitude modulation encoding.

This study shows that inhibitory neurons in the auditory cortex are crucial for detecting sounds in noisy environments.

The paper shows that rats adjust their decision biases based on previous outcomes, using a model where reward signals gate the influence of expectations on decisions.

This study investigates the role of posterior striatal neurons in auditory decisions in mice, finding they encode sound features and influence choices without directly controlling movement.