Brian DePasquale

BiXformer disentangles bidirectional neural communication by modeling causal and acausal streams, revealing temporally structured components in neural dynamics.

This paper introduces a drift diffusion model with hidden Markov states to analyze time-dependent decision-making in rats, revealing daily fluctuations in performance linked to circadian rhythms.

The paper shows that motor cortical involvement in movement changes rapidly and depends on task structure and sensorimotor uncertainty.

The paper shows that fine-tuning chemical foundation models improves odorant-receptor binding prediction, introducing LORAX which outperforms existing models.

The paper analyzes CO2-sensing circuits in Aedes aegypti mosquitoes, finding high recurrent connectivity among CO2-sensitive neurons and proposing it enhances CO2 detection.

The paper introduces a state-dependent drift diffusion model to show mice switch between impulsive and deliberative decision states during tasks, revealing active exploration of the speed-accuracy trade-off.

The paper develops a framework to model neural and behavioral data together, revealing distinct evidence accumulation dynamics in three rat brain regions.

The paper shows that striatal dopamine and acetylcholine encode trajectory errors during navigation, with distinct spatial and temporal patterns, and links these signals to learning and behavior.

The paper identifies transitions in neural dynamics and modes during perceptual decision-making in rats, linking these transitions to decision commitment.

The paper shows that diverse neural response profiles in auditory cortex arise from synaptic plasticity and contribute differently to task performance.

The paper trains a recurrent neural network to model decision-making in monkeys, revealing grid-like fixed points in network dynamics for context-dependent tasks.

This study shows error-correcting attractor dynamics reduce noise effects in working memory by pulling memories toward stable representations in both humans and monkeys.

The paper identifies task-relevant information in nominally non-responsive neurons using a spike-timing-based algorithm in behaving rats.