Jan Drugowitsch

This study investigates how distributional reinforcement learning is implemented in the basal ganglia by analyzing dopamine neuron activity and striatal representations.

The paper shows humans make more accurate decisions by compressing sensory evidence into decision categories, and RNNs trained on the same task exhibit similar resilience to noisy computations.

The paper presents the first densely reconstructed adult fruit fly connectome integrating the brain and ventral nerve cord, revealing distributed control circuits involving local and long-range neural connections.

The paper develops a model integrating time and reward to explain foraging decisions and identifies frontal cortex ramping dynamics as a mechanism for solving patch foraging problems.

The paper develops a theory predicting that receptive field heterogeneity increases population coding information and validates it using hippocampal place cell data.

The paper identifies a striatal circuit that encodes reward distributional information, showing D1 and D2 MSNs encode different reward distribution tails.

This study shows nerve injury disrupts temporal firing patterns in spinal cord dorsal horn neurons, driven by PV+ interneurons, contributing to mechanical allodynia.

The paper investigates causal inference during closed-loop navigation by examining how humans disambiguate self- and object-motion, showing misattribution patterns and their dependence on motion conditions and target location.

This paper introduces a Bayesian ring attractor model that incorporates uncertainty into working memory, showing it outperforms conventional models in accuracy.

The paper isolates the neural and cognitive signatures of information seeking by distinguishing it from changes-of-mind, finding increased attention and arousal during information seeking.

This paper shows that most awake hippocampal sharp-wave ripples simulate spatial trajectories with momentum, contrasting with sleep replay which follows Brownian motion.

This paper proposes an online hierarchical Bayesian inference model to explain human motion perception and demonstrates its ability to qualitatively and quantitatively explain various motion stimuli.

The paper shows that interacting with volatile environments leads to more stable hidden-state inference and distinct brain activity patterns compared to observing cues.

The paper develops a decision-making model that explains human fixation behavior by showing how attention modulates information reliability and optimizes cognitive resource allocation.

This study shows human motion perception aligns with Bayesian structural inference in psychophysics experiments, matching performance and error patterns.

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 investigates how humans use hierarchical motion structure in visual perception through controlled stimuli and experiments.

This study shows that learning influences speed-accuracy tradeoffs in decision-making by combining reinforcement learning with sensory evidence accumulation in rats.

The paper models decision-making under limited capacity, showing that optimal strategies shift from breadth to depth as capacity increases.

The paper derives a Bayesian learning rule for diffusion decision models that incorporates confidence and prior biases to improve decision-making under uncertainty.

The paper derives the optimal policy for multi-alternative decisions and shows how it can be implemented by a neural circuit with normalization and urgency signals.