Charles Findling

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

This paper introduces modular neural state-space models to implement the Bayesian Brain hypothesis, enabling learned and reusable world-model components for flexible cognition.

This study investigates how dopamine affects reinforcement learning in humans using L-DOPA and finds it reduces switching after low rewards and influences learning rate and precision.

This paper improves neural decoding by exploiting correlations across trials and sessions, showing better accuracy for four behaviors in mice using interpretable models.

This study examines how Nrxn1 loss-of-function affects prior utilization and neural dynamics in a mouse model of autism, revealing impaired decision-making and altered cortical activity.

Mice infer stimulus-side priors from recent choices; prior signals are decodable pre-stimulus in 24.0% to 30.2% of 242 brain regions, including early visual and motor areas.

The paper presents a brain-wide neural activity map from 621,733 neurons across 139 mice performing a decision-making task, showing how neural activity encodes task variables across brain regions.

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

This paper shows that adding computation noise to recurrent neural networks improves their resilience to adverse conditions during decision-making, mimicking human cognitive adaptability.

The paper shows that computational noise in reward-guided learning causes behavioral variability in volatile environments rather than reflecting exploration.

The paper shows that imprecise neural computations following the Weber Law enable near-optimal adaptive behavior in volatile environments, outperforming higher-order models and reinforcement learning.

The paper introduces a model of causal inference that explains how people perceive their causal power through counterfactual reasoning, showing it better explains behavior than alternative models.