Arthur Leblois

The paper evaluates the generation mechanisms of abnormal beta oscillations in the basal ganglia across species using a network model with species-specific parameters.

This paper presents a computational model of songbird vocal learning that uses a dual pathway architecture to achieve robust sensorimotor learning and outperforms standard reinforcement learning approaches.

This paper introduces Neural Latching Switch Circuits (NLSC) as a framework for implementing finite-state automata in neural networks, showing their applicability to various cognitive processes.

This paper presents a theoretical model of the BG-thalamo-cortical network to explain how reward-driven movement adaptation occurs through closed-loop dynamics and dopamine-dependent plasticity.

This study identifies cerebellar lobule IV neurons in zebra finches that encode syllable boundaries during singing, suggesting a role in controlling vocal element duration.

The paper investigates neural activity patterns in the songbird anterior forebrain pathway during sleep and awake states, finding synchronized bursts in LMAN during NREM sleep similar to hippocampal sharp-wave ripples.

This paper models abnormal beta oscillations in Parkinson's disease by simulating basal ganglia circuits constrained by experimental data, showing that pallidostriatal loops drive mid-beta oscillations.

The paper proposes a dual pathway model for vocal learning in songbirds, showing it can reach global optima in complex landscapes through exploration and consolidation.

This study identifies a subcortical circuit linking the cerebellum to the basal ganglia in zebra finches that is critical for vocal learning.

This study demonstrates that a model of recurrent neural networks can generate irregular motor behaviors and matches behavioral variability statistics in humans and songbirds.