Michale S. Fee

This study shows that the songbird brain can adjust individual neurons with millisecond precision using dopamine signals, challenging the idea that reinforcement learning is slow and imprecise.

The paper presents a neural circuit model of reinforcement learning in songbirds that demonstrates how temporal and spatial imprecision in dopaminergic signals can be overcome through fast reward prediction updates.

This paper presents the first vertebrate basal ganglia connectome, revealing complex connectivity patterns that challenge the traditional view of discrete pathways.

The paper introduces a method to track neurons across multiple in-vivo imaging sessions without requiring full field-of-view registration, validated on simulated and experimental datasets.

This study shows that neural sequences in songbirds can form without tutor exposure but become tightly associated with new syllables after tutoring, with some birds failing to learn due to pre-existing crystallized sequences.

The paper introduces a new optical design for head-mounted microscopes that reduces weight and increases field-of-view for imaging neurons in mice and songbirds.

The paper shows how the basal ganglia solve the credit assignment problem through synaptic architecture and biophysical modeling in songbirds.

The paper introduces seqNMF, a software toolbox for extracting and analyzing temporal sequences in high-dimensional neural data, showing its effectiveness on simulated and real neural datasets.