Stephen G. Lisberger

This paper develops a deep-learning classifier to identify cell types from extracellular recordings across species with over 95% accuracy.

The paper identifies input-output transformations in the cerebellar circuit and shows how interneurons and plasticity enable flexible motor control.

The paper develops deep-learning methods to identify neuron types from extracellular recordings in non-human primates' cerebellum during behavior.

The paper develops a sensory-motor decoder that transforms neural responses in area MT into smooth pursuit eye movements, revealing how reduced motion reliability affects pursuit initiation and steady-state tracking.

The paper investigates whether cerebellar Purkinje cells use firing rate or spike synchrony to transmit signals to downstream neurons, finding that rate codes dominate.

The paper investigates neural mechanisms of sensory decoding for motor control, finding that traditional models fail to explain behavioral variance and proposing a circuit model with flexible transmission adjustment.

The paper reveals how the dorsolateral pontine nucleus and nucleus reticularis tegmenti pontis decode cortical output for motor control during smooth pursuit eye movements.

This paper evaluates existing spike sorters and introduces a new method called full binary pursuit (FBP) that addresses multiple sorting challenges in neural recordings.

The paper identifies a mechanism in the frontal eye fields that enables preparatory activity without triggering movement by using output as a gain signal and showing partial reorganization of activity.

The paper investigates how reduced dot coherence affects smooth pursuit eye movements, showing distinct impacts on initiation and steady-state tracking, suggesting separate mechanisms.

The paper investigates neural mechanisms underlying reaction times in smooth pursuit eye movements, finding trial-by-trial correlations between neural activity and behavioral latency.