David J. Herzfeld

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

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

This study shows that residual errors in motor adaptation are due to implicit learning's error sensitivity, which depends on past error history, not forgetting.

The paper describes behavioral training and electrophysiological recording protocols in marmosets to increase trial numbers and target cerebellar Purkinje cells.

The paper tests if reward prediction error (RPE) modulates saccade vigor by inducing RPE events before saccade onset and measuring their effect on saccade velocity and reaction time.

The paper examines how a new editorial policy at Journal of Neuroscience affected rejection rates of human behavioral papers, finding a 30% decline in their proportion since 2015.