C. D. Brody

Flow-field inference from neural data using deep recurrent networks

This paper introduces FINDR, a deep learning method that infers low-dimensional nonlinear dynamics from neural data, outperforming existing methods in capturing neuron responses and visualizing flow fields.

Trial-history biases in evidence accumulation can give rise to apparent lapses

The paper shows that trial-history biases and apparent lapses in decision-making can both result from a single cognitive process involving misbeliefs about non-stationarity.

From predictive models to cognitive models: Separable behavioral processes underlying reward learning in the rat

The paper analyzes a rat behavioral dataset to identify cognitive models underlying reward learning, revealing three distinct behavioral components with different timescales.