Victor Geadah

This study infers reinforcement learning rules from single-animal behavior during de novo task learning in mice, finding that policy-gradient-like rules outperform classical temporal-difference algorithms.

This paper uses artificial neural networks to study single-neuron adaptation mechanisms and finds they improve robustness and recover biological coding strategies like gain scaling.

This paper introduces dDIRL to model complex animal behavior over time, identifying internal states and reward functions from observed navigation in water-starved mice.

This paper introduces a sparse-coding variational auto-encoder (SVAE) that improves upon sparse coding by using a probabilistic model with a deep neural network, achieving better performance on natural image data.