Andrew Saxe

This study shows that dopamine signals in the striatum reflect long-term learning strategies in mice, with a deep neural network model capturing these dynamics through reinforcement learning.

This study uses automated search algorithms to discover diverse synaptic plasticity rules that support memory tasks and network stability.

NeuralPlayground provides a standardized framework to compare computational models of hippocampus and entorhinal cortex with experimental data on spatial cognition.

This paper introduces a neural network model of systems consolidation that optimizes memory transfer for generalization, showing how selective consolidation supports both memory and adaptive behavior.

This study shows mice use action-driven exploration to learn subgoal locations and integrate them into spatial memory.

The paper compares 'lazy' and 'rich' coding strategies in neural networks and biological brains for multitasking, finding evidence for rich coding in human and macaque neural data.

The paper shows that human brains and neural networks can rapidly reorganize knowledge when new information is introduced, using BOLD signals and a gradient descent model.

This study shows rats initially prioritize learning over immediate reward, leading to faster overall reward accumulation during perceptual decision tasks.