Timothy Lillicrap

This study shows how rapid learning in mice involves structured changes in motor cortex connectivity, rerouting preparatory activity to control a conditioned neuron.

The paper uses a computer tournament to show that refining knowledge can be advantageous initially but leads to conformity and blind copying in complex cultures.

The paper shows that cortical pyramidal neurons exhibit distinct unexpected event signals in somata and distal apical dendrites that change over days, suggesting a role in hierarchical computation.

This study shows that two BCI learning phenomena can be explained by gradient-based optimization, without requiring special mechanisms.

The paper shows that rotational dynamics in motor cortex can arise from sensory feedback, even without recurrent connections, and are also present in somatosensory cortex and feedback signals.

Training a two-pathway deep neural network with a single self-supervised predictive loss function models both dorsal and ventral pathways of mouse visual cortex and outperforms competing models.

This paper uses meta-learning to discover synaptic plasticity rules that can produce desired neural functions, re-discovering known rules and finding new ones for complex tasks.