N. Alex Cayco-Gajic

This study investigates how cerebellar activity encodes paw movements during a complex locomotor task, finding that cerebellar circuits are tightly aligned with step cycle events.

The paper proposes a distributed learning model that explains how fast and slow neural systems coordinate during motor adaptation.

This paper introduces sliceTCA, a new unsupervised dimensionality reduction method for neural data tensors, showing it captures more task-relevant structure with fewer components than traditional methods.

The paper investigates how neural representations transform from dLGN to V1 in mice, finding evidence for both sparse coding and linear separability.

Open Source Brain provides a platform for visualizing, simulating, and collaboratively developing standardized neuronal and circuit models to improve model transparency and reproducibility.

This paper introduces a maximum entropy model that captures higher-order neural interactions by adaptively identifying estimable moments, improving prediction of spiking pattern frequencies.

This study shows that sparse synaptic connectivity in feedforward networks optimizes pattern separation by balancing coding space expansion and decorrelation.