Louis Kang

Computational demands shape seizure susceptibility in recurrent neural networks

This study shows that neural network computations, not just anatomy, influence seizure susceptibility in brain regions.

Distinguishing examples while building concepts in hippocampal and artificial networks

The paper models hippocampal CA3 as a Hopfield-like network that stores and retrieves correlated and decorrelated encodings, showing how inhibitory tone controls transitions between concept and example representations, and extends this to multitask learning in neural networks.

Multiple bumps can enhance robustness to noise in continuous attractor networks

This study shows that continuous attractor networks with more bumps are more robust to noise when encoding linear coordinates, but not circular coordinates.

Evaluating state space discovery by persistent cohomology in the spatial representation system

This study evaluates persistent cohomology's ability to detect topological structures in simulated neural data and identifies conditions for successful application.

Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network

The paper shows that a grid cell continuous attractor network naturally produces theta sequences and replay through modulating external input and inhibitory interneuron activity.

A geometric attractor mechanism for self-organization of entorhinal grid modules

The paper proposes a geometric mechanism for self-organization of entorhinal grid modules through inhibition and excitation interactions in the mEC.