Hannah Choi

The paper shows that synaptic rescaling preserves low-dimensional dynamics in low-rank RNNs but fails in high-rank networks during pruning.

The paper develops a mean-field model to study how heterogeneous inhibitory cell types in V1 affect network dynamics and stability through spatial connectivity properties.

This study identifies chamber-specific contributions of non-NaV1.5 sodium channel isoforms to cardiac excitability using a chemical-genetic mouse model and electrophysiological recordings.

The paper introduces a computational model of cortical circuits that explains cell-type-specific responses to novelty through predictive coding, energy efficiency, and reinforcement learning.

This study integrates molecular and morphological data to classify cortical neurons and map their connectomes, revealing organizational principles of brain-wide networks at the single-cell level.

The paper uses voltage imaging to study fast cortical dynamics during a reaching task, identifying distinct oscillatory patterns and network behaviors.

The paper models spike coding in feedforward networks with convergence and divergence, showing structural bottlenecks prefer spike timing over expansion layers and linking network structure to coding strategies.

The paper investigates how lateral inhibition from PV and SST neurons in mouse V1 affects neural and perceptual contrast sensitivity, revealing distinct computational roles for each cell type.

The paper develops RNNs that incorporate biological constraints like Dale's law and structured connectivity to better model cortical function and validate insights through mouse calcium imaging data.

The paper examines how functional connectivity in mouse visual cortex changes with different stimuli, finding that motif frequencies are preserved while neuron identities within motifs shift, and functional modules become more regionally contained with complex stimuli.

This study uses recurrent neural networks to analyze how inter-areal connections affect cortical microcircuit function during learning, finding feedback connections enhance modularization and predictive coding.

The paper uses mathematical models to study how structural and physiological factors affect excitation-inhibition balance in cortical networks and identifies key parameters for stability.

The paper shows that visual areas differentially encode temporal context and expectation in natural scenes, with representational drift observed in lower areas but not higher ones.

The paper presents a dataset and analysis of spiking activity across multiple mouse visual areas, revealing that functional connectivity mirrors anatomical hierarchy.

This study benchmarks scRNA-seq reproducibility across platforms and pipelines, finding that batch correction methods significantly affect results.

The paper expands the Allen Mouse Brain Connectivity Atlas by analyzing intracortical connections at the layer and cell class level, revealing modular organization and distinct projection patterns.

The paper examines how spatial connectivity structures in a mesoscopic whole-brain network influence synchronization patterns in coupled phase oscillators.