Jason Z. Kim

This paper introduces dSPMs to identify brain algorithms for scene prediction in macaques, finding a 'manifold-based simulation' algorithm in the DMFC.

This study shows that bioRNNs, which incorporate brain geometry, better predict fMRI activity and replicate brain hierarchy than other RNNs.

The paper uses a geometry-aware autoencoder to analyze hippocampal activity in mice, revealing a small-world network structure that enables both local fidelity and global searchability in cognitive maps.

The paper develops a framework using optimal control theory to estimate brain structure-function interactions from MRI data, showing correlations between self-inhibition parameters and neural timescales across species.

The paper shows that brain areas differ in how they format information rather than just containing it, affecting decision-making through distinct neural coding strategies.

This paper introduces a framework for applying network control theory to the human structural connectome to study neural dynamics and control energy, controllability, and their correlations with behavior and mental health.

The paper uses network control theory to show that bottom-up signaling across the sensory-fugal axis of the human connectome requires less energy than top-down signaling.

The paper uses network theory to predict compression levels in neural circuits and finds differences in dimensionality and representational capacity across brain regions and behaviors.

The paper shows that external inputs affect BOLD signal non-stationarity and presents a method to estimate these inputs using in silico models and fMRI data.

This study uses network control theory to analyze energy dynamics in temporal lobe epilepsy, finding increased control energy requirements and metabolic asymmetry in affected brain regions.

The paper uses network control theory to show that structural brain networks mature during adolescence to reduce the energy cost of activating the frontoparietal system, supporting executive function development.

This paper introduces a dynamical systems framework to estimate both intrinsic brain activity and external stimuli, showing task-related changes and non-stationarities in cortical dynamics.

This study shows how network topology influences neural dynamics, enabling long-lasting stimulus propagation and recovery through specific network motifs and control theory principles.

The paper uses machine learning to analyze brain states from fMRI data and shows that white matter connectivity influences their dynamics and development.