Uygar Sümbül

This paper introduces scDMGC, a method to analyze high-dimensional transcriptomic data using topological data analysis, revealing cell-type organization and transcriptional gradients in brain datasets.

The paper characterizes the 3D organization of large axons in human white matter, revealing distinct architectural patterns across different brain regions.

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 examines stress susceptibility by analyzing layer-specific synaptic input changes in the medial prefrontal cortex of mice under different stress conditions.

MMIDAS jointly infers discrete cell types and continuous type-specific variability in single-cell datasets, improving reproducibility and robustness in cell type identification.

The paper introduces a statistical framework to identify pure cell types in spatial transcriptomic data by analyzing spatial avoidance patterns in the mouse neocortex.

The paper introduces a computational pipeline for automated tracing and assembly of axons across serial sections, validated on centimeter-scale data for orientation analysis and proofreading.

The paper generates and evaluates over 130 million single-neuron models with active conductances to link electrophysiology, morphology, and transcriptomics across cortical cell types.

The paper analyzes dendritic and axonal arbors of inhibitory neurons to identify transcriptomic correlates of neuroanatomy.

This paper proposes a learning rule for recurrent spiking neural networks that incorporates cell-type-specific modulatory signals to improve temporal credit assignment.

The paper develops coupled autoencoders to identify cortical neurons across modalities and finds consistent cell types in transcriptomic and electrophysiological data.

The paper characterizes transcriptomic and physiological properties of GABAergic cortical neurons in mice, identifying 20 interneuron met-types with consistent features across modalities.

The paper analyzes single-cell RNA-seq data from mouse neocortical neurons to identify widespread neuropeptide signaling networks across all cortical neurons.

The paper introduces the Markov link method to predict measurements from different experimental techniques and evaluates it on single-cell RNA sequencing data.

The paper maps excitatory and inhibitory synapses across dendritic trees of cortical neurons and finds local balance in their ratio despite domain-specific density variations.