Daniela Egas Santander

The paper explains neuronal network architecture through spatial continuity and validates it with a stochastic algorithm matching reference data.

The paper investigates how human neuronal morphology influences synaptic connectivity complexity and shows that human neuron shapes lead to more complex connectomes compared to rodents.

The paper creates a detailed computational model of rat somatosensory cortex anatomy, including 4.2 million neurons and 14.2 billion synapses, and identifies limitations in connectivity prediction from morphology.

The paper presents a large-scale biophysically detailed model of somatosensory cortex to simulate and validate cortical dynamics and experimental results.

The paper uses simulations to study how synaptic plasticity interacts with network structure and dendritic organization to shape cortical microcircuit function and learning.

The paper introduces a graph model that captures non-random connectivity in cortical circuits by incorporating physical constraints of axon-soma-dendrite bridging.

The paper shows that heterogeneous and higher-order cortical connectivity allows neural networks to balance efficiency and robustness by supporting subpopulations with different roles.

The paper analyzes inhibition and excitation in a cortical connectome, finding that inhibition is structured through specific connections in large neuron motifs, not just pairwise interactions.

The paper uses a simulation-based approach to study how synaptic connectivity patterns shape functional cell assemblies in a cortical network model, showing that connectivity structure significantly influences assembly composition and activation.